Simple, Defensible Sample Sizes Based on Cost Efficiency

PubMed Central

Bacchetti, Peter; McCulloch, Charles E.; Segal, Mark R.

2009-01-01

Summary The conventional approach of choosing sample size to provide 80% or greater power ignores the cost implications of different sample size choices. Costs, however, are often impossible for investigators and funders to ignore in actual practice. Here, we propose and justify a new approach for choosing sample size based on cost efficiency, the ratio of a study’s projected scientific and/or practical value to its total cost. By showing that a study’s projected value exhibits diminishing marginal returns as a function of increasing sample size for a wide variety of definitions of study value, we are able to develop two simple choices that can be defended as more cost efficient than any larger sample size. The first is to choose the sample size that minimizes the average cost per subject. The second is to choose sample size to minimize total cost divided by the square root of sample size. This latter method is theoretically more justifiable for innovative studies, but also performs reasonably well and has some justification in other cases. For example, if projected study value is assumed to be proportional to power at a specific alternative and total cost is a linear function of sample size, then this approach is guaranteed either to produce more than 90% power or to be more cost efficient than any sample size that does. These methods are easy to implement, based on reliable inputs, and well justified, so they should be regarded as acceptable alternatives to current conventional approaches. PMID:18482055

Statistical power analysis in wildlife research

USGS Publications Warehouse

Steidl, R.J.; Hayes, J.P.

1997-01-01

Statistical power analysis can be used to increase the efficiency of research efforts and to clarify research results. Power analysis is most valuable in the design or planning phases of research efforts. Such prospective (a priori) power analyses can be used to guide research design and to estimate the number of samples necessary to achieve a high probability of detecting biologically significant effects. Retrospective (a posteriori) power analysis has been advocated as a method to increase information about hypothesis tests that were not rejected. However, estimating power for tests of null hypotheses that were not rejected with the effect size observed in the study is incorrect; these power estimates will always be a??0.50 when bias adjusted and have no relation to true power. Therefore, retrospective power estimates based on the observed effect size for hypothesis tests that were not rejected are misleading; retrospective power estimates are only meaningful when based on effect sizes other than the observed effect size, such as those effect sizes hypothesized to be biologically significant. Retrospective power analysis can be used effectively to estimate the number of samples or effect size that would have been necessary for a completed study to have rejected a specific null hypothesis. Simply presenting confidence intervals can provide additional information about null hypotheses that were not rejected, including information about the size of the true effect and whether or not there is adequate evidence to 'accept' a null hypothesis as true. We suggest that (1) statistical power analyses be routinely incorporated into research planning efforts to increase their efficiency, (2) confidence intervals be used in lieu of retrospective power analyses for null hypotheses that were not rejected to assess the likely size of the true effect, (3) minimum biologically significant effect sizes be used for all power analyses, and (4) if retrospective power estimates are to be reported, then the I?-level, effect sizes, and sample sizes used in calculations must also be reported.

[An investigation of the statistical power of the effect size in randomized controlled trials for the treatment of patients with type 2 diabetes mellitus using Chinese medicine].

PubMed

Ma, Li-Xin; Liu, Jian-Ping

2012-01-01

To investigate whether the power of the effect size was based on adequate sample size in randomized controlled trials (RCTs) for the treatment of patients with type 2 diabetes mellitus (T2DM) using Chinese medicine. China Knowledge Resource Integrated Database (CNKI), VIP Database for Chinese Technical Periodicals (VIP), Chinese Biomedical Database (CBM), and Wangfang Data were systematically recruited using terms like "Xiaoke" or diabetes, Chinese herbal medicine, patent medicine, traditional Chinese medicine, randomized, controlled, blinded, and placebo-controlled. Limitation was set on the intervention course > or = 3 months in order to identify the information of outcome assessement and the sample size. Data collection forms were made according to the checking lists found in the CONSORT statement. Independent double data extractions were performed on all included trials. The statistical power of the effects size for each RCT study was assessed using sample size calculation equations. (1) A total of 207 RCTs were included, including 111 superiority trials and 96 non-inferiority trials. (2) Among the 111 superiority trials, fasting plasma glucose (FPG) and glycosylated hemoglobin HbA1c (HbA1c) outcome measure were reported in 9% and 12% of the RCTs respectively with the sample size > 150 in each trial. For the outcome of HbA1c, only 10% of the RCTs had more than 80% power. For FPG, 23% of the RCTs had more than 80% power. (3) In the 96 non-inferiority trials, the outcomes FPG and HbA1c were reported as 31% and 36% respectively. These RCTs had a samples size > 150. For HbA1c only 36% of the RCTs had more than 80% power. For FPG, only 27% of the studies had more than 80% power. The sample size for statistical analysis was distressingly low and most RCTs did not achieve 80% power. In order to obtain a sufficient statistic power, it is recommended that clinical trials should establish clear research objective and hypothesis first, and choose scientific and evidence-based study design and outcome measurements. At the same time, calculate required sample size to ensure a precise research conclusion.

Threshold-dependent sample sizes for selenium assessment with stream fish tissue

USGS Publications Warehouse

Hitt, Nathaniel P.; Smith, David R.

2015-01-01

Natural resource managers are developing assessments of selenium (Se) contamination in freshwater ecosystems based on fish tissue concentrations. We evaluated the effects of sample size (i.e., number of fish per site) on the probability of correctly detecting mean whole-body Se values above a range of potential management thresholds. We modeled Se concentrations as gamma distributions with shape and scale parameters fitting an empirical mean-to-variance relationship in data from southwestern West Virginia, USA (63 collections, 382 individuals). We used parametric bootstrapping techniques to calculate statistical power as the probability of detecting true mean concentrations up to 3 mg Se/kg above management thresholds ranging from 4 to 8 mg Se/kg. Sample sizes required to achieve 80% power varied as a function of management thresholds and Type I error tolerance (α). Higher thresholds required more samples than lower thresholds because populations were more heterogeneous at higher mean Se levels. For instance, to assess a management threshold of 4 mg Se/kg, a sample of eight fish could detect an increase of approximately 1 mg Se/kg with 80% power (given α = 0.05), but this sample size would be unable to detect such an increase from a management threshold of 8 mg Se/kg with more than a coin-flip probability. Increasing α decreased sample size requirements to detect above-threshold mean Se concentrations with 80% power. For instance, at an α-level of 0.05, an 8-fish sample could detect an increase of approximately 2 units above a threshold of 8 mg Se/kg with 80% power, but when α was relaxed to 0.2, this sample size was more sensitive to increasing mean Se concentrations, allowing detection of an increase of approximately 1.2 units with equivalent power. Combining individuals into 2- and 4-fish composite samples for laboratory analysis did not decrease power because the reduced number of laboratory samples was compensated for by increased precision of composites for estimating mean conditions. However, low sample sizes (<5 fish) did not achieve 80% power to detect near-threshold values (i.e., <1 mg Se/kg) under any scenario we evaluated. This analysis can assist the sampling design and interpretation of Se assessments from fish tissue by accounting for natural variation in stream fish populations.

Tests of Independence in Contingency Tables with Small Samples: A Comparison of Statistical Power.

ERIC Educational Resources Information Center

Parshall, Cynthia G.; Kromrey, Jeffrey D.

1996-01-01

Power and Type I error rates were estimated for contingency tables with small sample sizes for the following four types of tests: (1) Pearson's chi-square; (2) chi-square with Yates's continuity correction; (3) the likelihood ratio test; and (4) Fisher's Exact Test. Various marginal distributions, sample sizes, and effect sizes were examined. (SLD)

Comparison of Sample Size by Bootstrap and by Formulas Based on Normal Distribution Assumption.

PubMed

Wang, Zuozhen

2018-01-01

Bootstrapping technique is distribution-independent, which provides an indirect way to estimate the sample size for a clinical trial based on a relatively smaller sample. In this paper, sample size estimation to compare two parallel-design arms for continuous data by bootstrap procedure are presented for various test types (inequality, non-inferiority, superiority, and equivalence), respectively. Meanwhile, sample size calculation by mathematical formulas (normal distribution assumption) for the identical data are also carried out. Consequently, power difference between the two calculation methods is acceptably small for all the test types. It shows that the bootstrap procedure is a credible technique for sample size estimation. After that, we compared the powers determined using the two methods based on data that violate the normal distribution assumption. To accommodate the feature of the data, the nonparametric statistical method of Wilcoxon test was applied to compare the two groups in the data during the process of bootstrap power estimation. As a result, the power estimated by normal distribution-based formula is far larger than that by bootstrap for each specific sample size per group. Hence, for this type of data, it is preferable that the bootstrap method be applied for sample size calculation at the beginning, and that the same statistical method as used in the subsequent statistical analysis is employed for each bootstrap sample during the course of bootstrap sample size estimation, provided there is historical true data available that can be well representative of the population to which the proposed trial is planning to extrapolate.

Estimation of sample size and testing power (Part 4).

PubMed

Hu, Liang-ping; Bao, Xiao-lei; Guan, Xue; Zhou, Shi-guo

2012-01-01

Sample size estimation is necessary for any experimental or survey research. An appropriate estimation of sample size based on known information and statistical knowledge is of great significance. This article introduces methods of sample size estimation of difference test for data with the design of one factor with two levels, including sample size estimation formulas and realization based on the formulas and the POWER procedure of SAS software for quantitative data and qualitative data with the design of one factor with two levels. In addition, this article presents examples for analysis, which will play a leading role for researchers to implement the repetition principle during the research design phase.

A Bayesian sequential design using alpha spending function to control type I error.

PubMed

Zhu, Han; Yu, Qingzhao

2017-10-01

We propose in this article a Bayesian sequential design using alpha spending functions to control the overall type I error in phase III clinical trials. We provide algorithms to calculate critical values, power, and sample sizes for the proposed design. Sensitivity analysis is implemented to check the effects from different prior distributions, and conservative priors are recommended. We compare the power and actual sample sizes of the proposed Bayesian sequential design with different alpha spending functions through simulations. We also compare the power of the proposed method with frequentist sequential design using the same alpha spending function. Simulations show that, at the same sample size, the proposed method provides larger power than the corresponding frequentist sequential design. It also has larger power than traditional Bayesian sequential design which sets equal critical values for all interim analyses. When compared with other alpha spending functions, O'Brien-Fleming alpha spending function has the largest power and is the most conservative in terms that at the same sample size, the null hypothesis is the least likely to be rejected at early stage of clinical trials. And finally, we show that adding a step of stop for futility in the Bayesian sequential design can reduce the overall type I error and reduce the actual sample sizes.

Approximate sample size formulas for the two-sample trimmed mean test with unequal variances.

PubMed

Luh, Wei-Ming; Guo, Jiin-Huarng

2007-05-01

Yuen's two-sample trimmed mean test statistic is one of the most robust methods to apply when variances are heterogeneous. The present study develops formulas for the sample size required for the test. The formulas are applicable for the cases of unequal variances, non-normality and unequal sample sizes. Given the specified alpha and the power (1-beta), the minimum sample size needed by the proposed formulas under various conditions is less than is given by the conventional formulas. Moreover, given a specified size of sample calculated by the proposed formulas, simulation results show that Yuen's test can achieve statistical power which is generally superior to that of the approximate t test. A numerical example is provided.

Determining Sample Size with a Given Range of Mean Effects in One-Way Heteroscedastic Analysis of Variance

ERIC Educational Resources Information Center

Shieh, Gwowen; Jan, Show-Li

2013-01-01

The authors examined 2 approaches for determining the required sample size of Welch's test for detecting equality of means when the greatest difference between any 2 group means is given. It is shown that the actual power obtained with the sample size of the suggested approach is consistently at least as great as the nominal power. However, the…

Power calculation for overall hypothesis testing with high-dimensional commensurate outcomes.

PubMed

Chi, Yueh-Yun; Gribbin, Matthew J; Johnson, Jacqueline L; Muller, Keith E

2014-02-28

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high-dimensional and commensurate outcomes are required. While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high-dimensional pathway analysis. With an account of any complex correlation structure between high-dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite-sample) and approximate non-null distributions of the 'univariate' approach to repeated measures test statistic, as well as power-equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects. Copyright © 2013 John Wiley & Sons, Ltd.

"PowerUp"!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies

ERIC Educational Resources Information Center

Dong, Nianbo; Maynard, Rebecca

2013-01-01

This paper and the accompanying tool are intended to complement existing supports for conducting power analysis tools by offering a tool based on the framework of Minimum Detectable Effect Sizes (MDES) formulae that can be used in determining sample size requirements and in estimating minimum detectable effect sizes for a range of individual- and…

Sample Size in Clinical Cardioprotection Trials Using Myocardial Salvage Index, Infarct Size, or Biochemical Markers as Endpoint.

PubMed

Engblom, Henrik; Heiberg, Einar; Erlinge, David; Jensen, Svend Eggert; Nordrehaug, Jan Erik; Dubois-Randé, Jean-Luc; Halvorsen, Sigrun; Hoffmann, Pavel; Koul, Sasha; Carlsson, Marcus; Atar, Dan; Arheden, Håkan

2016-03-09

Cardiac magnetic resonance (CMR) can quantify myocardial infarct (MI) size and myocardium at risk (MaR), enabling assessment of myocardial salvage index (MSI). We assessed how MSI impacts the number of patients needed to reach statistical power in relation to MI size alone and levels of biochemical markers in clinical cardioprotection trials and how scan day affect sample size. Controls (n=90) from the recent CHILL-MI and MITOCARE trials were included. MI size, MaR, and MSI were assessed from CMR. High-sensitivity troponin T (hsTnT) and creatine kinase isoenzyme MB (CKMB) levels were assessed in CHILL-MI patients (n=50). Utilizing distribution of these variables, 100 000 clinical trials were simulated for calculation of sample size required to reach sufficient power. For a treatment effect of 25% decrease in outcome variables, 50 patients were required in each arm using MSI compared to 93, 98, 120, 141, and 143 for MI size alone, hsTnT (area under the curve [AUC] and peak), and CKMB (AUC and peak) in order to reach a power of 90%. If average CMR scan day between treatment and control arms differed by 1 day, sample size needs to be increased by 54% (77 vs 50) to avoid scan day bias masking a treatment effect of 25%. Sample size in cardioprotection trials can be reduced 46% to 65% without compromising statistical power when using MSI by CMR as an outcome variable instead of MI size alone or biochemical markers. It is essential to ensure lack of bias in scan day between treatment and control arms to avoid compromising statistical power. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Power and Precision in Confirmatory Factor Analytic Tests of Measurement Invariance

ERIC Educational Resources Information Center

Meade, Adam W.; Bauer, Daniel J.

2007-01-01

This study investigates the effects of sample size, factor overdetermination, and communality on the precision of factor loading estimates and the power of the likelihood ratio test of factorial invariance in multigroup confirmatory factor analysis. Although sample sizes are typically thought to be the primary determinant of precision and power,…

Sample size calculation in cost-effectiveness cluster randomized trials: optimal and maximin approaches.

PubMed

Manju, Md Abu; Candel, Math J J M; Berger, Martijn P F

2014-07-10

In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost-effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra-cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost-effectiveness of an intervention.

Using Structural Equation Modeling to Assess Functional Connectivity in the Brain: Power and Sample Size Considerations

ERIC Educational Resources Information Center

Sideridis, Georgios; Simos, Panagiotis; Papanicolaou, Andrew; Fletcher, Jack

2014-01-01

The present study assessed the impact of sample size on the power and fit of structural equation modeling applied to functional brain connectivity hypotheses. The data consisted of time-constrained minimum norm estimates of regional brain activity during performance of a reading task obtained with magnetoencephalography. Power analysis was first…

Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate.

PubMed

Sepúlveda, Nuno; Paulino, Carlos Daniel; Drakeley, Chris

2015-12-30

Several studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted. A sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point. Overall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias. The proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.

The N-Pact Factor: Evaluating the Quality of Empirical Journals with Respect to Sample Size and Statistical Power

PubMed Central

Fraley, R. Chris; Vazire, Simine

2014-01-01

The authors evaluate the quality of research reported in major journals in social-personality psychology by ranking those journals with respect to their N-pact Factors (NF)—the statistical power of the empirical studies they publish to detect typical effect sizes. Power is a particularly important attribute for evaluating research quality because, relative to studies that have low power, studies that have high power are more likely to (a) to provide accurate estimates of effects, (b) to produce literatures with low false positive rates, and (c) to lead to replicable findings. The authors show that the average sample size in social-personality research is 104 and that the power to detect the typical effect size in the field is approximately 50%. Moreover, they show that there is considerable variation among journals in sample sizes and power of the studies they publish, with some journals consistently publishing higher power studies than others. The authors hope that these rankings will be of use to authors who are choosing where to submit their best work, provide hiring and promotion committees with a superior way of quantifying journal quality, and encourage competition among journals to improve their NF rankings. PMID:25296159

Optimal flexible sample size design with robust power.

PubMed

Zhang, Lanju; Cui, Lu; Yang, Bo

2016-08-30

It is well recognized that sample size determination is challenging because of the uncertainty on the treatment effect size. Several remedies are available in the literature. Group sequential designs start with a sample size based on a conservative (smaller) effect size and allow early stop at interim looks. Sample size re-estimation designs start with a sample size based on an optimistic (larger) effect size and allow sample size increase if the observed effect size is smaller than planned. Different opinions favoring one type over the other exist. We propose an optimal approach using an appropriate optimality criterion to select the best design among all the candidate designs. Our results show that (1) for the same type of designs, for example, group sequential designs, there is room for significant improvement through our optimization approach; (2) optimal promising zone designs appear to have no advantages over optimal group sequential designs; and (3) optimal designs with sample size re-estimation deliver the best adaptive performance. We conclude that to deal with the challenge of sample size determination due to effect size uncertainty, an optimal approach can help to select the best design that provides most robust power across the effect size range of interest. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Researchers’ Intuitions About Power in Psychological Research

PubMed Central

Bakker, Marjan; Hartgerink, Chris H. J.; Wicherts, Jelte M.; van der Maas, Han L. J.

2016-01-01

Many psychology studies are statistically underpowered. In part, this may be because many researchers rely on intuition, rules of thumb, and prior practice (along with practical considerations) to determine the number of subjects to test. In Study 1, we surveyed 291 published research psychologists and found large discrepancies between their reports of their preferred amount of power and the actual power of their studies (calculated from their reported typical cell size, typical effect size, and acceptable alpha). Furthermore, in Study 2, 89% of the 214 respondents overestimated the power of specific research designs with a small expected effect size, and 95% underestimated the sample size needed to obtain .80 power for detecting a small effect. Neither researchers’ experience nor their knowledge predicted the bias in their self-reported power intuitions. Because many respondents reported that they based their sample sizes on rules of thumb or common practice in the field, we recommend that researchers conduct and report formal power analyses for their studies. PMID:27354203

Researchers' Intuitions About Power in Psychological Research.

PubMed

Bakker, Marjan; Hartgerink, Chris H J; Wicherts, Jelte M; van der Maas, Han L J

2016-08-01

Many psychology studies are statistically underpowered. In part, this may be because many researchers rely on intuition, rules of thumb, and prior practice (along with practical considerations) to determine the number of subjects to test. In Study 1, we surveyed 291 published research psychologists and found large discrepancies between their reports of their preferred amount of power and the actual power of their studies (calculated from their reported typical cell size, typical effect size, and acceptable alpha). Furthermore, in Study 2, 89% of the 214 respondents overestimated the power of specific research designs with a small expected effect size, and 95% underestimated the sample size needed to obtain .80 power for detecting a small effect. Neither researchers' experience nor their knowledge predicted the bias in their self-reported power intuitions. Because many respondents reported that they based their sample sizes on rules of thumb or common practice in the field, we recommend that researchers conduct and report formal power analyses for their studies. © The Author(s) 2016.

Got Power? A Systematic Review of Sample Size Adequacy in Health Professions Education Research

ERIC Educational Resources Information Center

Cook, David A.; Hatala, Rose

2015-01-01

Many education research studies employ small samples, which in turn lowers statistical power. We re-analyzed the results of a meta-analysis of simulation-based education to determine study power across a range of effect sizes, and the smallest effect that could be plausibly excluded. We systematically searched multiple databases through May 2011,…

Using variance components to estimate power in a hierarchically nested sampling design improving monitoring of larval Devils Hole pupfish

USGS Publications Warehouse

Dzul, Maria C.; Dixon, Philip M.; Quist, Michael C.; Dinsomore, Stephen J.; Bower, Michael R.; Wilson, Kevin P.; Gaines, D. Bailey

2013-01-01

We used variance components to assess allocation of sampling effort in a hierarchically nested sampling design for ongoing monitoring of early life history stages of the federally endangered Devils Hole pupfish (DHP) (Cyprinodon diabolis). Sampling design for larval DHP included surveys (5 days each spring 2007–2009), events, and plots. Each survey was comprised of three counting events, where DHP larvae on nine plots were counted plot by plot. Statistical analysis of larval abundance included three components: (1) evaluation of power from various sample size combinations, (2) comparison of power in fixed and random plot designs, and (3) assessment of yearly differences in the power of the survey. Results indicated that increasing the sample size at the lowest level of sampling represented the most realistic option to increase the survey's power, fixed plot designs had greater power than random plot designs, and the power of the larval survey varied by year. This study provides an example of how monitoring efforts may benefit from coupling variance components estimation with power analysis to assess sampling design.

Addressing the "Replication Crisis": Using Original Studies to Design Replication Studies with Appropriate Statistical Power.

PubMed

Anderson, Samantha F; Maxwell, Scott E

2017-01-01

Psychology is undergoing a replication crisis. The discussion surrounding this crisis has centered on mistrust of previous findings. Researchers planning replication studies often use the original study sample effect size as the basis for sample size planning. However, this strategy ignores uncertainty and publication bias in estimated effect sizes, resulting in overly optimistic calculations. A psychologist who intends to obtain power of .80 in the replication study, and performs calculations accordingly, may have an actual power lower than .80. We performed simulations to reveal the magnitude of the difference between actual and intended power based on common sample size planning strategies and assessed the performance of methods that aim to correct for effect size uncertainty and/or bias. Our results imply that even if original studies reflect actual phenomena and were conducted in the absence of questionable research practices, popular approaches to designing replication studies may result in a low success rate, especially if the original study is underpowered. Methods correcting for bias and/or uncertainty generally had higher actual power, but were not a panacea for an underpowered original study. Thus, it becomes imperative that 1) original studies are adequately powered and 2) replication studies are designed with methods that are more likely to yield the intended level of power.

Sample size and power considerations in network meta-analysis

PubMed Central

2012-01-01

Background Network meta-analysis is becoming increasingly popular for establishing comparative effectiveness among multiple interventions for the same disease. Network meta-analysis inherits all methodological challenges of standard pairwise meta-analysis, but with increased complexity due to the multitude of intervention comparisons. One issue that is now widely recognized in pairwise meta-analysis is the issue of sample size and statistical power. This issue, however, has so far only received little attention in network meta-analysis. To date, no approaches have been proposed for evaluating the adequacy of the sample size, and thus power, in a treatment network. Findings In this article, we develop easy-to-use flexible methods for estimating the ‘effective sample size’ in indirect comparison meta-analysis and network meta-analysis. The effective sample size for a particular treatment comparison can be interpreted as the number of patients in a pairwise meta-analysis that would provide the same degree and strength of evidence as that which is provided in the indirect comparison or network meta-analysis. We further develop methods for retrospectively estimating the statistical power for each comparison in a network meta-analysis. We illustrate the performance of the proposed methods for estimating effective sample size and statistical power using data from a network meta-analysis on interventions for smoking cessation including over 100 trials. Conclusion The proposed methods are easy to use and will be of high value to regulatory agencies and decision makers who must assess the strength of the evidence supporting comparative effectiveness estimates. PMID:22992327

You Cannot Step Into the Same River Twice: When Power Analyses Are Optimistic.

PubMed

McShane, Blakeley B; Böckenholt, Ulf

2014-11-01

Statistical power depends on the size of the effect of interest. However, effect sizes are rarely fixed in psychological research: Study design choices, such as the operationalization of the dependent variable or the treatment manipulation, the social context, the subject pool, or the time of day, typically cause systematic variation in the effect size. Ignoring this between-study variation, as standard power formulae do, results in assessments of power that are too optimistic. Consequently, when researchers attempting replication set sample sizes using these formulae, their studies will be underpowered and will thus fail at a greater than expected rate. We illustrate this with both hypothetical examples and data on several well-studied phenomena in psychology. We provide formulae that account for between-study variation and suggest that researchers set sample sizes with respect to our generally more conservative formulae. Our formulae generalize to settings in which there are multiple effects of interest. We also introduce an easy-to-use website that implements our approach to setting sample sizes. Finally, we conclude with recommendations for quantifying between-study variation. © The Author(s) 2014.

Experimental design, power and sample size for animal reproduction experiments.

PubMed

Chapman, Phillip L; Seidel, George E

2008-01-01

The present paper concerns statistical issues in the design of animal reproduction experiments, with emphasis on the problems of sample size determination and power calculations. We include examples and non-technical discussions aimed at helping researchers avoid serious errors that may invalidate or seriously impair the validity of conclusions from experiments. Screen shots from interactive power calculation programs and basic SAS power calculation programs are presented to aid in understanding statistical power and computing power in some common experimental situations. Practical issues that are common to most statistical design problems are briefly discussed. These include one-sided hypothesis tests, power level criteria, equality of within-group variances, transformations of response variables to achieve variance equality, optimal specification of treatment group sizes, 'post hoc' power analysis and arguments for the increased use of confidence intervals in place of hypothesis tests.

Design of the value of imaging in enhancing the wellness of your heart (VIEW) trial and the impact of uncertainty on power.

PubMed

Ambrosius, Walter T; Polonsky, Tamar S; Greenland, Philip; Goff, David C; Perdue, Letitia H; Fortmann, Stephen P; Margolis, Karen L; Pajewski, Nicholas M

2012-04-01

Although observational evidence has suggested that the measurement of coronary artery calcium (CAC) may improve risk stratification for cardiovascular events and thus help guide the use of lipid-lowering therapy, this contention has not been evaluated within the context of a randomized trial. The Value of Imaging in Enhancing the Wellness of Your Heart (VIEW) trial is proposed as a randomized study in participants at low intermediate risk of future coronary heart disease (CHD) events to evaluate whether CAC testing leads to improved patient outcomes. To describe the challenges encountered in designing a prototypical screening trial and to examine the impact of uncertainty on power. The VIEW trial was designed as an effectiveness clinical trial to examine the benefit of CAC testing to guide therapy on a primary outcome consisting of a composite of nonfatal myocardial infarction, probable or definite angina with revascularization, resuscitated cardiac arrest, nonfatal stroke (not transient ischemic attack (TIA)), CHD death, stroke death, other atherosclerotic death, or other cardiovascular disease (CVD) death. Many critical choices were faced in designing the trial, including (1) the choice of primary outcome, (2) the choice of therapy, (3) the target population with corresponding ethical issues, (4) specifications of assumptions for sample size calculations, and (5) impact of uncertainty in these assumptions on power/sample size determination. We have proposed a sample size of 30,000 (800 events), which provides 92.7% power. Alternatively, sample sizes of 20,228 (539 events), 23,138 (617 events), and 27,078 (722 events) provide 80%, 85%, and 90% power. We have also allowed for uncertainty in our assumptions by computing average power integrated over specified prior distributions. This relaxation of specificity indicates a reduction in power, dropping to 89.9% (95% confidence interval (CI): 89.8-89.9) for a sample size of 30,000. Samples sizes of 20,228, 23,138, and 27,078 provide power of 78.0% (77.9-78.0), 82.5% (82.5-82.6), and 87.2% (87.2-87.3), respectively. These power estimates are dependent on form and parameters of the prior distributions. Despite the pressing need for a randomized trial to evaluate the utility of CAC testing, conduct of such a trial requires recruiting a large patient population, making efficiency of critical importance. The large sample size is primarily due to targeting a study population at relatively low risk of a CVD event. Our calculations also illustrate the importance of formally considering uncertainty in power calculations of large trials as standard power calculations may tend to overestimate power.

Design of the Value of Imaging in Enhancing the Wellness of Your Heart (VIEW) Trial and the Impact of Uncertainty on Power

PubMed Central

Ambrosius, Walter T.; Polonsky, Tamar S.; Greenland, Philip; Goff, David C.; Perdue, Letitia H.; Fortmann, Stephen P.; Margolis, Karen L.; Pajewski, Nicholas M.

2014-01-01

Background Although observational evidence has suggested that the measurement of CAC may improve risk stratification for cardiovascular events and thus help guide the use of lipid-lowering therapy, this contention has not been evaluated within the context of a randomized trial. The Value of Imaging in Enhancing the Wellness of Your Heart (VIEW) trial is proposed as a randomized study in participants at low intermediate risk of future coronary heart disease (CHD) events to evaluate whether coronary artery calcium (CAC) testing leads to improved patient outcomes. Purpose To describe the challenges encountered in designing a prototypical screening trial and to examine the impact of uncertainty on power. Methods The VIEW trial was designed as an effectiveness clinical trial to examine the benefit of CAC testing to guide therapy on a primary outcome consisting of a composite of non-fatal myocardial infarction, probable or definite angina with revascularization, resuscitated cardiac arrest, non-fatal stroke (not transient ischemic attack (TIA)), CHD death, stroke death, other atherosclerotic death, or other cardiovascular disease (CVD) death. Many critical choices were faced in designing the trial, including: (1) the choice of primary outcome, (2) the choice of therapy, (3) the target population with corresponding ethical issues, (4) specifications of assumptions for sample size calculations, and (5) impact of uncertainty in these assumptions on power/sample size determination. Results We have proposed a sample size of 30,000 (800 events) which provides 92.7% power. Alternatively, sample sizes of 20,228 (539 events), 23,138 (617 events) and 27,078 (722 events) provide 80, 85, and 90% power. We have also allowed for uncertainty in our assumptions by computing average power integrated over specified prior distributions. This relaxation of specificity indicates a reduction in power, dropping to 89.9% (95% confidence interval (CI): 89.8 to 89.9) for a sample size of 30,000. Samples sizes of 20,228, 23,138, and 27,078 provide power of 78.0% (77.9 to 78.0), 82.5% (82.5 to 82.6), and 87.2% (87.2 to 87.3), respectively. Limitations These power estimates are dependent on form and parameters of the prior distributions. Conclusions Despite the pressing need for a randomized trial to evaluate the utility of CAC testing, conduct of such a trial requires recruiting a large patient population, making efficiency of critical importance. The large sample size is primarily due to targeting a study population at relatively low risk of a CVD event. Our calculations also illustrate the importance of formally considering uncertainty in power calculations of large trials as standard power calculations may tend to overestimate power. PMID:22333998

A sequential bioequivalence design with a potential ethical advantage.

PubMed

Fuglsang, Anders

2014-07-01

This paper introduces a two-stage approach for evaluation of bioequivalence, where, in contrast to the designs of Diane Potvin and co-workers, two stages are mandatory regardless of the data obtained at stage 1. The approach is derived from Potvin's method C. It is shown that under circumstances with relatively high variability and relatively low initial sample size, this method has an advantage over Potvin's approaches in terms of sample sizes while controlling type I error rates at or below 5% with a minute occasional trade-off in power. Ethically and economically, the method may thus be an attractive alternative to the Potvin designs. It is also shown that when using the method introduced here, average total sample sizes are rather independent of initial sample size. Finally, it is shown that when a futility rule in terms of sample size for stage 2 is incorporated into this method, i.e., when a second stage can be abolished due to sample size considerations, there is often an advantage in terms of power or sample size as compared to the previously published methods.

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

Experimental and numerical modeling research of rubber material during microwave heating process

NASA Astrophysics Data System (ADS)

Chen, Hailong; Li, Tao; Li, Kunling; Li, Qingling

2018-05-01

This paper aims to investigate the heating behaviors of block rubber by experimental and simulated method. The COMSOL Multiphysics 5.0 software was utilized in numerical simulation work. The effects of microwave frequency, power and sample size on temperature distribution are examined. The effect of frequency on temperature distribution is obvious. The maximum and minimum temperatures of block rubber increase first and then decrease with frequency increasing. The microwave heating efficiency is maximum in the microwave frequency of 2450 MHz. However, more uniform temperature distribution is presented in other microwave frequencies. The influence of microwave power on temperature distribution is also remarkable. The smaller the power, the more uniform the temperature distribution on the block rubber. The effect of power on microwave heating efficiency is not obvious. The effect of sample size on temperature distribution is evidently found. The smaller the sample size, the more uniform the temperature distribution on the block rubber. However, the smaller the sample size, the lower the microwave heating efficiency. The results can serve as references for the research on heating rubber material by microwave technology.

Seven ways to increase power without increasing N.

PubMed

Hansen, W B; Collins, L M

1994-01-01

Many readers of this monograph may wonder why a chapter on statistical power was included. After all, by now the issue of statistical power is in many respects mundane. Everyone knows that statistical power is a central research consideration, and certainly most National Institute on Drug Abuse grantees or prospective grantees understand the importance of including a power analysis in research proposals. However, there is ample evidence that, in practice, prevention researchers are not paying sufficient attention to statistical power. If they were, the findings observed by Hansen (1992) in a recent review of the prevention literature would not have emerged. Hansen (1992) examined statistical power based on 46 cohorts followed longitudinally, using nonparametric assumptions given the subjects' age at posttest and the numbers of subjects. Results of this analysis indicated that, in order for a study to attain 80-percent power for detecting differences between treatment and control groups, the difference between groups at posttest would need to be at least 8 percent (in the best studies) and as much as 16 percent (in the weakest studies). In order for a study to attain 80-percent power for detecting group differences in pre-post change, 22 of the 46 cohorts would have needed relative pre-post reductions of greater than 100 percent. Thirty-three of the 46 cohorts had less than 50-percent power to detect a 50-percent relative reduction in substance use. These results are consistent with other review findings (e.g., Lipsey 1990) that have shown a similar lack of power in a broad range of research topics. Thus, it seems that, although researchers are aware of the importance of statistical power (particularly of the necessity for calculating it when proposing research), they somehow are failing to end up with adequate power in their completed studies. This chapter argues that the failure of many prevention studies to maintain adequate statistical power is due to an overemphasis on sample size (N) as the only, or even the best, way to increase statistical power. It is easy to see how this overemphasis has come about. Sample size is easy to manipulate, has the advantage of being related to power in a straight-forward way, and usually is under the direct control of the researcher, except for limitations imposed by finances or subject availability. Another option for increasing power is to increase the alpha used for hypothesis-testing but, as very few researchers seriously consider significance levels much larger than the traditional .05, this strategy seldom is used. Of course, sample size is important, and the authors of this chapter are not recommending that researchers cease choosing sample sizes carefully. Rather, they argue that researchers should not confine themselves to increasing N to enhance power. It is important to take additional measures to maintain and improve power over and above making sure the initial sample size is sufficient. The authors recommend two general strategies. One strategy involves attempting to maintain the effective initial sample size so that power is not lost needlessly. The other strategy is to take measures to maximize the third factor that determines statistical power: effect size.

Determining the sample size for co-dominant molecular marker-assisted linkage detection for a monogenic qualitative trait by controlling the type-I and type-II errors in a segregating F2 population.

PubMed

Hühn, M; Piepho, H P

2003-03-01

Tests for linkage are usually performed using the lod score method. A critical question in linkage analyses is the choice of sample size. The appropriate sample size depends on the desired type-I error and power of the test. This paper investigates the exact type-I error and power of the lod score method in a segregating F(2) population with co-dominant markers and a qualitative monogenic dominant-recessive trait. For illustration, a disease-resistance trait is considered, where the susceptible allele is recessive. A procedure is suggested for finding the appropriate sample size. It is shown that recessive plants have about twice the information content of dominant plants, so the former should be preferred for linkage detection. In some cases the exact alpha-values for a given nominal alpha may be rather small due to the discrete nature of the sampling distribution in small samples. We show that a gain in power is possible by using exact methods.

On the Post Hoc Power in Testing Mean Differences

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Maxwell, Scott

2005-01-01

Retrospective or post hoc power analysis is recommended by reviewers and editors of many journals. Little literature has been found that gave a serious study of the post hoc power. When the sample size is large, the observed effect size is a good estimator of the true power. This article studies whether such a power estimator provides valuable…

Blinded sample size re-estimation in three-arm trials with 'gold standard' design.

PubMed

Mütze, Tobias; Friede, Tim

2017-10-15

In this article, we study blinded sample size re-estimation in the 'gold standard' design with internal pilot study for normally distributed outcomes. The 'gold standard' design is a three-arm clinical trial design that includes an active and a placebo control in addition to an experimental treatment. We focus on the absolute margin approach to hypothesis testing in three-arm trials at which the non-inferiority of the experimental treatment and the assay sensitivity are assessed by pairwise comparisons. We compare several blinded sample size re-estimation procedures in a simulation study assessing operating characteristics including power and type I error. We find that sample size re-estimation based on the popular one-sample variance estimator results in overpowered trials. Moreover, sample size re-estimation based on unbiased variance estimators such as the Xing-Ganju variance estimator results in underpowered trials, as it is expected because an overestimation of the variance and thus the sample size is in general required for the re-estimation procedure to eventually meet the target power. To overcome this problem, we propose an inflation factor for the sample size re-estimation with the Xing-Ganju variance estimator and show that this approach results in adequately powered trials. Because of favorable features of the Xing-Ganju variance estimator such as unbiasedness and a distribution independent of the group means, the inflation factor does not depend on the nuisance parameter and, therefore, can be calculated prior to a trial. Moreover, we prove that the sample size re-estimation based on the Xing-Ganju variance estimator does not bias the effect estimate. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Revisiting sample size: are big trials the answer?

PubMed

Lurati Buse, Giovanna A L; Botto, Fernando; Devereaux, P J

2012-07-18

The superiority of the evidence generated in randomized controlled trials over observational data is not only conditional to randomization. Randomized controlled trials require proper design and implementation to provide a reliable effect estimate. Adequate random sequence generation, allocation implementation, analyses based on the intention-to-treat principle, and sufficient power are crucial to the quality of a randomized controlled trial. Power, or the probability of the trial to detect a difference when a real difference between treatments exists, strongly depends on sample size. The quality of orthopaedic randomized controlled trials is frequently threatened by a limited sample size. This paper reviews basic concepts and pitfalls in sample-size estimation and focuses on the importance of large trials in the generation of valid evidence.

Effect size and statistical power in the rodent fear conditioning literature - A systematic review.

PubMed

Carneiro, Clarissa F D; Moulin, Thiago C; Macleod, Malcolm R; Amaral, Olavo B

2018-01-01

Proposals to increase research reproducibility frequently call for focusing on effect sizes instead of p values, as well as for increasing the statistical power of experiments. However, it is unclear to what extent these two concepts are indeed taken into account in basic biomedical science. To study this in a real-case scenario, we performed a systematic review of effect sizes and statistical power in studies on learning of rodent fear conditioning, a widely used behavioral task to evaluate memory. Our search criteria yielded 410 experiments comparing control and treated groups in 122 articles. Interventions had a mean effect size of 29.5%, and amnesia caused by memory-impairing interventions was nearly always partial. Mean statistical power to detect the average effect size observed in well-powered experiments with significant differences (37.2%) was 65%, and was lower among studies with non-significant results. Only one article reported a sample size calculation, and our estimated sample size to achieve 80% power considering typical effect sizes and variances (15 animals per group) was reached in only 12.2% of experiments. Actual effect sizes correlated with effect size inferences made by readers on the basis of textual descriptions of results only when findings were non-significant, and neither effect size nor power correlated with study quality indicators, number of citations or impact factor of the publishing journal. In summary, effect sizes and statistical power have a wide distribution in the rodent fear conditioning literature, but do not seem to have a large influence on how results are described or cited. Failure to take these concepts into consideration might limit attempts to improve reproducibility in this field of science.

Effect size and statistical power in the rodent fear conditioning literature – A systematic review

PubMed Central

Macleod, Malcolm R.

2018-01-01

Proposals to increase research reproducibility frequently call for focusing on effect sizes instead of p values, as well as for increasing the statistical power of experiments. However, it is unclear to what extent these two concepts are indeed taken into account in basic biomedical science. To study this in a real-case scenario, we performed a systematic review of effect sizes and statistical power in studies on learning of rodent fear conditioning, a widely used behavioral task to evaluate memory. Our search criteria yielded 410 experiments comparing control and treated groups in 122 articles. Interventions had a mean effect size of 29.5%, and amnesia caused by memory-impairing interventions was nearly always partial. Mean statistical power to detect the average effect size observed in well-powered experiments with significant differences (37.2%) was 65%, and was lower among studies with non-significant results. Only one article reported a sample size calculation, and our estimated sample size to achieve 80% power considering typical effect sizes and variances (15 animals per group) was reached in only 12.2% of experiments. Actual effect sizes correlated with effect size inferences made by readers on the basis of textual descriptions of results only when findings were non-significant, and neither effect size nor power correlated with study quality indicators, number of citations or impact factor of the publishing journal. In summary, effect sizes and statistical power have a wide distribution in the rodent fear conditioning literature, but do not seem to have a large influence on how results are described or cited. Failure to take these concepts into consideration might limit attempts to improve reproducibility in this field of science. PMID:29698451

Longitudinal design considerations to optimize power to detect variances and covariances among rates of change: Simulation results based on actual longitudinal studies

PubMed Central

Rast, Philippe; Hofer, Scott M.

2014-01-01

We investigated the power to detect variances and covariances in rates of change in the context of existing longitudinal studies using linear bivariate growth curve models. Power was estimated by means of Monte Carlo simulations. Our findings show that typical longitudinal study designs have substantial power to detect both variances and covariances among rates of change in a variety of cognitive, physical functioning, and mental health outcomes. We performed simulations to investigate the interplay among number and spacing of occasions, total duration of the study, effect size, and error variance on power and required sample size. The relation between growth rate reliability (GRR) and effect size to the sample size required to detect power ≥ .80 was non-linear, with rapidly decreasing sample sizes needed as GRR increases. The results presented here stand in contrast to previous simulation results and recommendations (Hertzog, Lindenberger, Ghisletta, & von Oertzen, 2006; Hertzog, von Oertzen, Ghisletta, & Lindenberger, 2008; von Oertzen, Ghisletta, & Lindenberger, 2010), which are limited due to confounds between study length and number of waves, error variance with GCR, and parameter values which are largely out of bounds of actual study values. Power to detect change is generally low in the early phases (i.e. first years) of longitudinal studies but can substantially increase if the design is optimized. We recommend additional assessments, including embedded intensive measurement designs, to improve power in the early phases of long-term longitudinal studies. PMID:24219544

A New Sample Size Formula for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.; Barcikowski, Robert S.

The focus of this research was to determine the efficacy of a new method of selecting sample sizes for multiple linear regression. A Monte Carlo simulation was used to study both empirical predictive power rates and empirical statistical power rates of the new method and seven other methods: those of C. N. Park and A. L. Dudycha (1974); J. Cohen…

The Statistical Power of Planned Comparisons.

ERIC Educational Resources Information Center

Benton, Roberta L.

Basic principles underlying statistical power are examined; and issues pertaining to effect size, sample size, error variance, and significance level are highlighted via the use of specific hypothetical examples. Analysis of variance (ANOVA) and related methods remain popular, although other procedures sometimes have more statistical power against…

A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test.

PubMed

Yu, Qingzhao; Zhu, Lin; Zhu, Han

2017-11-01

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size. Copyright © 2017 John Wiley & Sons, Ltd.

Synthesis And Characterization Of Reduced Size Ferrite Reinforced Polymer Composites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borah, Subasit; Bhattacharyya, Nidhi S.

2008-04-24

Small sized Co{sub 1-x}Ni{sub x}Fe{sub 2}O{sub 4} ferrite particles are synthesized by chemical route. The precursor materials are annealed at 400, 600 and 800 C. The crystallographic structure and phases of the samples are characterized by X-ray diffraction (XRD). The annealed ferrite samples crystallized into cubic spinel structure. Transmission Electron Microscopy (TEM) micrographs show that the average particle size of the samples are <20 nm. Particulate magneto-polymer composite materials are fabricated by reinforcing low density polyethylene (LDPE) matrix with the ferrite samples. The B-H loop study conducted at 10 kHz on the toroid shaped composite samples shows reduction in magneticmore » losses with decrease in size of the filler sample. Magnetic losses are detrimental for applications of ferrite at high powers. The reduction in magnetic loss shows a possible application of Co-Ni ferrites at high microwave power levels.« less

Rule-of-thumb adjustment of sample sizes to accommodate dropouts in a two-stage analysis of repeated measurements.

PubMed

Overall, John E; Tonidandel, Scott; Starbuck, Robert R

2006-01-01

Recent contributions to the statistical literature have provided elegant model-based solutions to the problem of estimating sample sizes for testing the significance of differences in mean rates of change across repeated measures in controlled longitudinal studies with differentially correlated error and missing data due to dropouts. However, the mathematical complexity and model specificity of these solutions make them generally inaccessible to most applied researchers who actually design and undertake treatment evaluation research in psychiatry. In contrast, this article relies on a simple two-stage analysis in which dropout-weighted slope coefficients fitted to the available repeated measurements for each subject separately serve as the dependent variable for a familiar ANCOVA test of significance for differences in mean rates of change. This article is about how a sample of size that is estimated or calculated to provide desired power for testing that hypothesis without considering dropouts can be adjusted appropriately to take dropouts into account. Empirical results support the conclusion that, whatever reasonable level of power would be provided by a given sample size in the absence of dropouts, essentially the same power can be realized in the presence of dropouts simply by adding to the original dropout-free sample size the number of subjects who would be expected to drop from a sample of that original size under conditions of the proposed study.

Sample Size Requirements and Study Duration for Testing Main Effects and Interactions in Completely Randomized Factorial Designs When Time to Event is the Outcome

PubMed Central

Moser, Barry Kurt; Halabi, Susan

2013-01-01

In this paper we develop the methodology for designing clinical trials with any factorial arrangement when the primary outcome is time to event. We provide a matrix formulation for calculating the sample size and study duration necessary to test any effect with a pre-specified type I error rate and power. Assuming that a time to event follows an exponential distribution, we describe the relationships between the effect size, the power, and the sample size. We present examples for illustration purposes. We provide a simulation study to verify the numerical calculations of the expected number of events and the duration of the trial. The change in the power produced by a reduced number of observations or by accruing no patients to certain factorial combinations is also described. PMID:25530661

Are power calculations useful? A multicentre neuroimaging study

PubMed Central

Suckling, John; Henty, Julian; Ecker, Christine; Deoni, Sean C; Lombardo, Michael V; Baron-Cohen, Simon; Jezzard, Peter; Barnes, Anna; Chakrabarti, Bhismadev; Ooi, Cinly; Lai, Meng-Chuan; Williams, Steven C; Murphy, Declan GM; Bullmore, Edward

2014-01-01

There are now many reports of imaging experiments with small cohorts of typical participants that precede large-scale, often multicentre studies of psychiatric and neurological disorders. Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes. In this technical note, we suggest how previously reported voxel-based power calculations can support decision making in the design, execution and analysis of cross-sectional multicentre imaging studies. The choice of MRI acquisition sequence, distribution of recruitment across acquisition centres, and changes to the registration method applied during data analysis are considered as examples. The consequences of modification are explored in quantitative terms by assessing the impact on sample size for a fixed effect size and detectable effect size for a fixed sample size. The calibration experiment dataset used for illustration was a precursor to the now complete Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS). Validation of the voxel-based power calculations is made by comparing the predicted values from the calibration experiment with those observed in MRC-AIMS. The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation. In summary, power calculations offer a validated, quantitative means of making informed choices on important factors that influence the outcome of studies that consume significant resources. PMID:24644267

Reporting of sample size calculations in analgesic clinical trials: ACTTION systematic review.

PubMed

McKeown, Andrew; Gewandter, Jennifer S; McDermott, Michael P; Pawlowski, Joseph R; Poli, Joseph J; Rothstein, Daniel; Farrar, John T; Gilron, Ian; Katz, Nathaniel P; Lin, Allison H; Rappaport, Bob A; Rowbotham, Michael C; Turk, Dennis C; Dworkin, Robert H; Smith, Shannon M

2015-03-01

Sample size calculations determine the number of participants required to have sufficiently high power to detect a given treatment effect. In this review, we examined the reporting quality of sample size calculations in 172 publications of double-blind randomized controlled trials of noninvasive pharmacologic or interventional (ie, invasive) pain treatments published in European Journal of Pain, Journal of Pain, and Pain from January 2006 through June 2013. Sixty-five percent of publications reported a sample size calculation but only 38% provided all elements required to replicate the calculated sample size. In publications reporting at least 1 element, 54% provided a justification for the treatment effect used to calculate sample size, and 24% of studies with continuous outcome variables justified the variability estimate. Publications of clinical pain condition trials reported a sample size calculation more frequently than experimental pain model trials (77% vs 33%, P < .001) but did not differ in the frequency of reporting all required elements. No significant differences in reporting of any or all elements were detected between publications of trials with industry and nonindustry sponsorship. Twenty-eight percent included a discrepancy between the reported number of planned and randomized participants. This study suggests that sample size calculation reporting in analgesic trial publications is usually incomplete. Investigators should provide detailed accounts of sample size calculations in publications of clinical trials of pain treatments, which is necessary for reporting transparency and communication of pre-trial design decisions. In this systematic review of analgesic clinical trials, sample size calculations and the required elements (eg, treatment effect to be detected; power level) were incompletely reported. A lack of transparency regarding sample size calculations may raise questions about the appropriateness of the calculated sample size. Copyright © 2015 American Pain Society. All rights reserved.

Got power? A systematic review of sample size adequacy in health professions education research.

PubMed

Cook, David A; Hatala, Rose

2015-03-01

Many education research studies employ small samples, which in turn lowers statistical power. We re-analyzed the results of a meta-analysis of simulation-based education to determine study power across a range of effect sizes, and the smallest effect that could be plausibly excluded. We systematically searched multiple databases through May 2011, and included all studies evaluating simulation-based education for health professionals in comparison with no intervention or another simulation intervention. Reviewers working in duplicate abstracted information to calculate standardized mean differences (SMD's). We included 897 original research studies. Among the 627 no-intervention-comparison studies the median sample size was 25. Only two studies (0.3%) had ≥80% power to detect a small difference (SMD > 0.2 standard deviations) and 136 (22%) had power to detect a large difference (SMD > 0.8). 110 no-intervention-comparison studies failed to find a statistically significant difference, but none excluded a small difference and only 47 (43%) excluded a large difference. Among 297 studies comparing alternate simulation approaches the median sample size was 30. Only one study (0.3%) had ≥80% power to detect a small difference and 79 (27%) had power to detect a large difference. Of the 128 studies that did not detect a statistically significant effect, 4 (3%) excluded a small difference and 91 (71%) excluded a large difference. In conclusion, most education research studies are powered only to detect effects of large magnitude. For most studies that do not reach statistical significance, the possibility of large and important differences still exists.

Sample size requirements for separating out the effects of combination treatments: randomised controlled trials of combination therapy vs. standard treatment compared to factorial designs for patients with tuberculous meningitis.

PubMed

Wolbers, Marcel; Heemskerk, Dorothee; Chau, Tran Thi Hong; Yen, Nguyen Thi Bich; Caws, Maxine; Farrar, Jeremy; Day, Jeremy

2011-02-02

In certain diseases clinical experts may judge that the intervention with the best prospects is the addition of two treatments to the standard of care. This can either be tested with a simple randomized trial of combination versus standard treatment or with a 2 x 2 factorial design. We compared the two approaches using the design of a new trial in tuberculous meningitis as an example. In that trial the combination of 2 drugs added to standard treatment is assumed to reduce the hazard of death by 30% and the sample size of the combination trial to achieve 80% power is 750 patients. We calculated the power of corresponding factorial designs with one- to sixteen-fold the sample size of the combination trial depending on the contribution of each individual drug to the combination treatment effect and the strength of an interaction between the two. In the absence of an interaction, an eight-fold increase in sample size for the factorial design as compared to the combination trial is required to get 80% power to jointly detect effects of both drugs if the contribution of the less potent treatment to the total effect is at least 35%. An eight-fold sample size increase also provides a power of 76% to detect a qualitative interaction at the one-sided 10% significance level if the individual effects of both drugs are equal. Factorial designs with a lower sample size have a high chance to be underpowered, to show significance of only one drug even if both are equally effective, and to miss important interactions. Pragmatic combination trials of multiple interventions versus standard therapy are valuable in diseases with a limited patient pool if all interventions test the same treatment concept, it is considered likely that either both or none of the individual interventions are effective, and only moderate drug interactions are suspected. An adequately powered 2 x 2 factorial design to detect effects of individual drugs would require at least 8-fold the sample size of the combination trial. Current Controlled Trials ISRCTN61649292.

The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency.

PubMed

Sim, Julius; Lewis, Martyn

2012-03-01

To investigate methods to determine the size of a pilot study to inform a power calculation for a randomized controlled trial (RCT) using an interval/ratio outcome measure. Calculations based on confidence intervals (CIs) for the sample standard deviation (SD). Based on CIs for the sample SD, methods are demonstrated whereby (1) the observed SD can be adjusted to secure the desired level of statistical power in the main study with a specified level of confidence; (2) the sample for the main study, if calculated using the observed SD, can be adjusted, again to obtain the desired level of statistical power in the main study; (3) the power of the main study can be calculated for the situation in which the SD in the pilot study proves to be an underestimate of the true SD; and (4) an "efficient" pilot size can be determined to minimize the combined size of the pilot and main RCT. Trialists should calculate the appropriate size of a pilot study, just as they should the size of the main RCT, taking into account the twin needs to demonstrate efficiency in terms of recruitment and to produce precise estimates of treatment effect. Copyright © 2012 Elsevier Inc. All rights reserved.

On Two-Stage Multiple Comparison Procedures When There Are Unequal Sample Sizes in the First Stage.

ERIC Educational Resources Information Center

Wilcox, Rand R.

1984-01-01

Two stage multiple-comparison procedures give an exact solution to problems of power and Type I errors, but require equal sample sizes in the first stage. This paper suggests a method of evaluating the experimentwise Type I error probability when the first stage has unequal sample sizes. (Author/BW)

Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety

ERIC Educational Resources Information Center

Wolf, Erika J.; Harrington, Kelly M.; Clark, Shaunna L.; Miller, Mark W.

2013-01-01

Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb.…

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Sample size determination for a three-arm equivalence trial of Poisson and negative binomial responses.

PubMed

Chang, Yu-Wei; Tsong, Yi; Zhao, Zhigen

2017-01-01

Assessing equivalence or similarity has drawn much attention recently as many drug products have lost or will lose their patents in the next few years, especially certain best-selling biologics. To claim equivalence between the test treatment and the reference treatment when assay sensitivity is well established from historical data, one has to demonstrate both superiority of the test treatment over placebo and equivalence between the test treatment and the reference treatment. Thus, there is urgency for practitioners to derive a practical way to calculate sample size for a three-arm equivalence trial. The primary endpoints of a clinical trial may not always be continuous, but may be discrete. In this paper, the authors derive power function and discuss sample size requirement for a three-arm equivalence trial with Poisson and negative binomial clinical endpoints. In addition, the authors examine the effect of the dispersion parameter on the power and the sample size by varying its coefficient from small to large. In extensive numerical studies, the authors demonstrate that required sample size heavily depends on the dispersion parameter. Therefore, misusing a Poisson model for negative binomial data may easily lose power up to 20%, depending on the value of the dispersion parameter.

Efficient Bayesian mixed model analysis increases association power in large cohorts

PubMed Central

Loh, Po-Ru; Tucker, George; Bulik-Sullivan, Brendan K; Vilhjálmsson, Bjarni J; Finucane, Hilary K; Salem, Rany M; Chasman, Daniel I; Ridker, Paul M; Neale, Benjamin M; Berger, Bonnie; Patterson, Nick; Price, Alkes L

2014-01-01

Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts, and may not optimize power. All existing methods require time cost O(MN2) (where N = #samples and M = #SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here, we present a far more efficient mixed model association method, BOLT-LMM, which requires only a small number of O(MN)-time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to nine quantitative traits in 23,294 samples from the Women’s Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for GWAS in large cohorts. PMID:25642633

Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials

PubMed Central

Gilbert, Peter B.; Janes, Holly E.; Huang, Yunda

2016-01-01

In a randomized controlled clinical trial that assesses treatment efficacy, a common objective is to assess the association of a measured biomarker response endpoint with the primary study endpoint in the active treatment group, using a case-cohort, case-control, or two-phase sampling design. Methods for power and sample size calculations for such biomarker association analyses typically do not account for the level of treatment efficacy, precluding interpretation of the biomarker association results in terms of biomarker effect modification of treatment efficacy, with detriment that the power calculations may tacitly and inadvertently assume that the treatment harms some study participants. We develop power and sample size methods accounting for this issue, and the methods also account for inter-individual variability of the biomarker that is not biologically relevant (e.g., due to technical measurement error). We focus on a binary study endpoint and on a biomarker subject to measurement error that is normally distributed or categorical with two or three levels. We illustrate the methods with preventive HIV vaccine efficacy trials, and include an R package implementing the methods. PMID:27037797

Sample Size and Power Estimates for a Confirmatory Factor Analytic Model in Exercise and Sport: A Monte Carlo Approach

ERIC Educational Resources Information Center

Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying

2011-01-01

Monte Carlo methods can be used in data analytic situations (e.g., validity studies) to make decisions about sample size and to estimate power. The purpose of using Monte Carlo methods in a validity study is to improve the methodological approach within a study where the primary focus is on construct validity issues and not on advancing…

Neuromuscular dose-response studies: determining sample size.

PubMed

Kopman, A F; Lien, C A; Naguib, M

2011-02-01

Investigators planning dose-response studies of neuromuscular blockers have rarely used a priori power analysis to determine the minimal sample size their protocols require. Institutional Review Boards and peer-reviewed journals now generally ask for this information. This study outlines a proposed method for meeting these requirements. The slopes of the dose-response relationships of eight neuromuscular blocking agents were determined using regression analysis. These values were substituted for γ in the Hill equation. When this is done, the coefficient of variation (COV) around the mean value of the ED₅₀ for each drug is easily calculated. Using these values, we performed an a priori one-sample two-tailed t-test of the means to determine the required sample size when the allowable error in the ED₅₀ was varied from ±10-20%. The COV averaged 22% (range 15-27%). We used a COV value of 25% in determining the sample size. If the allowable error in finding the mean ED₅₀ is ±15%, a sample size of 24 is needed to achieve a power of 80%. Increasing 'accuracy' beyond this point requires increasing greater sample sizes (e.g. an 'n' of 37 for a ±12% error). On the basis of the results of this retrospective analysis, a total sample size of not less than 24 subjects should be adequate for determining a neuromuscular blocking drug's clinical potency with a reasonable degree of assurance.

Designing image segmentation studies: Statistical power, sample size and reference standard quality.

PubMed

Gibson, Eli; Hu, Yipeng; Huisman, Henkjan J; Barratt, Dean C

2017-12-01

Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Optimal sample sizes for the design of reliability studies: power consideration.

PubMed

Shieh, Gwowen

2014-09-01

Intraclass correlation coefficients are used extensively to measure the reliability or degree of resemblance among group members in multilevel research. This study concerns the problem of the necessary sample size to ensure adequate statistical power for hypothesis tests concerning the intraclass correlation coefficient in the one-way random-effects model. In view of the incomplete and problematic numerical results in the literature, the approximate sample size formula constructed from Fisher's transformation is reevaluated and compared with an exact approach across a wide range of model configurations. These comprehensive examinations showed that the Fisher transformation method is appropriate only under limited circumstances, and therefore it is not recommended as a general method in practice. For advance design planning of reliability studies, the exact sample size procedures are fully described and illustrated for various allocation and cost schemes. Corresponding computer programs are also developed to implement the suggested algorithms.

Sample size requirements for separating out the effects of combination treatments: Randomised controlled trials of combination therapy vs. standard treatment compared to factorial designs for patients with tuberculous meningitis

PubMed Central

2011-01-01

Background In certain diseases clinical experts may judge that the intervention with the best prospects is the addition of two treatments to the standard of care. This can either be tested with a simple randomized trial of combination versus standard treatment or with a 2 × 2 factorial design. Methods We compared the two approaches using the design of a new trial in tuberculous meningitis as an example. In that trial the combination of 2 drugs added to standard treatment is assumed to reduce the hazard of death by 30% and the sample size of the combination trial to achieve 80% power is 750 patients. We calculated the power of corresponding factorial designs with one- to sixteen-fold the sample size of the combination trial depending on the contribution of each individual drug to the combination treatment effect and the strength of an interaction between the two. Results In the absence of an interaction, an eight-fold increase in sample size for the factorial design as compared to the combination trial is required to get 80% power to jointly detect effects of both drugs if the contribution of the less potent treatment to the total effect is at least 35%. An eight-fold sample size increase also provides a power of 76% to detect a qualitative interaction at the one-sided 10% significance level if the individual effects of both drugs are equal. Factorial designs with a lower sample size have a high chance to be underpowered, to show significance of only one drug even if both are equally effective, and to miss important interactions. Conclusions Pragmatic combination trials of multiple interventions versus standard therapy are valuable in diseases with a limited patient pool if all interventions test the same treatment concept, it is considered likely that either both or none of the individual interventions are effective, and only moderate drug interactions are suspected. An adequately powered 2 × 2 factorial design to detect effects of individual drugs would require at least 8-fold the sample size of the combination trial. Trial registration Current Controlled Trials ISRCTN61649292 PMID:21288326

A computational framework for estimating statistical power and planning hypothesis-driven experiments involving one-dimensional biomechanical continua.

PubMed

Pataky, Todd C; Robinson, Mark A; Vanrenterghem, Jos

2018-01-03

Statistical power assessment is an important component of hypothesis-driven research but until relatively recently (mid-1990s) no methods were available for assessing power in experiments involving continuum data and in particular those involving one-dimensional (1D) time series. The purpose of this study was to describe how continuum-level power analyses can be used to plan hypothesis-driven biomechanics experiments involving 1D data. In particular, we demonstrate how theory- and pilot-driven 1D effect modeling can be used for sample-size calculations for both single- and multi-subject experiments. For theory-driven power analysis we use the minimum jerk hypothesis and single-subject experiments involving straight-line, planar reaching. For pilot-driven power analysis we use a previously published knee kinematics dataset. Results show that powers on the order of 0.8 can be achieved with relatively small sample sizes, five and ten for within-subject minimum jerk analysis and between-subject knee kinematics, respectively. However, the appropriate sample size depends on a priori justifications of biomechanical meaning and effect size. The main advantage of the proposed technique is that it encourages a priori justification regarding the clinical and/or scientific meaning of particular 1D effects, thereby robustly structuring subsequent experimental inquiry. In short, it shifts focus from a search for significance to a search for non-rejectable hypotheses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Review of Sample Size for Structural Equation Models in Second Language Testing and Learning Research: A Monte Carlo Approach

ERIC Educational Resources Information Center

In'nami, Yo; Koizumi, Rie

2013-01-01

The importance of sample size, although widely discussed in the literature on structural equation modeling (SEM), has not been widely recognized among applied SEM researchers. To narrow this gap, we focus on second language testing and learning studies and examine the following: (a) Is the sample size sufficient in terms of precision and power of…

Detection of linkage between a quantitative trait and a marker locus by the lod score method: sample size and sampling considerations.

PubMed

Demenais, F; Lathrop, G M; Lalouel, J M

1988-07-01

A simulation study is here conducted to measure the power of the lod score method to detect linkage between a quantitative trait and a marker locus in various situations. The number of families necessary to detect such linkage with 80% power is assessed for different sets of parameters at the trait locus and different values of the recombination fraction. The effects of varying the mode of sampling families and the sibship size are also evaluated.

Estimation of sample size and testing power (part 6).

PubMed

Hu, Liang-ping; Bao, Xiao-lei; Guan, Xue; Zhou, Shi-guo

2012-03-01

The design of one factor with k levels (k ≥ 3) refers to the research that only involves one experimental factor with k levels (k ≥ 3), and there is no arrangement for other important non-experimental factors. This paper introduces the estimation of sample size and testing power for quantitative data and qualitative data having a binary response variable with the design of one factor with k levels (k ≥ 3).

Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA.

PubMed

Kelly, Brendan J; Gross, Robert; Bittinger, Kyle; Sherrill-Mix, Scott; Lewis, James D; Collman, Ronald G; Bushman, Frederic D; Li, Hongzhe

2015-08-01

The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

On sample size of the kruskal-wallis test with application to a mouse peritoneal cavity study.

PubMed

Fan, Chunpeng; Zhang, Donghui; Zhang, Cun-Hui

2011-03-01

As the nonparametric generalization of the one-way analysis of variance model, the Kruskal-Wallis test applies when the goal is to test the difference between multiple samples and the underlying population distributions are nonnormal or unknown. Although the Kruskal-Wallis test has been widely used for data analysis, power and sample size methods for this test have been investigated to a much lesser extent. This article proposes new power and sample size calculation methods for the Kruskal-Wallis test based on the pilot study in either a completely nonparametric model or a semiparametric location model. No assumption is made on the shape of the underlying population distributions. Simulation results show that, in terms of sample size calculation for the Kruskal-Wallis test, the proposed methods are more reliable and preferable to some more traditional methods. A mouse peritoneal cavity study is used to demonstrate the application of the methods. © 2010, The International Biometric Society.

Meta-analysis of genome-wide association from genomic prediction models

USDA-ARS?s Scientific Manuscript database

A limitation of many genome-wide association studies (GWA) in animal breeding is that there are many loci with small effect sizes; thus, larger sample sizes (N) are required to guarantee suitable power of detection. To increase sample size, results from different GWA can be combined in a meta-analys...

Designing Intervention Studies: Selected Populations, Range Restrictions, and Statistical Power

PubMed Central

Miciak, Jeremy; Taylor, W. Pat; Stuebing, Karla K.; Fletcher, Jack M.; Vaughn, Sharon

2016-01-01

An appropriate estimate of statistical power is critical for the design of intervention studies. Although the inclusion of a pretest covariate in the test of the primary outcome can increase statistical power, samples selected on the basis of pretest performance may demonstrate range restriction on the selection measure and other correlated measures. This can result in attenuated pretest-posttest correlations, reducing the variance explained by the pretest covariate. We investigated the implications of two potential range restriction scenarios: direct truncation on a selection measure and indirect range restriction on correlated measures. Empirical and simulated data indicated direct range restriction on the pretest covariate greatly reduced statistical power and necessitated sample size increases of 82%–155% (dependent on selection criteria) to achieve equivalent statistical power to parameters with unrestricted samples. However, measures demonstrating indirect range restriction required much smaller sample size increases (32%–71%) under equivalent scenarios. Additional analyses manipulated the correlations between measures and pretest-posttest correlations to guide planning experiments. Results highlight the need to differentiate between selection measures and potential covariates and to investigate range restriction as a factor impacting statistical power. PMID:28479943

Designing Intervention Studies: Selected Populations, Range Restrictions, and Statistical Power.

PubMed

Miciak, Jeremy; Taylor, W Pat; Stuebing, Karla K; Fletcher, Jack M; Vaughn, Sharon

2016-01-01

An appropriate estimate of statistical power is critical for the design of intervention studies. Although the inclusion of a pretest covariate in the test of the primary outcome can increase statistical power, samples selected on the basis of pretest performance may demonstrate range restriction on the selection measure and other correlated measures. This can result in attenuated pretest-posttest correlations, reducing the variance explained by the pretest covariate. We investigated the implications of two potential range restriction scenarios: direct truncation on a selection measure and indirect range restriction on correlated measures. Empirical and simulated data indicated direct range restriction on the pretest covariate greatly reduced statistical power and necessitated sample size increases of 82%-155% (dependent on selection criteria) to achieve equivalent statistical power to parameters with unrestricted samples. However, measures demonstrating indirect range restriction required much smaller sample size increases (32%-71%) under equivalent scenarios. Additional analyses manipulated the correlations between measures and pretest-posttest correlations to guide planning experiments. Results highlight the need to differentiate between selection measures and potential covariates and to investigate range restriction as a factor impacting statistical power.

Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure.

PubMed

Ciarleglio, Maria M; Arendt, Christopher D; Peduzzi, Peter N

2016-06-01

When designing studies that have a continuous outcome as the primary endpoint, the hypothesized effect size ([Formula: see text]), that is, the hypothesized difference in means ([Formula: see text]) relative to the assumed variability of the endpoint ([Formula: see text]), plays an important role in sample size and power calculations. Point estimates for [Formula: see text] and [Formula: see text] are often calculated using historical data. However, the uncertainty in these estimates is rarely addressed. This article presents a hybrid classical and Bayesian procedure that formally integrates prior information on the distributions of [Formula: see text] and [Formula: see text] into the study's power calculation. Conditional expected power, which averages the traditional power curve using the prior distributions of [Formula: see text] and [Formula: see text] as the averaging weight, is used, and the value of [Formula: see text] is found that equates the prespecified frequentist power ([Formula: see text]) and the conditional expected power of the trial. This hypothesized effect size is then used in traditional sample size calculations when determining sample size for the study. The value of [Formula: see text] found using this method may be expressed as a function of the prior means of [Formula: see text] and [Formula: see text], [Formula: see text], and their prior standard deviations, [Formula: see text]. We show that the "naïve" estimate of the effect size, that is, the ratio of prior means, should be down-weighted to account for the variability in the parameters. An example is presented for designing a placebo-controlled clinical trial testing the antidepressant effect of alprazolam as monotherapy for major depression. Through this method, we are able to formally integrate prior information on the uncertainty and variability of both the treatment effect and the common standard deviation into the design of the study while maintaining a frequentist framework for the final analysis. Solving for the effect size which the study has a high probability of correctly detecting based on the available prior information on the difference [Formula: see text] and the standard deviation [Formula: see text] provides a valuable, substantiated estimate that can form the basis for discussion about the study's feasibility during the design phase. © The Author(s) 2016.

The relation between statistical power and inference in fMRI

PubMed Central

Wager, Tor D.; Yarkoni, Tal

2017-01-01

Statistically underpowered studies can result in experimental failure even when all other experimental considerations have been addressed impeccably. In fMRI the combination of a large number of dependent variables, a relatively small number of observations (subjects), and a need to correct for multiple comparisons can decrease statistical power dramatically. This problem has been clearly addressed yet remains controversial—especially in regards to the expected effect sizes in fMRI, and especially for between-subjects effects such as group comparisons and brain-behavior correlations. We aimed to clarify the power problem by considering and contrasting two simulated scenarios of such possible brain-behavior correlations: weak diffuse effects and strong localized effects. Sampling from these scenarios shows that, particularly in the weak diffuse scenario, common sample sizes (n = 20–30) display extremely low statistical power, poorly represent the actual effects in the full sample, and show large variation on subsequent replications. Empirical data from the Human Connectome Project resembles the weak diffuse scenario much more than the localized strong scenario, which underscores the extent of the power problem for many studies. Possible solutions to the power problem include increasing the sample size, using less stringent thresholds, or focusing on a region-of-interest. However, these approaches are not always feasible and some have major drawbacks. The most prominent solutions that may help address the power problem include model-based (multivariate) prediction methods and meta-analyses with related synthesis-oriented approaches. PMID:29155843

Generalized SAMPLE SIZE Determination Formulas for Investigating Contextual Effects by a Three-Level Random Intercept Model.

PubMed

Usami, Satoshi

2017-03-01

Behavioral and psychological researchers have shown strong interests in investigating contextual effects (i.e., the influences of combinations of individual- and group-level predictors on individual-level outcomes). The present research provides generalized formulas for determining the sample size needed in investigating contextual effects according to the desired level of statistical power as well as width of confidence interval. These formulas are derived within a three-level random intercept model that includes one predictor/contextual variable at each level to simultaneously cover various kinds of contextual effects that researchers can show interest. The relative influences of indices included in the formulas on the standard errors of contextual effects estimates are investigated with the aim of further simplifying sample size determination procedures. In addition, simulation studies are performed to investigate finite sample behavior of calculated statistical power, showing that estimated sample sizes based on derived formulas can be both positively and negatively biased due to complex effects of unreliability of contextual variables, multicollinearity, and violation of assumption regarding the known variances. Thus, it is advisable to compare estimated sample sizes under various specifications of indices and to evaluate its potential bias, as illustrated in the example.

An internal pilot design for prospective cancer screening trials with unknown disease prevalence.

PubMed

Brinton, John T; Ringham, Brandy M; Glueck, Deborah H

2015-10-13

For studies that compare the diagnostic accuracy of two screening tests, the sample size depends on the prevalence of disease in the study population, and on the variance of the outcome. Both parameters may be unknown during the design stage, which makes finding an accurate sample size difficult. To solve this problem, we propose adapting an internal pilot design. In this adapted design, researchers will accrue some percentage of the planned sample size, then estimate both the disease prevalence and the variances of the screening tests. The updated estimates of the disease prevalence and variance are used to conduct a more accurate power and sample size calculation. We demonstrate that in large samples, the adapted internal pilot design produces no Type I inflation. For small samples (N less than 50), we introduce a novel adjustment of the critical value to control the Type I error rate. We apply the method to two proposed prospective cancer screening studies: 1) a small oral cancer screening study in individuals with Fanconi anemia and 2) a large oral cancer screening trial. Conducting an internal pilot study without adjusting the critical value can cause Type I error rate inflation in small samples, but not in large samples. An internal pilot approach usually achieves goal power and, for most studies with sample size greater than 50, requires no Type I error correction. Further, we have provided a flexible and accurate approach to bound Type I error below a goal level for studies with small sample size.

Estimation and applications of size-biased distributions in forestry

Treesearch

Jeffrey H. Gove

2003-01-01

Size-biased distributions arise naturally in several contexts in forestry and ecology. Simple power relationships (e.g. basal area and diameter at breast height) between variables are one such area of interest arising from a modelling perspective. Another, probability proportional to size PPS) sampling, is found in the most widely used methods for sampling standing or...

[A comparison of convenience sampling and purposive sampling].

PubMed

Suen, Lee-Jen Wu; Huang, Hui-Man; Lee, Hao-Hsien

2014-06-01

Convenience sampling and purposive sampling are two different sampling methods. This article first explains sampling terms such as target population, accessible population, simple random sampling, intended sample, actual sample, and statistical power analysis. These terms are then used to explain the difference between "convenience sampling" and purposive sampling." Convenience sampling is a non-probabilistic sampling technique applicable to qualitative or quantitative studies, although it is most frequently used in quantitative studies. In convenience samples, subjects more readily accessible to the researcher are more likely to be included. Thus, in quantitative studies, opportunity to participate is not equal for all qualified individuals in the target population and study results are not necessarily generalizable to this population. As in all quantitative studies, increasing the sample size increases the statistical power of the convenience sample. In contrast, purposive sampling is typically used in qualitative studies. Researchers who use this technique carefully select subjects based on study purpose with the expectation that each participant will provide unique and rich information of value to the study. As a result, members of the accessible population are not interchangeable and sample size is determined by data saturation not by statistical power analysis.

Survival distributions impact the power of randomized placebo-phase design and parallel groups randomized clinical trials.

PubMed

Abrahamyan, Lusine; Li, Chuan Silvia; Beyene, Joseph; Willan, Andrew R; Feldman, Brian M

2011-03-01

The study evaluated the power of the randomized placebo-phase design (RPPD)-a new design of randomized clinical trials (RCTs), compared with the traditional parallel groups design, assuming various response time distributions. In the RPPD, at some point, all subjects receive the experimental therapy, and the exposure to placebo is for only a short fixed period of time. For the study, an object-oriented simulation program was written in R. The power of the simulated trials was evaluated using six scenarios, where the treatment response times followed the exponential, Weibull, or lognormal distributions. The median response time was assumed to be 355 days for the placebo and 42 days for the experimental drug. Based on the simulation results, the sample size requirements to achieve the same level of power were different under different response time to treatment distributions. The scenario where the response times followed the exponential distribution had the highest sample size requirement. In most scenarios, the parallel groups RCT had higher power compared with the RPPD. The sample size requirement varies depending on the underlying hazard distribution. The RPPD requires more subjects to achieve a similar power to the parallel groups design. Copyright © 2011 Elsevier Inc. All rights reserved.

[Practical aspects regarding sample size in clinical research].

PubMed

Vega Ramos, B; Peraza Yanes, O; Herrera Correa, G; Saldívar Toraya, S

1996-01-01

The knowledge of the right sample size let us to be sure if the published results in medical papers had a suitable design and a proper conclusion according to the statistics analysis. To estimate the sample size we must consider the type I error, type II error, variance, the size of the effect, significance and power of the test. To decide what kind of mathematics formula will be used, we must define what kind of study we have, it means if its a prevalence study, a means values one or a comparative one. In this paper we explain some basic topics of statistics and we describe four simple samples of estimation of sample size.

Breaking Free of Sample Size Dogma to Perform Innovative Translational Research

PubMed Central

Bacchetti, Peter; Deeks, Steven G.; McCune, Joseph M.

2011-01-01

Innovative clinical and translational research is often delayed or prevented by reviewers’ expectations that any study performed in humans must be shown in advance to have high statistical power. This supposed requirement is not justifiable and is contradicted by the reality that increasing sample size produces diminishing marginal returns. Studies of new ideas often must start small (sometimes even with an N of 1) because of cost and feasibility concerns, and recent statistical work shows that small sample sizes for such research can produce more projected scientific value per dollar spent than larger sample sizes. Renouncing false dogma about sample size would remove a serious barrier to innovation and translation. PMID:21677197

More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter‐Mediated Clearances

PubMed Central

Burt, Howard; Abduljalil, Khaled; Neuhoff, Sibylle

2016-01-01

Abstract Rosuvastatin is a substrate of choice in clinical studies of organic anion‐transporting polypeptide (OATP)1B1‐ and OATP1B3‐associated drug interactions; thus, understanding the effect of OATP1B1 polymorphisms on the pharmacokinetics of rosuvastatin is crucial. Here, physiologically based pharmacokinetic (PBPK) modeling was coupled with a power calculation algorithm to evaluate the influence of sample size on the ability to detect an effect (80% power) of OATP1B1 phenotype on pharmacokinetics of rosuvastatin. Intestinal, hepatic, and renal transporters were mechanistically incorporated into a rosuvastatin PBPK model using permeability‐limited models for intestine, liver, and kidney, respectively, nested within a full PBPK model. Simulated plasma rosuvastatin concentrations in healthy volunteers were in agreement with previously reported clinical data. Power calculations were used to determine the influence of sample size on study power while accounting for OATP1B1 haplotype frequency and abundance in addition to its correlation with OATP1B3 abundance. It was determined that 10 poor‐transporter and 45 intermediate‐transporter individuals are required to achieve 80% power to discriminate the AUC0‐48h of rosuvastatin from that of the extensive‐transporter phenotype. This number was reduced to 7 poor‐transporter and 40 intermediate‐transporter individuals when the reported correlation between OATP1B1 and 1B3 abundance was taken into account. The current study represents the first example in which PBPK modeling in conjunction with power analysis has been used to investigate sample size in clinical studies of OATP1B1 polymorphisms. This approach highlights the influence of interindividual variability and correlation of transporter abundance on study power and should allow more informed decision making in pharmacogenomic study design. PMID:27385171

Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA

PubMed Central

Kelly, Brendan J.; Gross, Robert; Bittinger, Kyle; Sherrill-Mix, Scott; Lewis, James D.; Collman, Ronald G.; Bushman, Frederic D.; Li, Hongzhe

2015-01-01

Motivation: The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence–absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. Results: We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study. Availability and implementation: http://github.com/brendankelly/micropower. Contact: brendank@mail.med.upenn.edu or hongzhe@upenn.edu PMID:25819674

GLIMMPSE Lite: Calculating Power and Sample Size on Smartphone Devices

PubMed Central

Munjal, Aarti; Sakhadeo, Uttara R.; Muller, Keith E.; Glueck, Deborah H.; Kreidler, Sarah M.

2014-01-01

Researchers seeking to develop complex statistical applications for mobile devices face a common set of difficult implementation issues. In this work, we discuss general solutions to the design challenges. We demonstrate the utility of the solutions for a free mobile application designed to provide power and sample size calculations for univariate, one-way analysis of variance (ANOVA), GLIMMPSE Lite. Our design decisions provide a guide for other scientists seeking to produce statistical software for mobile platforms. PMID:25541688

Publication Bias in Psychology: A Diagnosis Based on the Correlation between Effect Size and Sample Size

PubMed Central

Kühberger, Anton; Fritz, Astrid; Scherndl, Thomas

2014-01-01

Background The p value obtained from a significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias. Methods We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values. Results We found a negative correlation of r = −.45 [95% CI: −.53; −.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings. Conclusion The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology. PMID:25192357

Publication bias in psychology: a diagnosis based on the correlation between effect size and sample size.

PubMed

Kühberger, Anton; Fritz, Astrid; Scherndl, Thomas

2014-01-01

The p value obtained from a significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias. We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values. We found a negative correlation of r = -.45 [95% CI: -.53; -.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings. The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology.

A single test for rejecting the null hypothesis in subgroups and in the overall sample.

PubMed

Lin, Yunzhi; Zhou, Kefei; Ganju, Jitendra

2017-01-01

In clinical trials, some patient subgroups are likely to demonstrate larger effect sizes than other subgroups. For example, the effect size, or informally the benefit with treatment, is often greater in patients with a moderate condition of a disease than in those with a mild condition. A limitation of the usual method of analysis is that it does not incorporate this ordering of effect size by patient subgroup. We propose a test statistic which supplements the conventional test by including this information and simultaneously tests the null hypothesis in pre-specified subgroups and in the overall sample. It results in more power than the conventional test when the differences in effect sizes across subgroups are at least moderately large; otherwise it loses power. The method involves combining p-values from models fit to pre-specified subgroups and the overall sample in a manner that assigns greater weight to subgroups in which a larger effect size is expected. Results are presented for randomized trials with two and three subgroups.

The Army Communications Objectives Measurement System (ACOMS): Survey Design

DTIC Science & Technology

1988-04-01

monthly basis so that the annual sample includes sufficient Hispanics to detect at the .80 power level: (1) Year-to-year changes of 3% in item...Hispanics. The requirements are listed in terms of power level and must be translated into requisite sample sizes. The requirements are expressed as the...annual samples needed to detect certain differences at the 80% power level. Differences in both directions are to be examined, so that a two-tailed

The Effect of Small Sample Size on Measurement Equivalence of Psychometric Questionnaires in MIMIC Model: A Simulation Study.

PubMed

Jamali, Jamshid; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

2017-01-01

Evaluating measurement equivalence (also known as differential item functioning (DIF)) is an important part of the process of validating psychometric questionnaires. This study aimed at evaluating the multiple indicators multiple causes (MIMIC) model for DIF detection when latent construct distribution is nonnormal and the focal group sample size is small. In this simulation-based study, Type I error rates and power of MIMIC model for detecting uniform-DIF were investigated under different combinations of reference to focal group sample size ratio, magnitude of the uniform-DIF effect, scale length, the number of response categories, and latent trait distribution. Moderate and high skewness in the latent trait distribution led to a decrease of 0.33% and 0.47% power of MIMIC model for detecting uniform-DIF, respectively. The findings indicated that, by increasing the scale length, the number of response categories and magnitude DIF improved the power of MIMIC model, by 3.47%, 4.83%, and 20.35%, respectively; it also decreased Type I error of MIMIC approach by 2.81%, 5.66%, and 0.04%, respectively. This study revealed that power of MIMIC model was at an acceptable level when latent trait distributions were skewed. However, empirical Type I error rate was slightly greater than nominal significance level. Consequently, the MIMIC was recommended for detection of uniform-DIF when latent construct distribution is nonnormal and the focal group sample size is small.

The Effect of Small Sample Size on Measurement Equivalence of Psychometric Questionnaires in MIMIC Model: A Simulation Study

PubMed Central

Jafari, Peyman

2017-01-01

Evaluating measurement equivalence (also known as differential item functioning (DIF)) is an important part of the process of validating psychometric questionnaires. This study aimed at evaluating the multiple indicators multiple causes (MIMIC) model for DIF detection when latent construct distribution is nonnormal and the focal group sample size is small. In this simulation-based study, Type I error rates and power of MIMIC model for detecting uniform-DIF were investigated under different combinations of reference to focal group sample size ratio, magnitude of the uniform-DIF effect, scale length, the number of response categories, and latent trait distribution. Moderate and high skewness in the latent trait distribution led to a decrease of 0.33% and 0.47% power of MIMIC model for detecting uniform-DIF, respectively. The findings indicated that, by increasing the scale length, the number of response categories and magnitude DIF improved the power of MIMIC model, by 3.47%, 4.83%, and 20.35%, respectively; it also decreased Type I error of MIMIC approach by 2.81%, 5.66%, and 0.04%, respectively. This study revealed that power of MIMIC model was at an acceptable level when latent trait distributions were skewed. However, empirical Type I error rate was slightly greater than nominal significance level. Consequently, the MIMIC was recommended for detection of uniform-DIF when latent construct distribution is nonnormal and the focal group sample size is small. PMID:28713828

Kidney function endpoints in kidney transplant trials: a struggle for power.

PubMed

Ibrahim, A; Garg, A X; Knoll, G A; Akbari, A; White, C A

2013-03-01

Kidney function endpoints are commonly used in randomized controlled trials (RCTs) in kidney transplantation (KTx). We conducted this study to estimate the proportion of ongoing RCTs with kidney function endpoints in KTx where the proposed sample size is large enough to detect meaningful differences in glomerular filtration rate (GFR) with adequate statistical power. RCTs were retrieved using the key word "kidney transplantation" from the National Institute of Health online clinical trial registry. Included trials had at least one measure of kidney function tracked for at least 1 month after transplant. We determined the proportion of two-arm parallel trials that had sufficient sample sizes to detect a minimum 5, 7.5 and 10 mL/min difference in GFR between arms. Fifty RCTs met inclusion criteria. Only 7% of the trials were above a sample size of 562, the number needed to detect a minimum 5 mL/min difference between the groups should one exist (assumptions: α = 0.05; power = 80%, 10% loss to follow-up, common standard deviation of 20 mL/min). The result increased modestly to 36% of trials when a minimum 10 mL/min difference was considered. Only a minority of ongoing trials have adequate statistical power to detect between-group differences in kidney function using conventional sample size estimating parameters. For this reason, some potentially effective interventions which ultimately could benefit patients may be abandoned from future assessment. © Copyright 2013 The American Society of Transplantation and the American Society of Transplant Surgeons.

Cost-efficient designs for three-arm trials with treatment delivered by health professionals: Sample sizes for a combination of nested and crossed designs

PubMed Central

Moerbeek, Mirjam

2018-01-01

Background This article studies the design of trials that compare three treatment conditions that are delivered by two types of health professionals. The one type of health professional delivers one treatment, and the other type delivers two treatments, hence, this design is a combination of a nested and crossed design. As each health professional treats multiple patients, the data have a nested structure. This nested structure has thus far been ignored in the design of such trials, which may result in an underestimate of the required sample size. In the design stage, the sample sizes should be determined such that a desired power is achieved for each of the three pairwise comparisons, while keeping costs or sample size at a minimum. Methods The statistical model that relates outcome to treatment condition and explicitly takes the nested data structure into account is presented. Mathematical expressions that relate sample size to power are derived for each of the three pairwise comparisons on the basis of this model. The cost-efficient design achieves sufficient power for each pairwise comparison at lowest costs. Alternatively, one may minimize the total number of patients. The sample sizes are found numerically and an Internet application is available for this purpose. The design is also compared to a nested design in which each health professional delivers just one treatment. Results Mathematical expressions show that this design is more efficient than the nested design. For each pairwise comparison, power increases with the number of health professionals and the number of patients per health professional. The methodology of finding a cost-efficient design is illustrated using a trial that compares treatments for social phobia. The optimal sample sizes reflect the costs for training and supervising psychologists and psychiatrists, and the patient-level costs in the three treatment conditions. Conclusion This article provides the methodology for designing trials that compare three treatment conditions while taking the nesting of patients within health professionals into account. As such, it helps to avoid underpowered trials. To use the methodology, a priori estimates of the total outcome variances and intraclass correlation coefficients must be obtained from experts’ opinions or findings in the literature. PMID:29316807

Linear Combinations of Multiple Outcome Measures to Improve the Power of Efficacy Analysis ---Application to Clinical Trials on Early Stage Alzheimer Disease

PubMed Central

Xiong, Chengjie; Luo, Jingqin; Morris, John C; Bateman, Randall

2018-01-01

Modern clinical trials on Alzheimer disease (AD) focus on the early symptomatic stage or even the preclinical stage. Subtle disease progression at the early stages, however, poses a major challenge in designing such clinical trials. We propose a multivariate mixed model on repeated measures to model the disease progression over time on multiple efficacy outcomes, and derive the optimum weights to combine multiple outcome measures by minimizing the sample sizes to adequately power the clinical trials. A cross-validation simulation study is conducted to assess the accuracy for the estimated weights as well as the improvement in reducing the sample sizes for such trials. The proposed methodology is applied to the multiple cognitive tests from the ongoing observational study of the Dominantly Inherited Alzheimer Network (DIAN) to power future clinical trials in the DIAN with a cognitive endpoint. Our results show that the optimum weights to combine multiple outcome measures can be accurately estimated, and that compared to the individual outcomes, the combined efficacy outcome with these weights significantly reduces the sample size required to adequately power clinical trials. When applied to the clinical trial in the DIAN, the estimated linear combination of six cognitive tests can adequately power the clinical trial. PMID:29546251

Power of tests for comparing trend curves with application to national immunization survey (NIS).

PubMed

Zhao, Zhen

2011-02-28

To develop statistical tests for comparing trend curves of study outcomes between two socio-demographic strata across consecutive time points, and compare statistical power of the proposed tests under different trend curves data, three statistical tests were proposed. For large sample size with independent normal assumption among strata and across consecutive time points, the Z and Chi-square test statistics were developed, which are functions of outcome estimates and the standard errors at each of the study time points for the two strata. For small sample size with independent normal assumption, the F-test statistic was generated, which is a function of sample size of the two strata and estimated parameters across study period. If two trend curves are approximately parallel, the power of Z-test is consistently higher than that of both Chi-square and F-test. If two trend curves cross at low interaction, the power of Z-test is higher than or equal to the power of both Chi-square and F-test; however, at high interaction, the powers of Chi-square and F-test are higher than that of Z-test. The measurement of interaction of two trend curves was defined. These tests were applied to the comparison of trend curves of vaccination coverage estimates of standard vaccine series with National Immunization Survey (NIS) 2000-2007 data. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical power calculations for mixed pharmacokinetic study designs using a population approach.

PubMed

Kloprogge, Frank; Simpson, Julie A; Day, Nicholas P J; White, Nicholas J; Tarning, Joel

2014-09-01

Simultaneous modelling of dense and sparse pharmacokinetic data is possible with a population approach. To determine the number of individuals required to detect the effect of a covariate, simulation-based power calculation methodologies can be employed. The Monte Carlo Mapped Power method (a simulation-based power calculation methodology using the likelihood ratio test) was extended in the current study to perform sample size calculations for mixed pharmacokinetic studies (i.e. both sparse and dense data collection). A workflow guiding an easy and straightforward pharmacokinetic study design, considering also the cost-effectiveness of alternative study designs, was used in this analysis. Initially, data were simulated for a hypothetical drug and then for the anti-malarial drug, dihydroartemisinin. Two datasets (sampling design A: dense; sampling design B: sparse) were simulated using a pharmacokinetic model that included a binary covariate effect and subsequently re-estimated using (1) the same model and (2) a model not including the covariate effect in NONMEM 7.2. Power calculations were performed for varying numbers of patients with sampling designs A and B. Study designs with statistical power >80% were selected and further evaluated for cost-effectiveness. The simulation studies of the hypothetical drug and the anti-malarial drug dihydroartemisinin demonstrated that the simulation-based power calculation methodology, based on the Monte Carlo Mapped Power method, can be utilised to evaluate and determine the sample size of mixed (part sparsely and part densely sampled) study designs. The developed method can contribute to the design of robust and efficient pharmacokinetic studies.

Distribution of the two-sample t-test statistic following blinded sample size re-estimation.

PubMed

Lu, Kaifeng

2016-05-01

We consider the blinded sample size re-estimation based on the simple one-sample variance estimator at an interim analysis. We characterize the exact distribution of the standard two-sample t-test statistic at the final analysis. We describe a simulation algorithm for the evaluation of the probability of rejecting the null hypothesis at given treatment effect. We compare the blinded sample size re-estimation method with two unblinded methods with respect to the empirical type I error, the empirical power, and the empirical distribution of the standard deviation estimator and final sample size. We characterize the type I error inflation across the range of standardized non-inferiority margin for non-inferiority trials, and derive the adjusted significance level to ensure type I error control for given sample size of the internal pilot study. We show that the adjusted significance level increases as the sample size of the internal pilot study increases. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Quantal Response: Estimation and Inference

DTIC Science & Technology

2014-09-01

considered. The CI-based test is just another way of looking at the Wald test. A small-sample simulation illustrates aberrant behavior of the Wald/CI...asymptotic power computation (Eq. 36) exhibits this behavior but not to such an extent as the simulated small-sample power. Sample size is n = 11 and...as |m1−m0| increases, but the power of the Wald test actually decreases for large |m1−m0| and eventually π → α . This type of behavior was reported as

A comparative appraisal of two equivalence tests for multiple standardized effects.

PubMed

Shieh, Gwowen

2016-04-01

Equivalence testing is recommended as a better alternative to the traditional difference-based methods for demonstrating the comparability of two or more treatment effects. Although equivalent tests of two groups are widely discussed, the natural extensions for assessing equivalence between several groups have not been well examined. This article provides a detailed and schematic comparison of the ANOVA F and the studentized range tests for evaluating the comparability of several standardized effects. Power and sample size appraisals of the two grossly distinct approaches are conducted in terms of a constraint on the range of the standardized means when the standard deviation of the standardized means is fixed. Although neither method is uniformly more powerful, the studentized range test has a clear advantage in sample size requirements necessary to achieve a given power when the underlying effect configurations are close to the priori minimum difference for determining equivalence. For actual application of equivalence tests and advance planning of equivalence studies, both SAS and R computer codes are available as supplementary files to implement the calculations of critical values, p-values, power levels, and sample sizes. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Variable criteria sequential stopping rule: Validity and power with repeated measures ANOVA, multiple correlation, MANOVA and relation to Chi-square distribution.

PubMed

Fitts, Douglas A

2017-09-21

The variable criteria sequential stopping rule (vcSSR) is an efficient way to add sample size to planned ANOVA tests while holding the observed rate of Type I errors, α o , constant. The only difference from regular null hypothesis testing is that criteria for stopping the experiment are obtained from a table based on the desired power, rate of Type I errors, and beginning sample size. The vcSSR was developed using between-subjects ANOVAs, but it should work with p values from any type of F test. In the present study, the α o remained constant at the nominal level when using the previously published table of criteria with repeated measures designs with various numbers of treatments per subject, Type I error rates, values of ρ, and four different sample size models. New power curves allow researchers to select the optimal sample size model for a repeated measures experiment. The criteria held α o constant either when used with a multiple correlation that varied the sample size model and the number of predictor variables, or when used with MANOVA with multiple groups and two levels of a within-subject variable at various levels of ρ. Although not recommended for use with χ 2 tests such as the Friedman rank ANOVA test, the vcSSR produces predictable results based on the relation between F and χ 2 . Together, the data confirm the view that the vcSSR can be used to control Type I errors during sequential sampling with any t- or F-statistic rather than being restricted to certain ANOVA designs.

Group-sequential three-arm noninferiority clinical trial designs

PubMed Central

Ochiai, Toshimitsu; Hamasaki, Toshimitsu; Evans, Scott R.; Asakura, Koko; Ohno, Yuko

2016-01-01

We discuss group-sequential three-arm noninferiority clinical trial designs that include active and placebo controls for evaluating both assay sensitivity and noninferiority. We extend two existing approaches, the fixed margin and fraction approaches, into a group-sequential setting with two decision-making frameworks. We investigate the operating characteristics including power, Type I error rate, maximum and expected sample sizes, as design factors vary. In addition, we discuss sample size recalculation and its’ impact on the power and Type I error rate via a simulation study. PMID:26892481

Disease-Concordant Twins Empower Genetic Association Studies.

PubMed

Tan, Qihua; Li, Weilong; Vandin, Fabio

2017-01-01

Genome-wide association studies with moderate sample sizes are underpowered, especially when testing SNP alleles with low allele counts, a situation that may lead to high frequency of false-positive results and lack of replication in independent studies. Related individuals, such as twin pairs concordant for a disease, should confer increased power in genetic association analysis because of their genetic relatedness. We conducted a computer simulation study to explore the power advantage of the disease-concordant twin design, which uses singletons from disease-concordant twin pairs as cases and ordinary healthy samples as controls. We examined the power gain of the twin-based design for various scenarios (i.e., cases from monozygotic and dizygotic twin pairs concordant for a disease) and compared the power with the ordinary case-control design with cases collected from the unrelated patient population. Simulation was done by assigning various allele frequencies and allelic relative risks for different mode of genetic inheritance. In general, for achieving a power estimate of 80%, the sample sizes needed for dizygotic and monozygotic twin cases were one half and one fourth of the sample size of an ordinary case-control design, with variations depending on genetic mode. Importantly, the enriched power for dizygotic twins also applies to disease-concordant sibling pairs, which largely extends the application of the concordant twin design. Overall, our simulation revealed a high value of disease-concordant twins in genetic association studies and encourages the use of genetically related individuals for highly efficiently identifying both common and rare genetic variants underlying human complex diseases without increasing laboratory cost. © 2016 John Wiley & Sons Ltd/University College London.

A fiber optic sensor for noncontact measurement of shaft speed, torque, and power

NASA Technical Reports Server (NTRS)

Madzsar, George C.

1990-01-01

A fiber optic sensor which enables noncontact measurement of the speed, torque and power of a rotating shaft was fabricated and tested. The sensor provides a direct measurement of shaft rotational speed and shaft angular twist, from which torque and power can be determined. Angles of twist between 0.005 and 10 degrees were measured. Sensor resolution is limited by the sampling rate of the analog to digital converter, while accuracy is dependent on the spot size of the focused beam on the shaft. Increasing the sampling rate improves measurement resolution, and decreasing the focused spot size increases accuracy. Digital processing allows for enhancement of an electronically or optically degraded signal.

A fiber optic sensor for noncontact measurement of shaft speed, torque and power

NASA Technical Reports Server (NTRS)

Madzsar, George C.

1990-01-01

A fiber optic sensor which enables noncontact measurement of the speed, torque and power of a rotating shaft was fabricated and tested. The sensor provides a direct measurement of shaft rotational speed and shaft angular twist, from which torque and power can be determined. Angles of twist between 0.005 and 10 degrees were measured. Sensor resolution is limited by the sampling rate of the analog to digital converter, while accuracy is dependent on the spot size of the focused beam on the shaft. Increasing the sampling rate improves measurement resolution, and decreasing the focused spot size increases accuracy. Digital processing allows for enhancement of an electronically or optically degraded signal.

Effects of grain size on the properties of bulk nanocrystalline Co-Ni alloys

NASA Astrophysics Data System (ADS)

Qiao, Gui-Ying; Xiao, Fu-Ren

2017-08-01

Bulk nanocrystalline Co78Ni22 alloys with grain size ranging from 5 nm to 35 nm were prepared by high-speed jet electrodeposition (HSJED) and annealing. Microhardness and magnetic properties of these alloys were investigated by microhardness tester and vibrating sample magnetometer. Effects of grain size on these characteristics were also discussed. Results show that the microhardness of nanocrystalline Co78Ni22 alloys increases following a d -1/2-power law with decreasing grain size d. This phenomenon fits the Hall-Petch law when the grain size ranges from 5 nm to 35 nm. However, coercivity H c increases following a 1/d-power law with increasing grain size when the grain size ranges from 5 nm to 15.9 nm. Coercivity H c decreases again for grain sizes above 16.6 nm according to the d 6-power law.

Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature.

PubMed

Szucs, Denes; Ioannidis, John P A

2017-03-01

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64-1.46) for nominally statistically significant results and D = 0.24 (0.11-0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

On the analysis of very small samples of Gaussian repeated measurements: an alternative approach.

PubMed

Westgate, Philip M; Burchett, Woodrow W

2017-03-15

The analysis of very small samples of Gaussian repeated measurements can be challenging. First, due to a very small number of independent subjects contributing outcomes over time, statistical power can be quite small. Second, nuisance covariance parameters must be appropriately accounted for in the analysis in order to maintain the nominal test size. However, available statistical strategies that ensure valid statistical inference may lack power, whereas more powerful methods may have the potential for inflated test sizes. Therefore, we explore an alternative approach to the analysis of very small samples of Gaussian repeated measurements, with the goal of maintaining valid inference while also improving statistical power relative to other valid methods. This approach uses generalized estimating equations with a bias-corrected empirical covariance matrix that accounts for all small-sample aspects of nuisance correlation parameter estimation in order to maintain valid inference. Furthermore, the approach utilizes correlation selection strategies with the goal of choosing the working structure that will result in the greatest power. In our study, we show that when accurate modeling of the nuisance correlation structure impacts the efficiency of regression parameter estimation, this method can improve power relative to existing methods that yield valid inference. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Large sample area and size are needed for forest soil seed bank studies to ensure low discrepancy with standing vegetation.

PubMed

Shen, You-xin; Liu, Wei-li; Li, Yu-hui; Guan, Hui-lin

2014-01-01

A large number of small-sized samples invariably shows that woody species are absent from forest soil seed banks, leading to a large discrepancy with the seedling bank on the forest floor. We ask: 1) Does this conventional sampling strategy limit the detection of seeds of woody species? 2) Are large sample areas and sample sizes needed for higher recovery of seeds of woody species? We collected 100 samples that were 10 cm (length) × 10 cm (width) × 10 cm (depth), referred to as larger number of small-sized samples (LNSS) in a 1 ha forest plot, and placed them to germinate in a greenhouse, and collected 30 samples that were 1 m × 1 m × 10 cm, referred to as small number of large-sized samples (SNLS) and placed them (10 each) in a nearby secondary forest, shrub land and grass land. Only 15.7% of woody plant species of the forest stand were detected by the 100 LNSS, contrasting with 22.9%, 37.3% and 20.5% woody plant species being detected by SNLS in the secondary forest, shrub land and grassland, respectively. The increased number of species vs. sampled areas confirmed power-law relationships for forest stand, the LNSS and SNLS at all three recipient sites. Our results, although based on one forest, indicate that conventional LNSS did not yield a high percentage of detection for woody species, but SNLS strategy yielded a higher percentage of detection for woody species in the seed bank if samples were exposed to a better field germination environment. A 4 m2 minimum sample area derived from power equations is larger than the sampled area in most studies in the literature. Increased sample size also is needed to obtain an increased sample area if the number of samples is to remain relatively low.

Demonstration of Multi- and Single-Reader Sample Size Program for Diagnostic Studies software.

PubMed

Hillis, Stephen L; Schartz, Kevin M

2015-02-01

The recently released software Multi- and Single-Reader Sample Size Sample Size Program for Diagnostic Studies , written by Kevin Schartz and Stephen Hillis, performs sample size computations for diagnostic reader-performance studies. The program computes the sample size needed to detect a specified difference in a reader performance measure between two modalities, when using the analysis methods initially proposed by Dorfman, Berbaum, and Metz (DBM) and Obuchowski and Rockette (OR), and later unified and improved by Hillis and colleagues. A commonly used reader performance measure is the area under the receiver-operating-characteristic curve. The program can be used with typical common reader-performance measures which can be estimated parametrically or nonparametrically. The program has an easy-to-use step-by-step intuitive interface that walks the user through the entry of the needed information. Features of the software include the following: (1) choice of several study designs; (2) choice of inputs obtained from either OR or DBM analyses; (3) choice of three different inference situations: both readers and cases random, readers fixed and cases random, and readers random and cases fixed; (4) choice of two types of hypotheses: equivalence or noninferiority; (6) choice of two output formats: power for specified case and reader sample sizes, or a listing of case-reader combinations that provide a specified power; (7) choice of single or multi-reader analyses; and (8) functionality in Windows, Mac OS, and Linux.

Bon-EV: an improved multiple testing procedure for controlling false discovery rates.

PubMed

Li, Dongmei; Xie, Zidian; Zand, Martin; Fogg, Thomas; Dye, Timothy

2017-01-03

Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg's and Storey's q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey's q-value procedure has higher power and lower stability than Benjamini-Hochberg's procedure. To improve upon the stability of Storey's q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni's approach. Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey's q-value procedure and also result in better FDR control and higher stability than Storey's q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey's q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey's q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg's procedure. For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey's q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

Using meta-analysis to inform the design of subsequent studies of diagnostic test accuracy.

PubMed

Hinchliffe, Sally R; Crowther, Michael J; Phillips, Robert S; Sutton, Alex J

2013-06-01

An individual diagnostic accuracy study rarely provides enough information to make conclusive recommendations about the accuracy of a diagnostic test; particularly when the study is small. Meta-analysis methods provide a way of combining information from multiple studies, reducing uncertainty in the result and hopefully providing substantial evidence to underpin reliable clinical decision-making. Very few investigators consider any sample size calculations when designing a new diagnostic accuracy study. However, it is important to consider the number of subjects in a new study in order to achieve a precise measure of accuracy. Sutton et al. have suggested previously that when designing a new therapeutic trial, it could be more beneficial to consider the power of the updated meta-analysis including the new trial rather than of the new trial itself. The methodology involves simulating new studies for a range of sample sizes and estimating the power of the updated meta-analysis with each new study added. Plotting the power values against the range of sample sizes allows the clinician to make an informed decision about the sample size of a new trial. This paper extends this approach from the trial setting and applies it to diagnostic accuracy studies. Several meta-analytic models are considered including bivariate random effects meta-analysis that models the correlation between sensitivity and specificity. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Monitoring the impact of Bt maize on butterflies in the field: estimation of required sample sizes.

PubMed

Lang, Andreas

2004-01-01

The monitoring of genetically modified organisms (GMOs) after deliberate release is important in order to assess and evaluate possible environmental effects. Concerns have been raised that the transgenic crop, Bt maize, may affect butterflies occurring in field margins. Therefore, a monitoring of butterflies was suggested accompanying the commercial cultivation of Bt maize. In this study, baseline data on the butterfly species and their abundance in maize field margins is presented together with implications for butterfly monitoring. The study was conducted in Bavaria, South Germany, between 2000-2002. A total of 33 butterfly species was recorded in field margins. A small number of species dominated the community, and butterflies observed were mostly common species. Observation duration was the most important factor influencing the monitoring results. Field margin size affected the butterfly abundance, and habitat diversity had a tendency to influence species richness. Sample size and statistical power analyses indicated that a sample size in the range of 75 to 150 field margins for treatment (transgenic maize) and control (conventional maize) would detect (power of 80%) effects larger than 15% in species richness and the butterfly abundance pooled across species. However, a much higher number of field margins must be sampled in order to achieve a higher statistical power, to detect smaller effects, and to monitor single butterfly species.

Phase II Trials for Heterogeneous Patient Populations with a Time-to-Event Endpoint.

PubMed

Jung, Sin-Ho

2017-07-01

In this paper, we consider a single-arm phase II trial with a time-to-event end-point. We assume that the study population has multiple subpopulations with different prognosis, but the study treatment is expected to be similarly efficacious across the subpopulations. We review a stratified one-sample log-rank test and present its sample size calculation method under some practical design settings. Our sample size method requires specification of the prevalence of subpopulations. We observe that the power of the resulting sample size is not very sensitive to misspecification of the prevalence.

An analysis of adaptive design variations on the sequential parallel comparison design for clinical trials.

PubMed

Mi, Michael Y; Betensky, Rebecca A

2013-04-01

Currently, a growing placebo response rate has been observed in clinical trials for antidepressant drugs, a phenomenon that has made it increasingly difficult to demonstrate efficacy. The sequential parallel comparison design (SPCD) is a clinical trial design that was proposed to address this issue. The SPCD theoretically has the potential to reduce the sample-size requirement for a clinical trial and to simultaneously enrich the study population to be less responsive to the placebo. Because the basic SPCD already reduces the placebo response by removing placebo responders between the first and second phases of a trial, the purpose of this study was to examine whether we can further improve the efficiency of the basic SPCD and whether we can do so when the projected underlying drug and placebo response rates differ considerably from the actual ones. Three adaptive designs that used interim analyses to readjust the length of study duration for individual patients were tested to reduce the sample-size requirement or increase the statistical power of the SPCD. Various simulations of clinical trials using the SPCD with interim analyses were conducted to test these designs through calculations of empirical power. From the simulations, we found that the adaptive designs can recover unnecessary resources spent in the traditional SPCD trial format with overestimated initial sample sizes and provide moderate gains in power. Under the first design, results showed up to a 25% reduction in person-days, with most power losses below 5%. In the second design, results showed up to a 8% reduction in person-days with negligible loss of power. In the third design using sample-size re-estimation, up to 25% power was recovered from underestimated sample-size scenarios. Given the numerous possible test parameters that could have been chosen for the simulations, the study's results are limited to situations described by the parameters that were used and may not generalize to all possible scenarios. Furthermore, dropout of patients is not considered in this study. It is possible to make an already complex design such as the SPCD adaptive, and thus more efficient, potentially overcoming the problem of placebo response at lower cost. Ultimately, such a design may expedite the approval of future effective treatments.

An analysis of adaptive design variations on the sequential parallel comparison design for clinical trials

PubMed Central

Mi, Michael Y.; Betensky, Rebecca A.

2013-01-01

Background Currently, a growing placebo response rate has been observed in clinical trials for antidepressant drugs, a phenomenon that has made it increasingly difficult to demonstrate efficacy. The sequential parallel comparison design (SPCD) is a clinical trial design that was proposed to address this issue. The SPCD theoretically has the potential to reduce the sample size requirement for a clinical trial and to simultaneously enrich the study population to be less responsive to the placebo. Purpose Because the basic SPCD design already reduces the placebo response by removing placebo responders between the first and second phases of a trial, the purpose of this study was to examine whether we can further improve the efficiency of the basic SPCD and if we can do so when the projected underlying drug and placebo response rates differ considerably from the actual ones. Methods Three adaptive designs that used interim analyses to readjust the length of study duration for individual patients were tested to reduce the sample size requirement or increase the statistical power of the SPCD. Various simulations of clinical trials using the SPCD with interim analyses were conducted to test these designs through calculations of empirical power. Results From the simulations, we found that the adaptive designs can recover unnecessary resources spent in the traditional SPCD trial format with overestimated initial sample sizes and provide moderate gains in power. Under the first design, results showed up to a 25% reduction in person-days, with most power losses below 5%. In the second design, results showed up to a 8% reduction in person-days with negligible loss of power. In the third design using sample size re-estimation, up to 25% power was recovered from underestimated sample size scenarios. Limitations Given the numerous possible test parameters that could have been chosen for the simulations, the study’s results are limited to situations described by the parameters that were used, and may not generalize to all possible scenarios. Furthermore, drop-out of patients is not considered in this study. Conclusions It is possible to make an already complex design such as the SPCD adaptive, and thus more efficient, potentially overcoming the problem of placebo response at lower cost. Ultimately, such a design may expedite the approval of future effective treatments. PMID:23283576

Design considerations for case series models with exposure onset measurement error.

PubMed

Mohammed, Sandra M; Dalrymple, Lorien S; Sentürk, Damla; Nguyen, Danh V

2013-02-28

The case series model allows for estimation of the relative incidence of events, such as cardiovascular events, within a pre-specified time window after an exposure, such as an infection. The method requires only cases (individuals with events) and controls for all fixed/time-invariant confounders. The measurement error case series model extends the original case series model to handle imperfect data, where the timing of an infection (exposure) is not known precisely. In this work, we propose a method for power/sample size determination for the measurement error case series model. Extensive simulation studies are used to assess the accuracy of the proposed sample size formulas. We also examine the magnitude of the relative loss of power due to exposure onset measurement error, compared with the ideal situation where the time of exposure is measured precisely. To facilitate the design of case series studies, we provide publicly available web-based tools for determining power/sample size for both the measurement error case series model as well as the standard case series model. Copyright © 2012 John Wiley & Sons, Ltd.

Sample size determination for GEE analyses of stepped wedge cluster randomized trials.

PubMed

Li, Fan; Turner, Elizabeth L; Preisser, John S

2018-06-19

In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator. © 2018, The International Biometric Society.

Improving the analysis of composite endpoints in rare disease trials.

PubMed

McMenamin, Martina; Berglind, Anna; Wason, James M S

2018-05-22

Composite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections. We re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth's adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison. For the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6-8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17-18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power. The augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them.

A note on sample size calculation for mean comparisons based on noncentral t-statistics.

PubMed

Chow, Shein-Chung; Shao, Jun; Wang, Hansheng

2002-11-01

One-sample and two-sample t-tests are commonly used in analyzing data from clinical trials in comparing mean responses from two drug products. During the planning stage of a clinical study, a crucial step is the sample size calculation, i.e., the determination of the number of subjects (patients) needed to achieve a desired power (e.g., 80%) for detecting a clinically meaningful difference in the mean drug responses. Based on noncentral t-distributions, we derive some sample size calculation formulas for testing equality, testing therapeutic noninferiority/superiority, and testing therapeutic equivalence, under the popular one-sample design, two-sample parallel design, and two-sample crossover design. Useful tables are constructed and some examples are given for illustration.

Effect of capping and particle size on Raman laser-induced degradation of {gamma}-Fe{sub 2}O{sub 3} nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Varadwaj, K.S.K.; Panigrahi, M.K.; Ghose, J.

2004-11-01

Diol capped {gamma}-Fe{sub 2}O{sub 3} nanoparticles are prepared from ferric nitrate by refluxing in 1,4-butanediol (9.5nm) and 1,5-pentanediol (15nm) and uncapped particles are prepared by refluxing in 1,2-propanediol followed by sintering the alkoxide formed. X-ray diffraction (XRD) shows that all the samples have the spinel phase. Raman spectroscopy shows that the samples prepared in 1,4-butanediol and 1,5-pentanediol and 1,2-propanediol (sintered at 573 and 673K) are {gamma}-Fe{sub 2}O{sub 3} and the 773K-sintered sample is Fe{sub 3}O{sub 4}. Raman laser studies carried out at various laser powers show that all the samples undergo laser-induced degradation to {alpha}-Fe{sub 2}O{sub 3} at higher lasermore » power. The capped samples are however, found more stable to degradation than the uncapped samples. The stability of {gamma}-Fe{sub 2}O{sub 3} sample with large particle size (15.4nm) is more than the sample with small particle size (10.2nm). Fe{sub 3}O{sub 4} having a particle size of 48nm is however less stable than the smaller {gamma}-Fe{sub 2}O{sub 3} nanoparticles.« less

Measurement Error and Equating Error in Power Analysis

ERIC Educational Resources Information Center

Phillips, Gary W.; Jiang, Tao

2016-01-01

Power analysis is a fundamental prerequisite for conducting scientific research. Without power analysis the researcher has no way of knowing whether the sample size is large enough to detect the effect he or she is looking for. This paper demonstrates how psychometric factors such as measurement error and equating error affect the power of…

Introduction to Sample Size Choice for Confidence Intervals Based on "t" Statistics

ERIC Educational Resources Information Center

Liu, Xiaofeng Steven; Loudermilk, Brandon; Simpson, Thomas

2014-01-01

Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis…

Sample Size Calculation for Estimating or Testing a Nonzero Squared Multiple Correlation Coefficient

ERIC Educational Resources Information Center

Krishnamoorthy, K.; Xia, Yanping

2008-01-01

The problems of hypothesis testing and interval estimation of the squared multiple correlation coefficient of a multivariate normal distribution are considered. It is shown that available one-sided tests are uniformly most powerful, and the one-sided confidence intervals are uniformly most accurate. An exact method of calculating sample size to…

The special case of the 2 × 2 table: asymptotic unconditional McNemar test can be used to estimate sample size even for analysis based on GEE.

PubMed

Borkhoff, Cornelia M; Johnston, Patrick R; Stephens, Derek; Atenafu, Eshetu

2015-07-01

Aligning the method used to estimate sample size with the planned analytic method ensures the sample size needed to achieve the planned power. When using generalized estimating equations (GEE) to analyze a paired binary primary outcome with no covariates, many use an exact McNemar test to calculate sample size. We reviewed the approaches to sample size estimation for paired binary data and compared the sample size estimates on the same numerical examples. We used the hypothesized sample proportions for the 2 × 2 table to calculate the correlation between the marginal proportions to estimate sample size based on GEE. We solved the inside proportions based on the correlation and the marginal proportions to estimate sample size based on exact McNemar, asymptotic unconditional McNemar, and asymptotic conditional McNemar. The asymptotic unconditional McNemar test is a good approximation of GEE method by Pan. The exact McNemar is too conservative and yields unnecessarily large sample size estimates than all other methods. In the special case of a 2 × 2 table, even when a GEE approach to binary logistic regression is the planned analytic method, the asymptotic unconditional McNemar test can be used to estimate sample size. We do not recommend using an exact McNemar test. Copyright © 2015 Elsevier Inc. All rights reserved.

Spacecraft mass trade-offs versus radio-frequency power and antenna size at 8 GHz and 32 GHz

NASA Technical Reports Server (NTRS)

Gilchriest, C. E.

1987-01-01

The purpose of this analysis is to help determine the relative merits of 32 GHz over 8 GHz for future deep space communications. This analysis is only a piece of the overall analysis and only considers the downlink communication mass, power, and size comparisons for 8 and 32 GHz. Both parabolic antennas and flat-plate arrays are considered. The Mars Sample Return mission is considered in some detail as an example of the tradeoffs involved; for this mission the mass, power, and size show a definite advantage of roughly 2:1 in using the 32 GHz over 8 GHz.

Multilevel Factorial Experiments for Developing Behavioral Interventions: Power, Sample Size, and Resource Considerations†

PubMed Central

Dziak, John J.; Nahum-Shani, Inbal; Collins, Linda M.

2012-01-01

Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions, by helping investigators to screen several candidate intervention components simultaneously and decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or employees within organizations). In this article we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements such as the number of clusters, the number of lower-level units, and the intraclass correlation affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes, because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation. PMID:22309956

Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations.

PubMed

Dziak, John J; Nahum-Shani, Inbal; Collins, Linda M

2012-06-01

Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or when employees are nested within organizations). In this article, we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel, multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements-such as the number of clusters, the number of lower-level units, and the intraclass correlation-affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation. (c) 2012 APA, all rights reserved

Samples in applied psychology: over a decade of research in review.

PubMed

Shen, Winny; Kiger, Thomas B; Davies, Stacy E; Rasch, Rena L; Simon, Kara M; Ones, Deniz S

2011-09-01

This study examines sample characteristics of articles published in Journal of Applied Psychology (JAP) from 1995 to 2008. At the individual level, the overall median sample size over the period examined was approximately 173, which is generally adequate for detecting the average magnitude of effects of primary interest to researchers who publish in JAP. Samples using higher units of analyses (e.g., teams, departments/work units, and organizations) had lower median sample sizes (Mdn ≈ 65), yet were arguably robust given typical multilevel design choices of JAP authors despite the practical constraints of collecting data at higher units of analysis. A substantial proportion of studies used student samples (~40%); surprisingly, median sample sizes for student samples were smaller than working adult samples. Samples were more commonly occupationally homogeneous (~70%) than occupationally heterogeneous. U.S. and English-speaking participants made up the vast majority of samples, whereas Middle Eastern, African, and Latin American samples were largely unrepresented. On the basis of study results, recommendations are provided for authors, editors, and readers, which converge on 3 themes: (a) appropriateness and match between sample characteristics and research questions, (b) careful consideration of statistical power, and (c) the increased popularity of quantitative synthesis. Implications are discussed in terms of theory building, generalizability of research findings, and statistical power to detect effects. PsycINFO Database Record (c) 2011 APA, all rights reserved

Short hypervariable microhaplotypes: A novel set of very short high discriminating power loci without stutter artefacts.

PubMed

van der Gaag, Kristiaan J; de Leeuw, Rick H; Laros, Jeroen F J; den Dunnen, Johan T; de Knijff, Peter

2018-07-01

Since two decades, short tandem repeats (STRs) are the preferred markers for human identification, routinely analysed by fragment length analysis. Here we present a novel set of short hypervariable autosomal microhaplotypes (MH) that have four or more SNPs in a span of less than 70 nucleotides (nt). These MHs display a discriminating power approaching that of STRs and provide a powerful alternative for the analysis;1;is of forensic samples that are problematic when the STR fragment size range exceeds the integrity range of severely degraded DNA or when multiple donors contribute to an evidentiary stain and STR stutter artefacts complicate profile interpretation. MH typing was developed using the power of massively parallel sequencing (MPS) enabling new powerful, fast and efficient SNP-based approaches. MH candidates were obtained from queries in data of the 1000 Genomes, and Genome of the Netherlands (GoNL) projects. Wet-lab analysis of 276 globally dispersed samples and 97 samples of nine large CEPH families assisted locus selection and corroboration of informative value. We infer that MHs represent an alternative marker type with good discriminating power per locus (allowing the use of a limited number of loci), small amplicon sizes and absence of stutter artefacts that can be especially helpful when unbalanced mixed samples are submitted for human identification. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature

PubMed Central

Szucs, Denes; Ioannidis, John P. A.

2017-01-01

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64–1.46) for nominally statistically significant results and D = 0.24 (0.11–0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience. PMID:28253258

Design and analysis of three-arm trials with negative binomially distributed endpoints.

PubMed

Mütze, Tobias; Munk, Axel; Friede, Tim

2016-02-20

A three-arm clinical trial design with an experimental treatment, an active control, and a placebo control, commonly referred to as the gold standard design, enables testing of non-inferiority or superiority of the experimental treatment compared with the active control. In this paper, we propose methods for designing and analyzing three-arm trials with negative binomially distributed endpoints. In particular, we develop a Wald-type test with a restricted maximum-likelihood variance estimator for testing non-inferiority or superiority. For this test, sample size and power formulas as well as optimal sample size allocations will be derived. The performance of the proposed test will be assessed in an extensive simulation study with regard to type I error rate, power, sample size, and sample size allocation. For the purpose of comparison, Wald-type statistics with a sample variance estimator and an unrestricted maximum-likelihood estimator are included in the simulation study. We found that the proposed Wald-type test with a restricted variance estimator performed well across the considered scenarios and is therefore recommended for application in clinical trials. The methods proposed are motivated and illustrated by a recent clinical trial in multiple sclerosis. The R package ThreeArmedTrials, which implements the methods discussed in this paper, is available on CRAN. Copyright © 2015 John Wiley & Sons, Ltd.

Type-II generalized family-wise error rate formulas with application to sample size determination.

PubMed

Delorme, Phillipe; de Micheaux, Pierre Lafaye; Liquet, Benoit; Riou, Jérémie

2016-07-20

Multiple endpoints are increasingly used in clinical trials. The significance of some of these clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. The usual approach in multiple hypothesis testing is to control the family-wise error rate, which is defined as the probability that at least one type-I error is made. More recently, the q-generalized family-wise error rate has been introduced to control the probability of making at least q false rejections. For procedures controlling this global type-I error rate, we define a type-II r-generalized family-wise error rate, which is directly related to the r-power defined as the probability of rejecting at least r false null hypotheses. We obtain very general power formulas that can be used to compute the sample size for single-step and step-wise procedures. These are implemented in our R package rPowerSampleSize available on the CRAN, making them directly available to end users. Complexities of the formulas are presented to gain insight into computation time issues. Comparison with Monte Carlo strategy is also presented. We compute sample sizes for two clinical trials involving multiple endpoints: one designed to investigate the effectiveness of a drug against acute heart failure and the other for the immunogenicity of a vaccine strategy against pneumococcus. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Accounting for twin births in sample size calculations for randomised trials.

PubMed

Yelland, Lisa N; Sullivan, Thomas R; Collins, Carmel T; Price, David J; McPhee, Andrew J; Lee, Katherine J

2018-05-04

Including twins in randomised trials leads to non-independence or clustering in the data. Clustering has important implications for sample size calculations, yet few trials take this into account. Estimates of the intracluster correlation coefficient (ICC), or the correlation between outcomes of twins, are needed to assist with sample size planning. Our aims were to provide ICC estimates for infant outcomes, describe the information that must be specified in order to account for clustering due to twins in sample size calculations, and develop a simple tool for performing sample size calculations for trials including twins. ICCs were estimated for infant outcomes collected in four randomised trials that included twins. The information required to account for clustering due to twins in sample size calculations is described. A tool that calculates the sample size based on this information was developed in Microsoft Excel and in R as a Shiny web app. ICC estimates ranged between -0.12, indicating a weak negative relationship, and 0.98, indicating a strong positive relationship between outcomes of twins. Example calculations illustrate how the ICC estimates and sample size calculator can be used to determine the target sample size for trials including twins. Clustering among outcomes measured on twins should be taken into account in sample size calculations to obtain the desired power. Our ICC estimates and sample size calculator will be useful for designing future trials that include twins. Publication of additional ICCs is needed to further assist with sample size planning for future trials. © 2018 John Wiley & Sons Ltd.

Accounting for response misclassification and covariate measurement error improves power and reduces bias in epidemiologic studies.

PubMed

Cheng, Dunlei; Branscum, Adam J; Stamey, James D

2010-07-01

To quantify the impact of ignoring misclassification of a response variable and measurement error in a covariate on statistical power, and to develop software for sample size and power analysis that accounts for these flaws in epidemiologic data. A Monte Carlo simulation-based procedure is developed to illustrate the differences in design requirements and inferences between analytic methods that properly account for misclassification and measurement error to those that do not in regression models for cross-sectional and cohort data. We found that failure to account for these flaws in epidemiologic data can lead to a substantial reduction in statistical power, over 25% in some cases. The proposed method substantially reduced bias by up to a ten-fold margin compared to naive estimates obtained by ignoring misclassification and mismeasurement. We recommend as routine practice that researchers account for errors in measurement of both response and covariate data when determining sample size, performing power calculations, or analyzing data from epidemiological studies. 2010 Elsevier Inc. All rights reserved.

A modified approach to estimating sample size for simple logistic regression with one continuous covariate.

PubMed

Novikov, I; Fund, N; Freedman, L S

2010-01-15

Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.

Considerations for throughfall chemistry sample-size determination

Treesearch

Pamela J. Edwards; Paul Mohai; Howard G. Halverson; David R. DeWalle

1989-01-01

Both the number of trees sampled per species and the number of sampling points under each tree are important throughfall sampling considerations. Chemical loadings obtained from an urban throughfall study were used to evaluate the relative importance of both of these sampling factors in tests for determining species' differences. Power curves for detecting...

Statistical analyses support power law distributions found in neuronal avalanches.

PubMed

Klaus, Andreas; Yu, Shan; Plenz, Dietmar

2011-01-01

The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with exponent close to -1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analyses. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling ("finite size" effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to -1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution without and with exponential cut-off provided significantly better fits to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex.

Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Zhang, Zhiyong; Zhao, Yanyun

2017-01-01

The normal-distribution-based likelihood ratio statistic T[subscript ml] = nF[subscript ml] is widely used for power analysis in structural Equation modeling (SEM). In such an analysis, power and sample size are computed by assuming that T[subscript ml] follows a central chi-square distribution under H[subscript 0] and a noncentral chi-square…

On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change

ERIC Educational Resources Information Center

Hertzog, Christopher; Lindenberger, Ulman; Ghisletta, Paolo; Oertzen, Timo von

2006-01-01

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris…

Scale Comparability between Nonaccommodated and Accommodated Forms of a Statewide High School Assessment: Assessment Using "l[subscript z]" Person-Fit

ERIC Educational Resources Information Center

Seo, Dong Gi; Hao, Shiqi

2016-01-01

Differential item/test functioning (DIF/DTF) are routine procedures to detect item/test unfairness as an explanation for group performance difference. However, unequal sample sizes and small sample sizes have an impact on the statistical power of the DIF/DTF detection procedures. Furthermore, DIF/DTF cannot be used for two test forms without…

Statistical aspects of genetic association testing in small samples, based on selective DNA pooling data in the arctic fox.

PubMed

Szyda, Joanna; Liu, Zengting; Zatoń-Dobrowolska, Magdalena; Wierzbicki, Heliodor; Rzasa, Anna

2008-01-01

We analysed data from a selective DNA pooling experiment with 130 individuals of the arctic fox (Alopex lagopus), which originated from 2 different types regarding body size. The association between alleles of 6 selected unlinked molecular markers and body size was tested by using univariate and multinomial logistic regression models, applying odds ratio and test statistics from the power divergence family. Due to the small sample size and the resulting sparseness of the data table, in hypothesis testing we could not rely on the asymptotic distributions of the tests. Instead, we tried to account for data sparseness by (i) modifying confidence intervals of odds ratio; (ii) using a normal approximation of the asymptotic distribution of the power divergence tests with different approaches for calculating moments of the statistics; and (iii) assessing P values empirically, based on bootstrap samples. As a result, a significant association was observed for 3 markers. Furthermore, we used simulations to assess the validity of the normal approximation of the asymptotic distribution of the test statistics under the conditions of small and sparse samples.

Heating efficiency dependency on size and morphology of magnetite nanoparticles

NASA Astrophysics Data System (ADS)

Parekh, Kinnari; Parmar, Harshida; Sharma, Vinay; Ramanujan, R. V.

2018-04-01

Different size magnetite nanoparticles ranging from superparamagnetic (9 nm) to single domain (27 nm) and multi domain (53 nm) were synthesized using chemical route. Morphology of these particles as seen from TEM images indicates shape change from spherical to cubic with the growth of particles. The saturation magnetization (σs) and Specific Loss Power (SLP) showed maximum for single domain size, 72 emu/g and 102 W/g, respectively then those of multi domain size particles. These samples show higher SLP at relatively low concentration, low frequency and low amplitude compared to samples prepared by other routes.

UNIFORMLY MOST POWERFUL BAYESIAN TESTS

PubMed Central

Johnson, Valen E.

2014-01-01

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternative hypothesis, exceeds a specified threshold. Like their classical counterpart, uniformly most powerful Bayesian tests are most easily defined in one-parameter exponential family models, although extensions outside of this class are possible. The connection between uniformly most powerful tests and uniformly most powerful Bayesian tests can be used to provide an approximate calibration between p-values and Bayes factors. Finally, issues regarding the strong dependence of resulting Bayes factors and p-values on sample size are discussed. PMID:24659829

Explanation of Two Anomalous Results in Statistical Mediation Analysis.

PubMed

Fritz, Matthew S; Taylor, Aaron B; Mackinnon, David P

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M , a , increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y , b , was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a . Implications of these findings are discussed.

Thermal Stability of Zone Melting p-Type (Bi, Sb)2Te3 Ingots and Comparison with the Corresponding Powder Metallurgy Samples

NASA Astrophysics Data System (ADS)

Jiang, Chengpeng; Fan, Xi'an; Hu, Jie; Feng, Bo; Xiang, Qiusheng; Li, Guangqiang; Li, Yawei; He, Zhu

2018-04-01

During the past few decades, Bi2Te3-based alloys have been investigated extensively because of their promising application in the area of low temperature waste heat thermoelectric power generation. However, their thermal stability must be evaluated to explore the appropriate service temperature. In this work, the thermal stability of zone melting p-type (Bi, Sb)2Te3-based ingots was investigated under different annealing treatment conditions. The effect of service temperature on the thermoelectric properties and hardness of the samples was also discussed in detail. The results showed that the grain size, density, dimension size and mass remained nearly unchanged when the service temperature was below 523 K, which suggested that the geometry size of zone melting p-type (Bi, Sb)2Te3-based materials was stable below 523 K. The power factor and Vickers hardness of the ingots also changed little and maintained good thermal stability. Unfortunately, the thermal conductivity increased with increasing annealing temperature, which resulted in an obvious decrease of the zT value. In addition, the thermal stabilities of the zone melting p-type (Bi, Sb)2Te3-based materials and the corresponding powder metallurgy samples were also compared. All evidence implied that the thermal stabilities of the zone-melted (ZMed) p-type (Bi, Sb)2Te3 ingots in terms of crystal structure, geometry size, power factor (PF) and hardness were better than those of the corresponding powder metallurgy samples. However, their thermal stabilities in terms of zT values were similar under different annealing temperatures.

Small Sample Performance of Bias-corrected Sandwich Estimators for Cluster-Randomized Trials with Binary Outcomes

PubMed Central

Li, Peng; Redden, David T.

2014-01-01

SUMMARY The sandwich estimator in generalized estimating equations (GEE) approach underestimates the true variance in small samples and consequently results in inflated type I error rates in hypothesis testing. This fact limits the application of the GEE in cluster-randomized trials (CRTs) with few clusters. Under various CRT scenarios with correlated binary outcomes, we evaluate the small sample properties of the GEE Wald tests using bias-corrected sandwich estimators. Our results suggest that the GEE Wald z test should be avoided in the analyses of CRTs with few clusters even when bias-corrected sandwich estimators are used. With t-distribution approximation, the Kauermann and Carroll (KC)-correction can keep the test size to nominal levels even when the number of clusters is as low as 10, and is robust to the moderate variation of the cluster sizes. However, in cases with large variations in cluster sizes, the Fay and Graubard (FG)-correction should be used instead. Furthermore, we derive a formula to calculate the power and minimum total number of clusters one needs using the t test and KC-correction for the CRTs with binary outcomes. The power levels as predicted by the proposed formula agree well with the empirical powers from the simulations. The proposed methods are illustrated using real CRT data. We conclude that with appropriate control of type I error rates under small sample sizes, we recommend the use of GEE approach in CRTs with binary outcomes due to fewer assumptions and robustness to the misspecification of the covariance structure. PMID:25345738

Nano-Sized Structurally Disordered Metal Oxide Composite Aerogels as High-Power Anodes in Hybrid Supercapacitors.

PubMed

Huang, Haijian; Wang, Xing; Tervoort, Elena; Zeng, Guobo; Liu, Tian; Chen, Xi; Sologubenko, Alla; Niederberger, Markus

2018-03-27

A general method for preparing nano-sized metal oxide nanoparticles with highly disordered crystal structure and their processing into stable aqueous dispersions is presented. With these nanoparticles as building blocks, a series of nanoparticles@reduced graphene oxide (rGO) composite aerogels are fabricated and directly used as high-power anodes for lithium-ion hybrid supercapacitors (Li-HSCs). To clarify the effect of the degree of disorder, control samples of crystalline nanoparticles with similar particle size are prepared. The results indicate that the structurally disordered samples show a significantly enhanced electrochemical performance compared to the crystalline counterparts. In particular, structurally disordered Ni x Fe y O z @rGO delivers a capacity of 388 mAh g -1 at 5 A g -1 , which is 6 times that of the crystalline sample. Disordered Ni x Fe y O z @rGO is taken as an example to study the reasons for the enhanced performance. Compared with the crystalline sample, density functional theory calculations reveal a smaller volume expansion during Li + insertion for the structurally disordered Ni x Fe y O z nanoparticles, and they are found to exhibit larger pseudocapacitive effects. Combined with an activated carbon (AC) cathode, full-cell tests of the lithium-ion hybrid supercapacitors are performed, demonstrating that the structurally disordered metal oxide nanoparticles@rGO||AC hybrid systems deliver high energy and power densities within the voltage range of 1.0-4.0 V. These results indicate that structurally disordered nanomaterials might be interesting candidates for exploring high-power anodes for Li-HSCs.

Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Ross-Degnan, Dennis

2011-11-01

Interrupted time series is a strong quasi-experimental research design to evaluate the impacts of health policy interventions. Using simulation methods, we estimated the power requirements for interrupted time series studies under various scenarios. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 and effect size was 0.5, 1.0, and 2.0, investigating balanced and unbalanced numbers of time periods before and after an intervention. Simple scenarios of autoregressive conditional heteroskedasticity (ARCH) models were also explored. For AR models, power increased when sample size or effect size increased, and tended to decrease when autocorrelation increased. Compared with a balanced number of study periods before and after an intervention, designs with unbalanced numbers of periods had less power, although that was not the case for ARCH models. The power to detect effect size 1.0 appeared to be reasonable for many practical applications with a moderate or large number of time points in the study equally divided around the intervention. Investigators should be cautious when the expected effect size is small or the number of time points is small. We recommend conducting various simulations before investigation. Copyright © 2011 Elsevier Inc. All rights reserved.

Estimation and applications of size-based distributions in forestry

Treesearch

Jeffrey H. Gove

2003-01-01

Size-based distributions arise in several contexts in forestry and ecology. Simple power relationships (e.g., basal area and diameter at breast height) between variables are one such area of interest arising from a modeling perspective. Another, probability proportional to size sampline (PPS), is found in the most widely used methods for sampling standing or dead and...

Using Monte Carlo Simulations to Determine Power and Sample Size for Planned Missing Designs

ERIC Educational Resources Information Center

Schoemann, Alexander M.; Miller, Patrick; Pornprasertmanit, Sunthud; Wu, Wei

2014-01-01

Planned missing data designs allow researchers to increase the amount and quality of data collected in a single study. Unfortunately, the effect of planned missing data designs on power is not straightforward. Under certain conditions using a planned missing design will increase power, whereas in other situations using a planned missing design…

Sampling designs for contaminant temporal trend analyses using sedentary species exemplified by the snails Bellamya aeruginosa and Viviparus viviparus.

PubMed

Yin, Ge; Danielsson, Sara; Dahlberg, Anna-Karin; Zhou, Yihui; Qiu, Yanling; Nyberg, Elisabeth; Bignert, Anders

2017-10-01

Environmental monitoring typically assumes samples and sampling activities to be representative of the population being studied. Given a limited budget, an appropriate sampling strategy is essential to support detecting temporal trends of contaminants. In the present study, based on real chemical analysis data on polybrominated diphenyl ethers in snails collected from five subsites in Tianmu Lake, computer simulation is performed to evaluate three sampling strategies by the estimation of required sample size, to reach a detection of an annual change of 5% with a statistical power of 80% and 90% with a significant level of 5%. The results showed that sampling from an arbitrarily selected sampling spot is the worst strategy, requiring much more individual analyses to achieve the above mentioned criteria compared with the other two approaches. A fixed sampling site requires the lowest sample size but may not be representative for the intended study object e.g. a lake and is also sensitive to changes of that particular sampling site. In contrast, sampling at multiple sites along the shore each year, and using pooled samples when the cost to collect and prepare individual specimens are much lower than the cost for chemical analysis, would be the most robust and cost efficient strategy in the long run. Using statistical power as criterion, the results demonstrated quantitatively the consequences of various sampling strategies, and could guide users with respect of required sample sizes depending on sampling design for long term monitoring programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving the quality of biomarker discovery research: the right samples and enough of them.

PubMed

Pepe, Margaret S; Li, Christopher I; Feng, Ziding

2015-06-01

Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs. The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE). We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions. Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified. The scientific rigor of discovery research should be improved. ©2015 American Association for Cancer Research.

Effect of Microwave Radiation Power on the Size of Aggregates of ZnO NPs Prepared Using Microwave Solvothermal Synthesis

PubMed Central

Chudoba, Tadeusz; Gierlotka, Stanisław; Lojkowski, Witold

2018-01-01

This paper reports the possibility of changing the size of zinc oxide nanoparticles (ZnO NPs) aggregates through a change of synthesis parameters. The effect of the changed power of microwave heating on the properties of ZnO NPs obtained by the microwave solvothermal synthesis from zinc acetate dissolved in ethylene glycol was tested for the first time. It was found that the size of ZnO aggregates ranged from 60 to 120 nm depending on the power of microwave radiation used in the synthesis of ZnO NPs. The increase in the microwave radiation power resulted in the reduction of the total synthesis time with simultaneous preservation of the constant size and shape of single ZnO NPs, which were synthesized at a pressure of 4 bar. All the obtained ZnO NPs samples were composed of homogeneous spherical particles that were single crystals with an average size of 27 ± 3 nm with a developed specific surface area of 40 m2/g and the skeleton density of 5.18 ± 0.03 g/cm3. A model of a mechanism explaining the correlation between the size of aggregates and the power of microwaves was proposed. This method of controlling the average size of ZnO NPs aggregates is presented for the first time and similar investigations are not found in the literature. PMID:29783651

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS

PubMed Central

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2013-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models. PMID:23935262

A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research.

PubMed

Yang, Songshan; Cranford, James A; Jester, Jennifer M; Li, Runze; Zucker, Robert A; Buu, Anne

2017-02-28

This study proposes a time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes. The motivating example demonstrates that this zero-inflated Poisson model allows investigators to study group differences in different aspects of substance use (e.g., the probability of abstinence and the quantity of alcohol use) simultaneously. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size increases; the accuracy under equal group sizes is only higher when the sample size is small (100). In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all settings. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size is larger. Moreover, the hypothesis test for the group difference in the zero component tends to be less powerful than the test for the group difference in the Poisson component. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The quality of the reported sample size calculations in randomized controlled trials indexed in PubMed.

PubMed

Lee, Paul H; Tse, Andy C Y

2017-05-01

There are limited data on the quality of reporting of information essential for replication of the calculation as well as the accuracy of the sample size calculation. We examine the current quality of reporting of the sample size calculation in randomized controlled trials (RCTs) published in PubMed and to examine the variation in reporting across study design, study characteristics, and journal impact factor. We also reviewed the targeted sample size reported in trial registries. We reviewed and analyzed all RCTs published in December 2014 with journals indexed in PubMed. The 2014 Impact Factors for the journals were used as proxies for their quality. Of the 451 analyzed papers, 58.1% reported an a priori sample size calculation. Nearly all papers provided the level of significance (97.7%) and desired power (96.6%), and most of the papers reported the minimum clinically important effect size (73.3%). The median (inter-quartile range) of the percentage difference of the reported and calculated sample size calculation was 0.0% (IQR -4.6%;3.0%). The accuracy of the reported sample size was better for studies published in journals that endorsed the CONSORT statement and journals with an impact factor. A total of 98 papers had provided targeted sample size on trial registries and about two-third of these papers (n=62) reported sample size calculation, but only 25 (40.3%) had no discrepancy with the reported number in the trial registries. The reporting of the sample size calculation in RCTs published in PubMed-indexed journals and trial registries were poor. The CONSORT statement should be more widely endorsed. Copyright © 2016 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Sample size considerations for clinical research studies in nuclear cardiology.

PubMed

Chiuzan, Cody; West, Erin A; Duong, Jimmy; Cheung, Ken Y K; Einstein, Andrew J

2015-12-01

Sample size calculation is an important element of research design that investigators need to consider in the planning stage of the study. Funding agencies and research review panels request a power analysis, for example, to determine the minimum number of subjects needed for an experiment to be informative. Calculating the right sample size is crucial to gaining accurate information and ensures that research resources are used efficiently and ethically. The simple question "How many subjects do I need?" does not always have a simple answer. Before calculating the sample size requirements, a researcher must address several aspects, such as purpose of the research (descriptive or comparative), type of samples (one or more groups), and data being collected (continuous or categorical). In this article, we describe some of the most frequent methods for calculating the sample size with examples from nuclear cardiology research, including for t tests, analysis of variance (ANOVA), non-parametric tests, correlation, Chi-squared tests, and survival analysis. For the ease of implementation, several examples are also illustrated via user-friendly free statistical software.

A U-statistics based approach to sample size planning of two-arm trials with discrete outcome criterion aiming to establish either superiority or noninferiority.

PubMed

Wellek, Stefan

2017-02-28

In current practice, the most frequently applied approach to the handling of ties in the Mann-Whitney-Wilcoxon (MWW) test is based on the conditional distribution of the sum of mid-ranks, given the observed pattern of ties. Starting from this conditional version of the testing procedure, a sample size formula was derived and investigated by Zhao et al. (Stat Med 2008). In contrast, the approach we pursue here is a nonconditional one exploiting explicit representations for the variances of and the covariance between the two U-statistics estimators involved in the Mann-Whitney form of the test statistic. The accuracy of both ways of approximating the sample sizes required for attaining a prespecified level of power in the MWW test for superiority with arbitrarily tied data is comparatively evaluated by means of simulation. The key qualitative conclusions to be drawn from these numerical comparisons are as follows: With the sample sizes calculated by means of the respective formula, both versions of the test maintain the level and the prespecified power with about the same degree of accuracy. Despite the equivalence in terms of accuracy, the sample size estimates obtained by means of the new formula are in many cases markedly lower than that calculated for the conditional test. Perhaps, a still more important advantage of the nonconditional approach based on U-statistics is that it can be also adopted for noninferiority trials. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies

PubMed Central

2014-01-01

Background The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. Methods The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. Results The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. Conclusions If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used. PMID:24552686

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies.

PubMed

Kottas, Martina; Kuss, Oliver; Zapf, Antonia

2014-02-19

The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used.

Small sample sizes in the study of ontogenetic allometry; implications for palaeobiology

PubMed Central

Vavrek, Matthew J.

2015-01-01

Quantitative morphometric analyses, particularly ontogenetic allometry, are common methods used in quantifying shape, and changes therein, in both extinct and extant organisms. Due to incompleteness and the potential for restricted sample sizes in the fossil record, palaeobiological analyses of allometry may encounter higher rates of error. Differences in sample size between fossil and extant studies and any resulting effects on allometric analyses have not been thoroughly investigated, and a logical lower threshold to sample size is not clear. Here we show that studies based on fossil datasets have smaller sample sizes than those based on extant taxa. A similar pattern between vertebrates and invertebrates indicates this is not a problem unique to either group, but common to both. We investigate the relationship between sample size, ontogenetic allometric relationship and statistical power using an empirical dataset of skull measurements of modern Alligator mississippiensis. Across a variety of subsampling techniques, used to simulate different taphonomic and/or sampling effects, smaller sample sizes gave less reliable and more variable results, often with the result that allometric relationships will go undetected due to Type II error (failure to reject the null hypothesis). This may result in a false impression of fewer instances of positive/negative allometric growth in fossils compared to living organisms. These limitations are not restricted to fossil data and are equally applicable to allometric analyses of rare extant taxa. No mathematically derived minimum sample size for ontogenetic allometric studies is found; rather results of isometry (but not necessarily allometry) should not be viewed with confidence at small sample sizes. PMID:25780770

The quantitative LOD score: test statistic and sample size for exclusion and linkage of quantitative traits in human sibships.

PubMed

Page, G P; Amos, C I; Boerwinkle, E

1998-04-01

We present a test statistic, the quantitative LOD (QLOD) score, for the testing of both linkage and exclusion of quantitative-trait loci in randomly selected human sibships. As with the traditional LOD score, the boundary values of 3, for linkage, and -2, for exclusion, can be used for the QLOD score. We investigated the sample sizes required for inferring exclusion and linkage, for various combinations of linked genetic variance, total heritability, recombination distance, and sibship size, using fixed-size sampling. The sample sizes required for both linkage and exclusion were not qualitatively different and depended on the percentage of variance being linked or excluded and on the total genetic variance. Information regarding linkage and exclusion in sibships larger than size 2 increased as approximately all possible pairs n(n-1)/2 up to sibships of size 6. Increasing the recombination (theta) distance between the marker and the trait loci reduced empirically the power for both linkage and exclusion, as a function of approximately (1-2theta)4.

Deactivation of Escherichia coli by the plasma needle

NASA Astrophysics Data System (ADS)

Sladek, R. E. J.; Stoffels, E.

2005-06-01

In this paper we present a parameter study on deactivation of Escherichia coli (E. coli) by means of a non-thermal plasma (plasma needle). The plasma needle is a small-sized (1 mm) atmospheric glow sustained by radio-frequency excitation. This plasma will be used to disinfect heat-sensitive objects; one of the intended applications is in vivo deactivation of dental bacteria: destruction of plaque and treatment of caries. We use E. coli films plated on agar dishes as a model system to optimize the conditions for bacterial destruction. Plasma power, treatment time and needle-to-sample distance are varied. Plasma treatment of E. coli films results in formation of a bacteria-free void with a size up to 12 mm. 104-105 colony forming units are already destroyed after 10 s of treatment. Prolongation of treatment time and usage of high powers do not significantly improve the destruction efficiency: short exposure at low plasma power is sufficient. Furthermore, we study the effects of temperature increase on the survival of E. coli and compare it with thermal effects of the plasma. The population of E. coli heated in a warm water bath starts to decrease at temperatures above 40°C. Sample temperature during plasma treatment has been monitored. The temperature can reach up to 60°C at high plasma powers and short needle-to-sample distances. However, thermal effects cannot account for bacterial destruction at low power conditions. For safe and efficient in vivo disinfection, the sample temperature should be kept low. Thus, plasma power and treatment time should not exceed 150 mW and 60 s, respectively.

Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study.

PubMed

Durand, Casey P

2013-01-01

Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model. A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations. In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified. Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.

Investigating the unification of LOFAR-detected powerful AGN in the Boötes field

NASA Astrophysics Data System (ADS)

Morabito, Leah K.; Williams, W. L.; Duncan, Kenneth J.; Röttgering, H. J. A.; Miley, George; Saxena, Aayush; Barthel, Peter; Best, P. N.; Bruggen, M.; Brunetti, G.; Chyży, K. T.; Engels, D.; Hardcastle, M. J.; Harwood, J. J.; Jarvis, Matt J.; Mahony, E. K.; Prandoni, I.; Shimwell, T. W.; Shulevski, A.; Tasse, C.

2017-08-01

Low radio frequency surveys are important for testing unified models of radio-loud quasars and radio galaxies. Intrinsically similar sources that are randomly oriented on the sky will have different projected linear sizes. Measuring the projected linear sizes of these sources provides an indication of their orientation. Steep-spectrum isotropic radio emission allows for orientation-free sample selection at low radio frequencies. We use a new radio survey of the Boötes field at 150 MHz made with the Low-Frequency Array (LOFAR) to select a sample of radio sources. We identify 60 radio sources with powers P > 1025.5 W Hz-1 at 150 MHz using cross-matched multiwavelength information from the AGN and Galaxy Evolution Survey, which provides spectroscopic redshifts and photometric identification of 16 quasars and 44 radio galaxies. When considering the radio spectral slope only, we find that radio sources with steep spectra have projected linear sizes that are on average 4.4 ± 1.4 larger than those with flat spectra. The projected linear sizes of radio galaxies are on average 3.1 ± 1.0 larger than those of quasars (2.0 ± 0.3 after correcting for redshift evolution). Combining these results with three previous surveys, we find that the projected linear sizes of radio galaxies and quasars depend on redshift but not on power. The projected linear size ratio does not correlate with either parameter. The LOFAR data are consistent within the uncertainties with theoretical predictions of the correlation between the quasar fraction and linear size ratio, based on an orientation-based unification scheme.

Novel joint selection methods can reduce sample size for rheumatoid arthritis clinical trials with ultrasound endpoints.

PubMed

Allen, John C; Thumboo, Julian; Lye, Weng Kit; Conaghan, Philip G; Chew, Li-Ching; Tan, York Kiat

2018-03-01

To determine whether novel methods of selecting joints through (i) ultrasonography (individualized-ultrasound [IUS] method), or (ii) ultrasonography and clinical examination (individualized-composite-ultrasound [ICUS] method) translate into smaller rheumatoid arthritis (RA) clinical trial sample sizes when compared to existing methods utilizing predetermined joint sites for ultrasonography. Cohen's effect size (ES) was estimated (ES^) and a 95% CI (ES^L, ES^U) calculated on a mean change in 3-month total inflammatory score for each method. Corresponding 95% CIs [nL(ES^U), nU(ES^L)] were obtained on a post hoc sample size reflecting the uncertainty in ES^. Sample size calculations were based on a one-sample t-test as the patient numbers needed to provide 80% power at α = 0.05 to reject a null hypothesis H 0 : ES = 0 versus alternative hypotheses H 1 : ES = ES^, ES = ES^L and ES = ES^U. We aimed to provide point and interval estimates on projected sample sizes for future studies reflecting the uncertainty in our study ES^S. Twenty-four treated RA patients were followed up for 3 months. Utilizing the 12-joint approach and existing methods, the post hoc sample size (95% CI) was 22 (10-245). Corresponding sample sizes using ICUS and IUS were 11 (7-40) and 11 (6-38), respectively. Utilizing a seven-joint approach, the corresponding sample sizes using ICUS and IUS methods were nine (6-24) and 11 (6-35), respectively. Our pilot study suggests that sample size for RA clinical trials with ultrasound endpoints may be reduced using the novel methods, providing justification for larger studies to confirm these observations. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

The Power of Percent

ERIC Educational Resources Information Center

Watson, Jane; English, Lyn

2013-01-01

Jane Watson and Lyn English use a chance activity exploring expectation and variation with coin tossing to highlight the importance of understanding the part-whole relationship embodied in percentage and its power to measure and compare for different wholes, in this case different sample sizes. The purpose of this article is to raise awareness of…

Strategies for Improving Power in School-Randomized Studies of Professional Development.

PubMed

Kelcey, Ben; Phelps, Geoffrey

2013-12-01

Group-randomized designs are well suited for studies of professional development because they can accommodate programs that are delivered to intact groups (e.g., schools), the collaborative nature of professional development, and extant teacher/school assignments. Though group designs may be theoretically favorable, prior evidence has suggested that they may be challenging to conduct in professional development studies because well-powered designs will typically require large sample sizes or expect large effect sizes. Using teacher knowledge outcomes in mathematics, we investigated when and the extent to which there is evidence that covariance adjustment on a pretest, teacher certification, or demographic covariates can reduce the sample size necessary to achieve reasonable power. Our analyses drew on multilevel models and outcomes in five different content areas for over 4,000 teachers and 2,000 schools. Using these estimates, we assessed the minimum detectable effect sizes for several school-randomized designs with and without covariance adjustment. The analyses suggested that teachers' knowledge is substantially clustered within schools in each of the five content areas and that covariance adjustment for a pretest or, to a lesser extent, teacher certification, has the potential to transform designs that are unreasonably large for professional development studies into viable studies. © The Author(s) 2014.

Sample size calculations for cluster randomised crossover trials in Australian and New Zealand intensive care research.

PubMed

Arnup, Sarah J; McKenzie, Joanne E; Pilcher, David; Bellomo, Rinaldo; Forbes, Andrew B

2018-06-01

The cluster randomised crossover (CRXO) design provides an opportunity to conduct randomised controlled trials to evaluate low risk interventions in the intensive care setting. Our aim is to provide a tutorial on how to perform a sample size calculation for a CRXO trial, focusing on the meaning of the elements required for the calculations, with application to intensive care trials. We use all-cause in-hospital mortality from the Australian and New Zealand Intensive Care Society Adult Patient Database clinical registry to illustrate the sample size calculations. We show sample size calculations for a two-intervention, two 12-month period, cross-sectional CRXO trial. We provide the formulae, and examples of their use, to determine the number of intensive care units required to detect a risk ratio (RR) with a designated level of power between two interventions for trials in which the elements required for sample size calculations remain constant across all ICUs (unstratified design); and in which there are distinct groups (strata) of ICUs that differ importantly in the elements required for sample size calculations (stratified design). The CRXO design markedly reduces the sample size requirement compared with the parallel-group, cluster randomised design for the example cases. The stratified design further reduces the sample size requirement compared with the unstratified design. The CRXO design enables the evaluation of routinely used interventions that can bring about small, but important, improvements in patient care in the intensive care setting.

Fractionating power and outlet stream polydispersity in asymmetrical flow field-flow fractionation. Part I: isocratic operation.

PubMed

Williams, P Stephen

2016-05-01

Asymmetrical flow field-flow fractionation (As-FlFFF) has become the most commonly used of the field-flow fractionation techniques. However, because of the interdependence of the channel flow and the cross flow through the accumulation wall, it is the most difficult of the techniques to optimize, particularly for programmed cross flow operation. For the analysis of polydisperse samples, the optimization should ideally be guided by the predicted fractionating power. Many experimentalists, however, neglect fractionating power and rely on light scattering detection simply to confirm apparent selectivity across the breadth of the eluted peak. The size information returned by the light scattering software is assumed to dispense with any reliance on theory to predict retention, and any departure of theoretical predictions from experimental observations is therefore considered of no importance. Separation depends on efficiency as well as selectivity, however, and efficiency can be a strong function of retention. The fractionation of a polydisperse sample by field-flow fractionation never provides a perfectly separated series of monodisperse fractions at the channel outlet. The outlet stream has some residual polydispersity, and it will be shown in this manuscript that the residual polydispersity is inversely related to the fractionating power. Due to the strong dependence of light scattering intensity and its angular distribution on the size of the scattering species, the outlet polydispersity must be minimized if reliable size data are to be obtained from the light scattering detector signal. It is shown that light scattering detection should be used with careful control of fractionating power to obtain optimized analysis of polydisperse samples. Part I is concerned with isocratic operation of As-FlFFF, and part II with programmed operation.

Development of size reduction equations for calculating power input for grinding pine wood chips using hammer mill

DOE Office of Scientific and Technical Information (OSTI.GOV)

Naimi, Ladan J.; Collard, Flavien; Bi, Xiaotao

Size reduction is an unavoidable operation for preparing biomass for biofuels and bioproduct conversion. Yet, there is considerable uncertainty in power input requirement and the uniformity of ground biomass. Considerable gains are possible if the required power input for a size reduction ratio is estimated accurately. In this research three well-known mechanistic equations attributed to Rittinger, Kick, and Bond available for predicting energy input for grinding pine wood chips were tested against experimental grinding data. Prior to testing, samples of pine wood chips were conditioned to 11.7% wb, moisture content. The wood chips were successively ground in a hammer millmore » using screen sizes of 25.4 mm, 10 mm, 6.4 mm, and 3.2 mm. The input power and the flow of material into the grinder were recorded continuously. The recorded power input vs. mean particle size showed that the Rittinger equation had the best fit to the experimental data. The ground particle sizes were 4 to 7 times smaller than the size of installed screen. Geometric mean size of particles were calculated using two methods (1) Tyler sieves and using particle size analysis and (2) Sauter mean diameter calculated from the ratio of volume to surface that were estimated from measured length and width. The two mean diameters agreed well, pointing to the fact that either mechanical sieving or particle imaging can be used to characterize particle size. In conclusion, specific energy input to the hammer mill increased from 1.4 kWh t –1 (5.2 J g –1) for large 25.1-mm screen to 25 kWh t –1 (90.4 J g –1) for small 3.2-mm screen.« less

Development of size reduction equations for calculating power input for grinding pine wood chips using hammer mill

DOE PAGES

Naimi, Ladan J.; Collard, Flavien; Bi, Xiaotao; ...

2016-01-05

Size reduction is an unavoidable operation for preparing biomass for biofuels and bioproduct conversion. Yet, there is considerable uncertainty in power input requirement and the uniformity of ground biomass. Considerable gains are possible if the required power input for a size reduction ratio is estimated accurately. In this research three well-known mechanistic equations attributed to Rittinger, Kick, and Bond available for predicting energy input for grinding pine wood chips were tested against experimental grinding data. Prior to testing, samples of pine wood chips were conditioned to 11.7% wb, moisture content. The wood chips were successively ground in a hammer millmore » using screen sizes of 25.4 mm, 10 mm, 6.4 mm, and 3.2 mm. The input power and the flow of material into the grinder were recorded continuously. The recorded power input vs. mean particle size showed that the Rittinger equation had the best fit to the experimental data. The ground particle sizes were 4 to 7 times smaller than the size of installed screen. Geometric mean size of particles were calculated using two methods (1) Tyler sieves and using particle size analysis and (2) Sauter mean diameter calculated from the ratio of volume to surface that were estimated from measured length and width. The two mean diameters agreed well, pointing to the fact that either mechanical sieving or particle imaging can be used to characterize particle size. In conclusion, specific energy input to the hammer mill increased from 1.4 kWh t –1 (5.2 J g –1) for large 25.1-mm screen to 25 kWh t –1 (90.4 J g –1) for small 3.2-mm screen.« less

[Sample size calculation in clinical post-marketing evaluation of traditional Chinese medicine].

PubMed

Fu, Yingkun; Xie, Yanming

2011-10-01

In recent years, as the Chinese government and people pay more attention on the post-marketing research of Chinese Medicine, part of traditional Chinese medicine breed has or is about to begin after the listing of post-marketing evaluation study. In the post-marketing evaluation design, sample size calculation plays a decisive role. It not only ensures the accuracy and reliability of post-marketing evaluation. but also assures that the intended trials will have a desired power for correctly detecting a clinically meaningful difference of different medicine under study if such a difference truly exists. Up to now, there is no systemic method of sample size calculation in view of the traditional Chinese medicine. In this paper, according to the basic method of sample size calculation and the characteristic of the traditional Chinese medicine clinical evaluation, the sample size calculation methods of the Chinese medicine efficacy and safety are discussed respectively. We hope the paper would be beneficial to medical researchers, and pharmaceutical scientists who are engaged in the areas of Chinese medicine research.

Sample size re-assessment leading to a raised sample size does not inflate type I error rate under mild conditions.

PubMed

Broberg, Per

2013-07-19

One major concern with adaptive designs, such as the sample size adjustable designs, has been the fear of inflating the type I error rate. In (Stat Med 23:1023-1038, 2004) it is however proven that when observations follow a normal distribution and the interim result show promise, meaning that the conditional power exceeds 50%, type I error rate is protected. This bound and the distributional assumptions may seem to impose undesirable restrictions on the use of these designs. In (Stat Med 30:3267-3284, 2011) the possibility of going below 50% is explored and a region that permits an increased sample size without inflation is defined in terms of the conditional power at the interim. A criterion which is implicit in (Stat Med 30:3267-3284, 2011) is derived by elementary methods and expressed in terms of the test statistic at the interim to simplify practical use. Mathematical and computational details concerning this criterion are exhibited. Under very general conditions the type I error rate is preserved under sample size adjustable schemes that permit a raise. The main result states that for normally distributed observations raising the sample size when the result looks promising, where the definition of promising depends on the amount of knowledge gathered so far, guarantees the protection of the type I error rate. Also, in the many situations where the test statistic approximately follows a normal law, the deviation from the main result remains negligible. This article provides details regarding the Weibull and binomial distributions and indicates how one may approach these distributions within the current setting. There is thus reason to consider such designs more often, since they offer a means of adjusting an important design feature at little or no cost in terms of error rate.

Augmenting the logrank test in the design of clinical trials in which non-proportional hazards of the treatment effect may be anticipated.

PubMed

Royston, Patrick; Parmar, Mahesh K B

2016-02-11

Most randomized controlled trials with a time-to-event outcome are designed assuming proportional hazards (PH) of the treatment effect. The sample size calculation is based on a logrank test. However, non-proportional hazards are increasingly common. At analysis, the estimated hazards ratio with a confidence interval is usually presented. The estimate is often obtained from a Cox PH model with treatment as a covariate. If non-proportional hazards are present, the logrank and equivalent Cox tests may lose power. To safeguard power, we previously suggested a 'joint test' combining the Cox test with a test of non-proportional hazards. Unfortunately, a larger sample size is needed to preserve power under PH. Here, we describe a novel test that unites the Cox test with a permutation test based on restricted mean survival time. We propose a combined hypothesis test based on a permutation test of the difference in restricted mean survival time across time. The test involves the minimum of the Cox and permutation test P-values. We approximate its null distribution and correct it for correlation between the two P-values. Using extensive simulations, we assess the type 1 error and power of the combined test under several scenarios and compare with other tests. We investigate powering a trial using the combined test. The type 1 error of the combined test is close to nominal. Power under proportional hazards is slightly lower than for the Cox test. Enhanced power is available when the treatment difference shows an 'early effect', an initial separation of survival curves which diminishes over time. The power is reduced under a 'late effect', when little or no difference in survival curves is seen for an initial period and then a late separation occurs. We propose a method of powering a trial using the combined test. The 'insurance premium' offered by the combined test to safeguard power under non-PH represents about a single-digit percentage increase in sample size. The combined test increases trial power under an early treatment effect and protects power under other scenarios. Use of restricted mean survival time facilitates testing and displaying a generalized treatment effect.

A Fixed-Precision Sequential Sampling Plan for the Potato Tuberworm Moth, Phthorimaea operculella Zeller (Lepidoptera: Gelechidae), on Potato Cultivars.

PubMed

Shahbi, M; Rajabpour, A

2017-08-01

Phthorimaea operculella Zeller is an important pest of potato in Iran. Spatial distribution and fixed-precision sequential sampling for population estimation of the pest on two potato cultivars, Arinda ® and Sante ® , were studied in two separate potato fields during two growing seasons (2013-2014 and 2014-2015). Spatial distribution was investigated by Taylor's power law and Iwao's patchiness. Results showed that the spatial distribution of eggs and larvae was random. In contrast to Iwao's patchiness, Taylor's power law provided a highly significant relationship between variance and mean density. Therefore, fixed-precision sequential sampling plan was developed by Green's model at two precision levels of 0.25 and 0.1. The optimum sample size on Arinda ® and Sante ® cultivars at precision level of 0.25 ranged from 151 to 813 and 149 to 802 leaves, respectively. At 0.1 precision level, the sample sizes varied from 5083 to 1054 and 5100 to 1050 leaves for Arinda ® and Sante ® cultivars, respectively. Therefore, the optimum sample sizes for the cultivars, with different resistance levels, were not significantly different. According to the calculated stop lines, the sampling must be continued until cumulative number of eggs + larvae reached to 15-16 or 96-101 individuals at precision levels of 0.25 or 0.1, respectively. The performance of the sampling plan was validated by resampling analysis using resampling for validation of sampling plans software. The sampling plant provided in this study can be used to obtain a rapid estimate of the pest density with minimal effort.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings.

PubMed

Lorca-Puls, Diego L; Gajardo-Vidal, Andrea; White, Jitrachote; Seghier, Mohamed L; Leff, Alexander P; Green, David W; Crinion, Jenny T; Ludersdorfer, Philipp; Hope, Thomas M H; Bowman, Howard; Price, Cathy J

2018-07-01

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Soil carbon inventories under a bioenergy crop (switchgrass): Measurement limitations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garten, C.T. Jr.; Wullschleger, S.D.

Approximately 5 yr after planting, coarse root carbon (C) and soil organic C (SOC) inventories were compared under different types of plant cover at four switchgrass (Panicum virgatum L.) production field trials in the southeastern USA. There was significantly more coarse root C under switchgrass (Alamo variety) and forest cover than tall fescue (Festuca arundinacea Schreb.), corn (Zea mays L.), or native pastures of mixed grasses. Inventories of SOC under switchgrass were not significantly greater than SOC inventories under other plant covers. At some locations the statistical power associated with ANOVA of SOC inventories was low, which raised questions aboutmore » whether differences in SOC could be detected statistically. A minimum detectable difference (MDD) for SOC inventories was calculated. The MDD is the smallest detectable difference between treatment means once the variation, significance level, statistical power, and sample size are specified. The analysis indicated that a difference of {approx}50 mg SOC/cm{sup 2} or 5 Mg SOC/ha, which is {approx}10 to 15% of existing SOC, could be detected with reasonable sample sizes and good statistical power. The smallest difference in SOC inventories that can be detected, and only with exceedingly large sample sizes, is {approx}2 to 3%. These measurement limitations have implications for monitoring and verification of proposals to ameliorate increasing global atmospheric CO{sub 2} concentrations by sequestering C in soils.« less

Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer's disease.

PubMed

Huang, Zhiyue; Muniz-Terrera, Graciela; Tom, Brian D M

2017-09-01

Assessing cognitive and functional changes at the early stage of Alzheimer's disease (AD) and detecting treatment effects in clinical trials for early AD are challenging. Under the assumption that transformed versions of the Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Alzheimer's Disease Assessment Scale-Cognitive Subscale tests'/components' scores are from a multivariate linear mixed-effects model, we calculated the sample sizes required to detect treatment effects on the annual rates of change in these three components in clinical trials for participants with mild cognitive impairment. Our results suggest that a large number of participants would be required to detect a clinically meaningful treatment effect in a population with preclinical or prodromal Alzheimer's disease. We found that the transformed Mini-Mental State Examination is more sensitive for detecting treatment effects in early AD than the transformed Clinical Dementia Rating Scale-Sum of Boxes and Alzheimer's Disease Assessment Scale-Cognitive Subscale. The use of optimal weights to construct powerful test statistics or sensitive composite scores/endpoints can reduce the required sample sizes needed for clinical trials. Consideration of the multivariate/joint distribution of components' scores rather than the distribution of a single composite score when designing clinical trials can lead to an increase in power and reduced sample sizes for detecting treatment effects in clinical trials for early AD.

Sampling benthic macroinvertebrates in a large flood-plain river: Considerations of study design, sample size, and cost

USGS Publications Warehouse

Bartsch, L.A.; Richardson, W.B.; Naimo, T.J.

1998-01-01

Estimation of benthic macroinvertebrate populations over large spatial scales is difficult due to the high variability in abundance and the cost of sample processing and taxonomic analysis. To determine a cost-effective, statistically powerful sample design, we conducted an exploratory study of the spatial variation of benthic macroinvertebrates in a 37 km reach of the Upper Mississippi River. We sampled benthos at 36 sites within each of two strata, contiguous backwater and channel border. Three standard ponar (525 cm(2)) grab samples were obtained at each site ('Original Design'). Analysis of variance and sampling cost of strata-wide estimates for abundance of Oligochaeta, Chironomidae, and total invertebrates showed that only one ponar sample per site ('Reduced Design') yielded essentially the same abundance estimates as the Original Design, while reducing the overall cost by 63%. A posteriori statistical power analysis (alpha = 0.05, beta = 0.20) on the Reduced Design estimated that at least 18 sites per stratum were needed to detect differences in mean abundance between contiguous backwater and channel border areas for Oligochaeta, Chironomidae, and total invertebrates. Statistical power was nearly identical for the three taxonomic groups. The abundances of several taxa of concern (e.g., Hexagenia mayflies and Musculium fingernail clams) were too spatially variable to estimate power with our method. Resampling simulations indicated that to achieve adequate sampling precision for Oligochaeta, at least 36 sample sites per stratum would be required, whereas a sampling precision of 0.2 would not be attained with any sample size for Hexagenia in channel border areas, or Chironomidae and Musculium in both strata given the variance structure of the original samples. Community-wide diversity indices (Brillouin and 1-Simpsons) increased as sample area per site increased. The backwater area had higher diversity than the channel border area. The number of sampling sites required to sample benthic macroinvertebrates during our sampling period depended on the study objective and ranged from 18 to more than 40 sites per stratum. No single sampling regime would efficiently and adequately sample all components of the macroinvertebrate community.

10 CFR 431.325 - Units to be tested.

Code of Federal Regulations, 2011 CFR

2011-01-01

... EQUIPMENT Metal Halide Lamp Ballasts and Fixtures Test Procedures § 431.325 Units to be tested. For each basic model of metal halide lamp ballast selected for testing, a sample of sufficient size, no less than... energy efficiency calculated as the measured output power to the lamp divided by the measured input power...

The Power of the Test for Treatment Effects in Three-Level Block Randomized Designs

ERIC Educational Resources Information Center

Konstantopoulos, Spyros

2008-01-01

Experiments that involve nested structures may assign treatment conditions either to subgroups (such as classrooms) or individuals within subgroups (such as students). The design of such experiments requires knowledge of the intraclass correlation structure to compute the sample sizes necessary to achieve adequate power to detect the treatment…

Power analysis to detect treatment effect in longitudinal studies with heterogeneous errors and incomplete data.

PubMed

Vallejo, Guillermo; Ato, Manuel; Fernández García, Paula; Livacic Rojas, Pablo E; Tuero Herrero, Ellián

2016-08-01

 S. Usami (2014) describes a method to realistically determine sample size in longitudinal research using a multilevel model. The present research extends the aforementioned work to situations where it is likely that the assumption of homogeneity of the errors across groups is not met and the error term does not follow a scaled identity covariance structure.   For this purpose, we followed a procedure based on transforming the variance components of the linear growth model and the parameter related to the treatment effect into specific and easily understandable indices. At the same time, we provide the appropriate statistical machinery for researchers to use when data loss is unavoidable, and changes in the expected value of the observed responses are not linear.   The empirical powers based on unknown variance components were virtually the same as the theoretical powers derived from the use of statistically processed indexes.   The main conclusion of the study is the accuracy of the proposed method to calculate sample size in the described situations with the stipulated power criteria.

Characterization of fly ash from low-sulfur and high-sulfur coal sources: Partitioning of carbon and trace elements with particle size

USGS Publications Warehouse

Hower, J.C.; Trimble, A.S.; Eble, C.F.; Palmer, C.A.; Kolker, A.

1999-01-01

Fly ash samples were collected in November and December of 1994, from generating units at a Kentucky power station using high- and low-sulfur feed coals. The samples are part of a two-year study of the coal and coal combustion byproducts from the power station. The ashes were wet screened at 100, 200, 325, and 500 mesh (150, 75, 42, and 25 ??m, respectively). The size fractions were then dried, weighed, split for petrographic and chemical analysis, and analyzed for ash yield and carbon content. The low-sulfur "heavy side" and "light side" ashes each have a similar size distribution in the November samples. In contrast, the December fly ashes showed the trend observed in later months, the light-side ash being finer (over 20 % more ash in the -500 mesh [-25 ??m] fraction) than the heavy-side ash. Carbon tended to be concentrated in the coarse fractions in the December samples. The dominance of the -325 mesh (-42 ??m) fractions in the overall size analysis implies, though, that carbon in the fine sizes may be an important consideration in the utilization of the fly ash. Element partitioning follows several patterns. Volatile elements, such as Zn and As, are enriched in the finer sizes, particularly in fly ashes collected at cooler, light-side electrostatic precipitator (ESP) temperatures. The latter trend is a function of precipitation at the cooler-ESP temperatures and of increasing concentration with the increased surface area of the finest fraction. Mercury concentrations are higher in high-carbon fly ashes, suggesting Hg adsorption on the fly ash carbon. Ni and Cr are associated, in part, with the spinel minerals in the fly ash. Copyright ?? 1999 Taylor & Francis.

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Incorporating Biological Knowledge into Evaluation of Casual Regulatory Hypothesis

NASA Technical Reports Server (NTRS)

Chrisman, Lonnie; Langley, Pat; Bay, Stephen; Pohorille, Andrew; DeVincenzi, D. (Technical Monitor)

2002-01-01

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review

PubMed Central

Morris, Tom; Gray, Laura

2017-01-01

Objectives To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials. Setting Any, not limited to healthcare settings. Participants Any taking part in an SW-CRT published up to March 2016. Primary and secondary outcome measures The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes. Results Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22–0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed. Conclusions Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed. PMID:29146637

Characteristics of inhalable particulate matter concentration and size distribution from power plants in China.

PubMed

Yi, Honghong; Hao, Jiming; Duan, Lei; Li, Xinghua; Guo, Xingming

2006-09-01

In this investigation, the collection efficiency of particulate emission control devices (PECDs), particulate matter (PM) emissions, and PM size distribution were determined experimentally at the inlet and outlet of PECDs at five coal-fired power plants. Different boilers, coals, and PECDs are used in these power plants. Measurement in situ was performed by an electrical low-pressure impactor with a sampling system, which consisted of an isokinetic sampler probe, precut cyclone, and two-stage dilution system with a sample line to the instruments. The size distribution was measured over a range from 0.03 to 10 microm. Before and after all of the PECDs, the particle number size distributions display a bimodal distribution. The PM2.5 fraction emitted to atmosphere includes a significant amount of the mass from the coarse particle mode. The controlled and uncontrolled emission factors of total PM, inhalable PM (PM10), and fine PM P(M2.5) were obtained. Electrostatic precipitator (ESP) and baghouse total collection efficiencies are 96.38-99.89% and 99.94%, respectively. The minimum collection efficiency of the ESP and the baghouse both appear in the particle size range of 0.1-1 microm. In this size range, ESP and baghouse collection efficiencies are 85.79-98.6% and 99.54%. Real-time measurement shows that the mass and number concentration of PM10 will be greatly affected by the operating conditions of the PECDs. The number of emitted particles increases with increasing boiler load level because of higher combustion temperature. During test run periods, the data reproducibility is satisfactory.

Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification

PubMed Central

Faye, Laura L.; Machiela, Mitchell J.; Kraft, Peter; Bull, Shelley B.; Sun, Lei

2013-01-01

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. PMID:23950724

Variance Estimation, Design Effects, and Sample Size Calculations for Respondent-Driven Sampling

PubMed Central

2006-01-01

Hidden populations, such as injection drug users and sex workers, are central to a number of public health problems. However, because of the nature of these groups, it is difficult to collect accurate information about them, and this difficulty complicates disease prevention efforts. A recently developed statistical approach called respondent-driven sampling improves our ability to study hidden populations by allowing researchers to make unbiased estimates of the prevalence of certain traits in these populations. Yet, not enough is known about the sample-to-sample variability of these prevalence estimates. In this paper, we present a bootstrap method for constructing confidence intervals around respondent-driven sampling estimates and demonstrate in simulations that it outperforms the naive method currently in use. We also use simulations and real data to estimate the design effects for respondent-driven sampling in a number of situations. We conclude with practical advice about the power calculations that are needed to determine the appropriate sample size for a study using respondent-driven sampling. In general, we recommend a sample size twice as large as would be needed under simple random sampling. PMID:16937083

Methodological issues regarding power of classical test theory (CTT) and item response theory (IRT)-based approaches for the comparison of patient-reported outcomes in two groups of patients - a simulation study

PubMed Central

2010-01-01

Background Patients-Reported Outcomes (PRO) are increasingly used in clinical and epidemiological research. Two main types of analytical strategies can be found for these data: classical test theory (CTT) based on the observed scores and models coming from Item Response Theory (IRT). However, whether IRT or CTT would be the most appropriate method to analyse PRO data remains unknown. The statistical properties of CTT and IRT, regarding power and corresponding effect sizes, were compared. Methods Two-group cross-sectional studies were simulated for the comparison of PRO data using IRT or CTT-based analysis. For IRT, different scenarios were investigated according to whether items or person parameters were assumed to be known, to a certain extent for item parameters, from good to poor precision, or unknown and therefore had to be estimated. The powers obtained with IRT or CTT were compared and parameters having the strongest impact on them were identified. Results When person parameters were assumed to be unknown and items parameters to be either known or not, the power achieved using IRT or CTT were similar and always lower than the expected power using the well-known sample size formula for normally distributed endpoints. The number of items had a substantial impact on power for both methods. Conclusion Without any missing data, IRT and CTT seem to provide comparable power. The classical sample size formula for CTT seems to be adequate under some conditions but is not appropriate for IRT. In IRT, it seems important to take account of the number of items to obtain an accurate formula. PMID:20338031

Is a data set distributed as a power law? A test, with application to gamma-ray burst brightnesses

NASA Technical Reports Server (NTRS)

Wijers, Ralph A. M. J.; Lubin, Lori M.

1994-01-01

We present a method to determine whether an observed sample of data is drawn from a parent distribution that is pure power law. The method starts from a class of statistics which have zero expectation value under the null hypothesis, H(sub 0), that the distribution is a pure power law: F(x) varies as x(exp -alpha). We study one simple member of the class, named the `bending statistic' B, in detail. It is most effective for detection a type of deviation from a power law where the power-law slope varies slowly and monotonically as a function of x. Our estimator of B has a distribution under H(sub 0) that depends only on the size of the sample, not on the parameters of the parent population, and is approximated well by a normal distribution even for modest sample sizes. The bending statistic can therefore be used to test a set of numbers is drawn from any power-law parent population. Since many measurable quantities in astrophysics have distriibutions that are approximately power laws, and since deviations from the ideal power law often provide interesting information about the object of study (e.g., a `bend' or `break' in a luminosity function, a line in an X- or gamma-ray spectrum), we believe that a test of this type will be useful in many different contexts. In the present paper, we apply our test to various subsamples of gamma-ray burst brightness from the first-year Burst and Transient Source Experiment (BATSE) catalog and show that we can only marginally detect the expected steepening of the log (N (greater than C(sub max))) - log (C(sub max)) distribution.

A new study of shower age distribution in near vertical showers by EAS air shower array

NASA Technical Reports Server (NTRS)

Chaudhuri, N.; Basak, D. K.; Goswami, G. C.; Ghosh, B.

1984-01-01

The air shower array has been developed since it started operation in 1931. The array covering an area of 900 sq m now incorporates 21 particle density sampling detectors around two muon magnetic spectrographs. The air showers are detected in the size range 10 to the 4th power to 10 to the 6th power particles. A total of 11000 showers has so far been detected. Average values of shower age have been obtained in various shower size ranges to study the dependence of shower age on shower size. The core distance dependence of shower age parameter has also been analyzed for presentation.

There is More than a Power Law in Zipf

PubMed Central

Cristelli, Matthieu; Batty, Michael; Pietronero, Luciano

2012-01-01

The largest cities, the most frequently used words, the income of the richest countries, and the most wealthy billionaires, can be all described in terms of Zipf’s Law, a rank-size rule capturing the relation between the frequency of a set of objects or events and their size. It is assumed to be one of many manifestations of an underlying power law like Pareto’s or Benford’s, but contrary to popular belief, from a distribution of, say, city sizes and a simple random sampling, one does not obtain Zipf’s law for the largest cities. This pathology is reflected in the fact that Zipf’s Law has a functional form depending on the number of events N. This requires a fundamental property of the sample distribution which we call ‘coherence’ and it corresponds to a ‘screening’ between various elements of the set. We show how it should be accounted for when fitting Zipf’s Law. PMID:23139862

Testing the non-unity of rate ratio under inverse sampling.

PubMed

Tang, Man-Lai; Liao, Yi Jie; Ng, Hong Keung Tony; Chan, Ping Shing

2007-08-01

Inverse sampling is considered to be a more appropriate sampling scheme than the usual binomial sampling scheme when subjects arrive sequentially, when the underlying response of interest is acute, and when maximum likelihood estimators of some epidemiologic indices are undefined. In this article, we study various statistics for testing non-unity rate ratios in case-control studies under inverse sampling. These include the Wald, unconditional score, likelihood ratio and conditional score statistics. Three methods (the asymptotic, conditional exact, and Mid-P methods) are adopted for P-value calculation. We evaluate the performance of different combinations of test statistics and P-value calculation methods in terms of their empirical sizes and powers via Monte Carlo simulation. In general, asymptotic score and conditional score tests are preferable for their actual type I error rates are well controlled around the pre-chosen nominal level, and their powers are comparatively the largest. The exact version of Wald test is recommended if one wants to control the actual type I error rate at or below the pre-chosen nominal level. If larger power is expected and fluctuation of sizes around the pre-chosen nominal level are allowed, then the Mid-P version of Wald test is a desirable alternative. We illustrate the methodologies with a real example from a heart disease study. (c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Performance of Identifiler Direct and PowerPlex 16 HS on the Applied Biosystems 3730 DNA Analyzer for processing biological samples archived on FTA cards.

PubMed

Laurin, Nancy; DeMoors, Anick; Frégeau, Chantal

2012-09-01

Direct amplification of STR loci from biological samples collected on FTA cards without prior DNA purification was evaluated using Identifiler Direct and PowerPlex 16 HS in conjunction with the use of a high throughput Applied Biosystems 3730 DNA Analyzer. In order to reduce the overall sample processing cost, reduced PCR volumes combined with various FTA disk sizes were tested. Optimized STR profiles were obtained using a 0.53 mm disk size in 10 μL PCR volume for both STR systems. These protocols proved effective in generating high quality profiles on the 3730 DNA Analyzer from both blood and buccal FTA samples. Reproducibility, concordance, robustness, sample stability and profile quality were assessed using a collection of blood and buccal samples on FTA cards from volunteer donors as well as from convicted offenders. The new developed protocols offer enhanced throughput capability and cost effectiveness without compromising the robustness and quality of the STR profiles obtained. These results support the use of these protocols for processing convicted offender samples submitted to the National DNA Data Bank of Canada. Similar protocols could be applied to the processing of casework reference samples or in paternity or family relationship testing. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Sample size calculation in economic evaluations.

PubMed

Al, M J; van Hout, B A; Michel, B C; Rutten, F F

1998-06-01

A simulation method is presented for sample size calculation in economic evaluations. As input the method requires: the expected difference and variance of costs and effects, their correlation, the significance level (alpha) and the power of the testing method and the maximum acceptable ratio of incremental effectiveness to incremental costs. The method is illustrated with data from two trials. The first compares primary coronary angioplasty with streptokinase in the treatment of acute myocardial infarction, in the second trial, lansoprazole is compared with omeprazole in the treatment of reflux oesophagitis. These case studies show how the various parameters influence the sample size. Given the large number of parameters that have to be specified in advance, the lack of knowledge about costs and their standard deviation, and the difficulty of specifying the maximum acceptable ratio of incremental effectiveness to incremental costs, the conclusion of the study is that from a technical point of view it is possible to perform a sample size calculation for an economic evaluation, but one should wonder how useful it is.

In-situ detection of micron-sized dust particles in near-Earth space

NASA Technical Reports Server (NTRS)

Gruen, E.; Zook, H. A.

1985-01-01

In situ detectors for micron sized dust particles based on the measurement of impact ionization have been flown on several space missions (Pioneer 8/9, HEOS-2 and Helios 1/2). Previous measurements of small dust particles in near-Earth space are reviewed. An instrument is proposed for the measurement of micron sized meteoroids and space debris such as solid rocket exhaust particles from on board an Earth orbiting satellite. The instrument will measure the mass, speed, flight direction and electrical charge of individually impacting debris and meteoritic particles. It is a multicoincidence detector of 1000 sq cm sensitive area and measures particle masses in the range from 10 to the -14th power g to 10 to the -8th power g at an impact speed of 10 km/s. The instrument is lightweight (5 kg), consumes little power (4 watts), and requires a data sampling rate of about 100 bits per second.

Précis of statistical significance: rationale, validity, and utility.

PubMed

Chow, S L

1998-04-01

The null-hypothesis significance-test procedure (NHSTP) is defended in the context of the theory-corroboration experiment, as well as the following contrasts: (a) substantive hypotheses versus statistical hypotheses, (b) theory corroboration versus statistical hypothesis testing, (c) theoretical inference versus statistical decision, (d) experiments versus nonexperimental studies, and (e) theory corroboration versus treatment assessment. The null hypothesis can be true because it is the hypothesis that errors are randomly distributed in data. Moreover, the null hypothesis is never used as a categorical proposition. Statistical significance means only that chance influences can be excluded as an explanation of data; it does not identify the nonchance factor responsible. The experimental conclusion is drawn with the inductive principle underlying the experimental design. A chain of deductive arguments gives rise to the theoretical conclusion via the experimental conclusion. The anomalous relationship between statistical significance and the effect size often used to criticize NHSTP is more apparent than real. The absolute size of the effect is not an index of evidential support for the substantive hypothesis. Nor is the effect size, by itself, informative as to the practical importance of the research result. Being a conditional probability, statistical power cannot be the a priori probability of statistical significance. The validity of statistical power is debatable because statistical significance is determined with a single sampling distribution of the test statistic based on H0, whereas it takes two distributions to represent statistical power or effect size. Sample size should not be determined in the mechanical manner envisaged in power analysis. It is inappropriate to criticize NHSTP for nonstatistical reasons. At the same time, neither effect size, nor confidence interval estimate, nor posterior probability can be used to exclude chance as an explanation of data. Neither can any of them fulfill the nonstatistical functions expected of them by critics.

as response to seasonal variability

PubMed

Badano, Ernesto I; Labra, Fabio A; Martínez-Pérez, Cecilia G; Vergara, Carlos H

2016-03-01

Ecologists have been largely interested in the description and understanding of the power scaling relationships between body size and abundance of organisms. Many studies have focused on estimating the exponents of these functions across taxonomic groups and spatial scales, to draw inferences about the processes underlying this pattern. The exponents of these functions usually approximate -3/4 at geographical scales, but they deviate from this value when smaller spatial extensions are considered. This has led to propose that body size-abundance relationships at small spatial scales may reflect the impact of environmental changes. This study tests this hypothesis by examining body size spectra of benthic shrimps (Decapoda: Caridea) and snails (Gastropoda) in the Tamiahua lagoon, a brackish body water located in the Eastern coast of Mexico. We mea- sured water quality parameters (dissolved oxygen, salinity, pH, water temperature, sediment organic matter and chemical oxygen demand) and sampled benthic macrofauna during three different climatic conditions of the year (cold, dry and rainy season). Given the small size of most individuals in the benthic macrofaunal samples, we used body volume, instead of weight, to estimate their body size. Body size-abundance relationships of both taxonomic groups were described by tabulating data from each season into base-2 logarithmic body size bins. In both taxonomic groups, observed frequencies per body size class in each season were standardized to yield densities (i.e., individuals/m(3)). Nonlinear regression analyses were separately performed for each taxonomic group at each season to assess whether body size spectra followed power scaling functions. Additionally, for each taxonomic group, multiple regression analyses were used to determine whether these relationships varied among seasons. Our results indicated that, while body size-abundance relationships in both taxonomic groups followed power functions, the parameters defining the shape of these relationships varied among seasons. These variations in the parameters of the body size-abundance relationships seems to be related to changes in the abundance of individuals within the different body size classes, which seems to follow the seasonal changes that occur in the environmental conditions of the lagoon. Thus, we propose that these body size-abundance relation- ships are influenced by the frequency and intensity of environmental changes affecting this ecosystem.

[A Review on the Use of Effect Size in Nursing Research].

PubMed

Kang, Hyuncheol; Yeon, Kyupil; Han, Sang Tae

2015-10-01

The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.

Robust Covariate-Adjusted Log-Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

PubMed Central

Song, Rui; Kosorok, Michael R.; Cai, Jianwen

2009-01-01

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate-adjusted log-rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log-rank tests are robust with respect to different data-generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika 84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate-adjusted log-rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics 39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study. PMID:18162107

Dependence of flux-flow critical frequencies and generalized bundle sizes on distance of fluxoid traversal and fluxoid length in foil samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, J.D.; Joiner, W.C.H.

1979-10-01

Flux-flow noise power spectra taken on Pb/sub 80/In/sub 20/ foils as a function of the orientation of the magnetic field with respect to the sample surfaces are used to study changes in frequencies and bundle sizes as distances of fluxoid traversal and fluxoid lengths change. The results obtained for the frequency dependence of the noise spectra are entirely consistent with our model for flux motion interrupted by pinning centers, provided one makes the reasonable assumption that the distance between pinning centers which a fluxoid may encounter scales inversely with the fluxoid length. The importance of pinning centers in determining themore » noise characteristics is also demonstrated by the way in which subpulse distributions and generalized bundle sizes are altered by changes in the metallurgical structure of the sample. In unannealed samples the dependence of bundle size on magnetic field orientation is controlled by a structural anisotropy, and we find a correlation between large bundle size and the absence of short subpulse times. Annealing removes this anisotropy, and we find a stronger angular variation of bundle size than would be expected using present simplified models.« less

Utility of the Mantel-Haenszel Procedure for Detecting Differential Item Functioning in Small Samples

ERIC Educational Resources Information Center

Fidalgo, Angel M.; Ferreres, Doris; Muniz, Jose

2004-01-01

Sample-size restrictions limit the contingency table approaches based on asymptotic distributions, such as the Mantel-Haenszel (MH) procedure, for detecting differential item functioning (DIF) in many practical applications. Within this framework, the present study investigated the power and Type I error performance of empirical and inferential…

Utilization of Yatagan Power Plant Fly Ash in Production of Building Bricks

NASA Astrophysics Data System (ADS)

Önel, Öznur; Tanriverdi, Mehmet; Cicek, Tayfun

2017-12-01

Fly ash is a by-product of coal combustion, which accumulates in large quantities near the coal-fired power plants as waste material. Fly ash causes serious operational and environmental problems. In this study, fly ash from Yatağgan thermal power plant was used to produce light-weight building bricks. The study aimed to reduce the problems related to fly ash by creating a new area for their use. The optimum process parameters were determined for the production of real size bricks to be used in construction industry. The commercial size bricks (200 × 200 × 90-110 mm) were manufactured using pilot size equipment. Mechanical properties, thermal conductivity coefficients, freezing and thawing strengths, water absorption rates, and unit volume weights of the bricks were determined. Etringite (Ca6Al2 (SO4)3 (OH)12 25(H2O)) and Calcium Silicate Hydrate (2CaO.SiO2.4H2O) were identified as the binding phases in the real size brick samples after 2 days of pre-curing and 28 days curing at 50° C and 95% relative moisture. The water absorption rate was found to be 27.7 % in terms of mass. The mechanical and bending strength of the brick samples with unit volume weight of 1.29 g.cm-3 were determined as 6.75 MPa and 1,56 MPa respectively. The thermal conductivity of the fly ash bricks was measured in average as 0,340 W m-1 K-1. The fly ash sample produced was subjected to toxic leaching tests (Toxic Property Leaching Procedure (EPA-TCLP 1311), Single-step BATCH Test and Method-A Disintegration Procedure (ASTM)). The results of these tests suggested that the materials could be classified as non-hazardous wastes / materials.

Split-plot microarray experiments: issues of design, power and sample size.

PubMed

Tsai, Pi-Wen; Lee, Mei-Ling Ting

2005-01-01

This article focuses on microarray experiments with two or more factors in which treatment combinations of the factors corresponding to the samples paired together onto arrays are not completely random. A main effect of one (or more) factor(s) is confounded with arrays (the experimental blocks). This is called a split-plot microarray experiment. We utilise an analysis of variance (ANOVA) model to assess differentially expressed genes for between-array and within-array comparisons that are generic under a split-plot microarray experiment. Instead of standard t- or F-test statistics that rely on mean square errors of the ANOVA model, we use a robust method, referred to as 'a pooled percentile estimator', to identify genes that are differentially expressed across different treatment conditions. We illustrate the design and analysis of split-plot microarray experiments based on a case application described by Jin et al. A brief discussion of power and sample size for split-plot microarray experiments is also presented.

A systematic approach to designing statistically powerful heteroscedastic 2 × 2 factorial studies while minimizing financial costs.

PubMed

Jan, Show-Li; Shieh, Gwowen

2016-08-31

The 2 × 2 factorial design is widely used for assessing the existence of interaction and the extent of generalizability of two factors where each factor had only two levels. Accordingly, research problems associated with the main effects and interaction effects can be analyzed with the selected linear contrasts. To correct for the potential heterogeneity of variance structure, the Welch-Satterthwaite test is commonly used as an alternative to the t test for detecting the substantive significance of a linear combination of mean effects. This study concerns the optimal allocation of group sizes for the Welch-Satterthwaite test in order to minimize the total cost while maintaining adequate power. The existing method suggests that the optimal ratio of sample sizes is proportional to the ratio of the population standard deviations divided by the square root of the ratio of the unit sampling costs. Instead, a systematic approach using optimization technique and screening search is presented to find the optimal solution. Numerical assessments revealed that the current allocation scheme generally does not give the optimal solution. Alternatively, the suggested approaches to power and sample size calculations give accurate and superior results under various treatment and cost configurations. The proposed approach improves upon the current method in both its methodological soundness and overall performance. Supplementary algorithms are also developed to aid the usefulness and implementation of the recommended technique in planning 2 × 2 factorial designs.

A more powerful test based on ratio distribution for retention noninferiority hypothesis.

PubMed

Deng, Ling; Chen, Gang

2013-03-11

Rothmann et al. ( 2003 ) proposed a method for the statistical inference of fraction retention noninferiority (NI) hypothesis. A fraction retention hypothesis is defined as a ratio of the new treatment effect verse the control effect in the context of a time to event endpoint. One of the major concerns using this method in the design of an NI trial is that with a limited sample size, the power of the study is usually very low. This makes an NI trial not applicable particularly when using time to event endpoint. To improve power, Wang et al. ( 2006 ) proposed a ratio test based on asymptotic normality theory. Under a strong assumption (equal variance of the NI test statistic under null and alternative hypotheses), the sample size using Wang's test was much smaller than that using Rothmann's test. However, in practice, the assumption of equal variance is generally questionable for an NI trial design. This assumption is removed in the ratio test proposed in this article, which is derived directly from a Cauchy-like ratio distribution. In addition, using this method, the fundamental assumption used in Rothmann's test, that the observed control effect is always positive, that is, the observed hazard ratio for placebo over the control is greater than 1, is no longer necessary. Without assuming equal variance under null and alternative hypotheses, the sample size required for an NI trial can be significantly reduced if using the proposed ratio test for a fraction retention NI hypothesis.

A Monte Carlo Study of Levene's Test of Homogeneity of Variance: Empirical Frequencies of Type I Error in Normal Distributions.

ERIC Educational Resources Information Center

Neel, John H.; Stallings, William M.

An influential statistics test recommends a Levene text for homogeneity of variance. A recent note suggests that Levene's test is upwardly biased for small samples. Another report shows inflated Alpha estimates and low power. Neither study utilized more than two sample sizes. This Monte Carlo study involved sampling from a normal population for…

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants.

PubMed

Pierce, Brandon L; Ahsan, Habibul; Vanderweele, Tyler J

2011-06-01

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored. We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic. Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure. The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

Spatial distribution of nymphs of Scaphoideus titanus (Homoptera: Cicadellidae) in grapes, and evaluation of sequential sampling plans.

PubMed

Lessio, Federico; Alma, Alberto

2006-04-01

The spatial distribution of the nymphs of Scaphoideus titanus Ball (Homoptera Cicadellidae), the vector of grapevine flavescence dorée (Candidatus Phytoplasma vitis, 16Sr-V), was studied by applying Taylor's power law. Studies were conducted from 2002 to 2005, in organic and conventional vineyards of Piedmont, northern Italy. Minimum sample size and fixed precision level stop lines were calculated to develop appropriate sampling plans. Model validation was performed, using independent field data, by means of Resampling Validation of Sample Plans (RVSP) resampling software. The nymphal distribution, analyzed via Taylor's power law, was aggregated, with b = 1.49. A sample of 32 plants was adequate at low pest densities with a precision level of D0 = 0.30; but for a more accurate estimate (D0 = 0.10), the required sample size needs to be 292 plants. Green's fixed precision level stop lines seem to be more suitable for field sampling: RVSP simulations of this sampling plan showed precision levels very close to the desired levels. However, at a prefixed precision level of 0.10, sampling would become too time-consuming, whereas a precision level of 0.25 is easily achievable. How these results could influence the correct application of the compulsory control of S. titanus and Flavescence dorée in Italy is discussed.

Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.

PubMed

Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A

2017-06-30

Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Evaluation of a regional monitoring program's statistical power to detect temporal trends in forest health indicators

USGS Publications Warehouse

Perles, Stephanie J.; Wagner, Tyler; Irwin, Brian J.; Manning, Douglas R.; Callahan, Kristina K.; Marshall, Matthew R.

2014-01-01

Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service’s Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of theVital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year−1 for all indicators and is appropriate for detecting a 1 % trend·year−1 in most indicators.

Blinded and unblinded internal pilot study designs for clinical trials with count data.

PubMed

Schneider, Simon; Schmidli, Heinz; Friede, Tim

2013-07-01

Internal pilot studies are a popular design feature to address uncertainties in the sample size calculations caused by vague information on nuisance parameters. Despite their popularity, only very recently blinded sample size reestimation procedures for trials with count data were proposed and their properties systematically investigated. Although blinded procedures are favored by regulatory authorities, practical application is somewhat limited by fears that blinded procedures are prone to bias if the treatment effect was misspecified in the planning. Here, we compare unblinded and blinded procedures with respect to bias, error rates, and sample size distribution. We find that both procedures maintain the desired power and that the unblinded procedure is slightly liberal whereas the actual significance level of the blinded procedure is close to the nominal level. Furthermore, we show that in situations where uncertainty about the assumed treatment effect exists, the blinded estimator of the control event rate is biased in contrast to the unblinded estimator, which results in differences in mean sample sizes in favor of the unblinded procedure. However, these differences are rather small compared to the deviations of the mean sample sizes from the sample size required to detect the true, but unknown effect. We demonstrate that the variation of the sample size resulting from the blinded procedure is in many practically relevant situations considerably smaller than the one of the unblinded procedures. The methods are extended to overdispersed counts using a quasi-likelihood approach and are illustrated by trials in relapsing multiple sclerosis. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sample size requirements for indirect association studies of gene-environment interactions (G x E).

PubMed

Hein, Rebecca; Beckmann, Lars; Chang-Claude, Jenny

2008-04-01

Association studies accounting for gene-environment interactions (G x E) may be useful for detecting genetic effects. Although current technology enables very dense marker spacing in genetic association studies, the true disease variants may not be genotyped. Thus, causal genes are searched for by indirect association using genetic markers in linkage disequilibrium (LD) with the true disease variants. Sample sizes needed to detect G x E effects in indirect case-control association studies depend on the true genetic main effects, disease allele frequencies, whether marker and disease allele frequencies match, LD between loci, main effects and prevalence of environmental exposures, and the magnitude of interactions. We explored variables influencing sample sizes needed to detect G x E, compared these sample sizes with those required to detect genetic marginal effects, and provide an algorithm for power and sample size estimations. Required sample sizes may be heavily inflated if LD between marker and disease loci decreases. More than 10,000 case-control pairs may be required to detect G x E. However, given weak true genetic main effects, moderate prevalence of environmental exposures, as well as strong interactions, G x E effects may be detected with smaller sample sizes than those needed for the detection of genetic marginal effects. Moreover, in this scenario, rare disease variants may only be detectable when G x E is included in the analyses. Thus, the analysis of G x E appears to be an attractive option for the detection of weak genetic main effects of rare variants that may not be detectable in the analysis of genetic marginal effects only.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review.

PubMed

Kristunas, Caroline; Morris, Tom; Gray, Laura

2017-11-15

To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials. Any, not limited to healthcare settings. Any taking part in an SW-CRT published up to March 2016. The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes. Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22-0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed. Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

PubMed

Asimit, Jennifer L; Panoutsopoulou, Kalliope; Wheeler, Eleanor; Berndt, Sonja I; Cordell, Heather J; Morris, Andrew P; Zeggini, Eleftheria; Barroso, Inês

2015-12-01

Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome-wide association study data to identify single-nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P-value threshold. However, P-values do not account for differences in power, whereas Bayes' factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P-values of a decreasing type I error rate as study size increases for single-disease associations. Consequently, the overlap analysis of traits from different-sized studies encounters issues in fair P-value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P-values, particularly in low-power scenarios. Calibration tables between BFs and P-values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P-values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis. © 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Treatment Trials for Neonatal Seizures: The Effect of Design on Sample Size

PubMed Central

Stevenson, Nathan J.; Boylan, Geraldine B.; Hellström-Westas, Lena; Vanhatalo, Sampsa

2016-01-01

Neonatal seizures are common in the neonatal intensive care unit. Clinicians treat these seizures with several anti-epileptic drugs (AEDs) to reduce seizures in a neonate. Current AEDs exhibit sub-optimal efficacy and several randomized control trials (RCT) of novel AEDs are planned. The aim of this study was to measure the influence of trial design on the required sample size of a RCT. We used seizure time courses from 41 term neonates with hypoxic ischaemic encephalopathy to build seizure treatment trial simulations. We used five outcome measures, three AED protocols, eight treatment delays from seizure onset (Td) and four levels of trial AED efficacy to simulate different RCTs. We performed power calculations for each RCT design and analysed the resultant sample size. We also assessed the rate of false positives, or placebo effect, in typical uncontrolled studies. We found that the false positive rate ranged from 5 to 85% of patients depending on RCT design. For controlled trials, the choice of outcome measure had the largest effect on sample size with median differences of 30.7 fold (IQR: 13.7–40.0) across a range of AED protocols, Td and trial AED efficacy (p<0.001). RCTs that compared the trial AED with positive controls required sample sizes with a median fold increase of 3.2 (IQR: 1.9–11.9; p<0.001). Delays in AED administration from seizure onset also increased the required sample size 2.1 fold (IQR: 1.7–2.9; p<0.001). Subgroup analysis showed that RCTs in neonates treated with hypothermia required a median fold increase in sample size of 2.6 (IQR: 2.4–3.0) compared to trials in normothermic neonates (p<0.001). These results show that RCT design has a profound influence on the required sample size. Trials that use a control group, appropriate outcome measure, and control for differences in Td between groups in analysis will be valid and minimise sample size. PMID:27824913

Multiple high-intensity focused ultrasound probes for kidney-tissue ablation.

PubMed

Häcker, Axel; Chauhan, Sunita; Peters, Kristina; Hildenbrand, Ralf; Marlinghaus, Ernst; Alken, Peter; Michel, Maurice Stephan

2005-10-01

To investigate kidney-tissue ablation by high-intensity focused ultrasound (HIFU) using multiple and single probes. Ultrasound beams (1.75 MHz) produced by a piezoceramic element (focal distance 80 mm) were focused at the center of renal parenchyma. One of the three probes (mounted on a jig) could also be used for comparison with a single probe at comparable power ratings. Lesion dimensions were examined in perfused and unperfused ex vivo porcine kidneys at different power levels (40, 60, and 80 W) and treatment times (4, 6, and 8 seconds). At identical power levels, the lesions induced by multiple probes were larger than those induced by a single probe. Lesion size increased with increasing pulse duration and generator power. The sizes and shapes of the lesions were predictably repeatable in all samples. Lesions in perfused kidneys were smaller than those in unperfused kidneys. Ex vivo, kidney-tissue ablation by means of multiple HIFU probes offers significant advantages over single HIFU probes in respect of lesion size and formation. These advantages need to be confirmed by tests in vivo at higher energy levels.

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

Steve Verrill; Victoria L. Herian; David W. Green

2002-01-01

Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random...

Performance of Bootstrapping Approaches To Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling.

ERIC Educational Resources Information Center

Nevitt, Jonathan; Hancock, Gregory R.

2001-01-01

Evaluated the bootstrap method under varying conditions of nonnormality, sample size, model specification, and number of bootstrap samples drawn from the resampling space. Results for the bootstrap suggest the resampling-based method may be conservative in its control over model rejections, thus having an impact on the statistical power associated…

High-Field Liquid-State Dynamic Nuclear Polarization in Microliter Samples.

PubMed

Yoon, Dongyoung; Dimitriadis, Alexandros I; Soundararajan, Murari; Caspers, Christian; Genoud, Jeremy; Alberti, Stefano; de Rijk, Emile; Ansermet, Jean-Philippe

2018-05-01

Nuclear hyperpolarization in the liquid state by dynamic nuclear polarization (DNP) has been of great interest because of its potential use in NMR spectroscopy of small samples of biological and chemical compounds in aqueous media. Liquid state DNP generally requires microwave resonators in order to generate an alternating magnetic field strong enough to saturate electron spins in the solution. As a consequence, the sample size is limited to dimensions of the order of the wavelength, and this restricts the sample volume to less than 100 nL for DNP at 9 T (∼260 GHz). We show here a new approach that overcomes this sample size limitation. Large saturation of electron spins was obtained with a high-power (∼150 W) gyrotron without microwave resonators. Since high power microwaves can cause serious dielectric heating in polar solutions, we designed a planar probe which effectively alleviates dielectric heating. A thin liquid sample of 100 μm of thickness is placed on a block of high thermal conductivity aluminum nitride, with a gold coating that serves both as a ground plane and as a heat sink. A meander or a coil were used for NMR. We performed 1 H DNP at 9.2 T (∼260 GHz) and at room temperature with 10 μL of water, a volume that is more than 100× larger than reported so far. The 1 H NMR signal is enhanced by a factor of about -10 with 70 W of microwave power. We also demonstrated the liquid state of 31 P DNP in fluorobenzene containing triphenylphosphine and obtained an enhancement of ∼200.

Necessary conditions for superior thermoelectric power of Si/Au artificial superlattice thin-film

NASA Astrophysics Data System (ADS)

Okamoto, Yoichi; Watanabe, Shin; Miyazaki, Hisashi; Morimoto, Jun

2018-03-01

The Si-Ge-Au ternary artificial superlattice thin-films showed superior thermoelectric power with low reproducibility. Superior thermoelectric power was only generated, when nanocrystals existed. Therefore, the origin of superior thermoelectric power was considered to be the quantum size effect of nanocrystals. However, even with the presence of nanocrystals, superior thermoelectric power was often not generated. In order to investigate the generation conditions of superior thermoelectric power in more detail, the samples were simplified to Si-Au binary artificial superlattice samples. Furthermore, annealings were carried out under conditions where nanocrystals were likely to be formed. From the results of Raman scattering spectroscopy and X-ray diffraction (XRD) analysis, the diameter of nanocrystals and the spacing between nanocrystals were calculated with an isotropic three-dimensional mosaic model. It was found that superior thermoelectric power was generated only when the diameter of nanocrystals was 11 nm or less and the spacing between nanocrystals was 3 nm or less.

Statistical power for nonequivalent pretest-posttest designs. The impact of change-score versus ANCOVA models.

PubMed

Oakes, J M; Feldman, H A

2001-02-01

Nonequivalent controlled pretest-posttest designs are central to evaluation science, yet no practical and unified approach for estimating power in the two most widely used analytic approaches to these designs exists. This article fills the gap by presenting and comparing useful, unified power formulas for ANCOVA and change-score analyses, indicating the implications of each on sample-size requirements. The authors close with practical recommendations for evaluators. Mathematical details and a simple spreadsheet approach are included in appendices.

The decline and fall of Type II error rates

Treesearch

Steve Verrill; Mark Durst

2005-01-01

For general linear models with normally distributed random errors, the probability of a Type II error decreases exponentially as a function of sample size. This potentially rapid decline reemphasizes the importance of performing power calculations.

Homogeneity tests of clustered diagnostic markers with applications to the BioCycle Study

PubMed Central

Tang, Liansheng Larry; Liu, Aiyi; Schisterman, Enrique F.; Zhou, Xiao-Hua; Liu, Catherine Chun-ling

2014-01-01

Diagnostic trials often require the use of a homogeneity test among several markers. Such a test may be necessary to determine the power both during the design phase and in the initial analysis stage. However, no formal method is available for the power and sample size calculation when the number of markers is greater than two and marker measurements are clustered in subjects. This article presents two procedures for testing the accuracy among clustered diagnostic markers. The first procedure is a test of homogeneity among continuous markers based on a global null hypothesis of the same accuracy. The result under the alternative provides the explicit distribution for the power and sample size calculation. The second procedure is a simultaneous pairwise comparison test based on weighted areas under the receiver operating characteristic curves. This test is particularly useful if a global difference among markers is found by the homogeneity test. We apply our procedures to the BioCycle Study designed to assess and compare the accuracy of hormone and oxidative stress markers in distinguishing women with ovulatory menstrual cycles from those without. PMID:22733707

Detecting a Weak Association by Testing its Multiple Perturbations: a Data Mining Approach

NASA Astrophysics Data System (ADS)

Lo, Min-Tzu; Lee, Wen-Chung

2014-05-01

Many risk factors/interventions in epidemiologic/biomedical studies are of minuscule effects. To detect such weak associations, one needs a study with a very large sample size (the number of subjects, n). The n of a study can be increased but unfortunately only to an extent. Here, we propose a novel method which hinges on increasing sample size in a different direction-the total number of variables (p). We construct a p-based `multiple perturbation test', and conduct power calculations and computer simulations to show that it can achieve a very high power to detect weak associations when p can be made very large. As a demonstration, we apply the method to analyze a genome-wide association study on age-related macular degeneration and identify two novel genetic variants that are significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical tests.

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Acute Respiratory Distress Syndrome Measurement Error. Potential Effect on Clinical Study Results

PubMed Central

Cooke, Colin R.; Iwashyna, Theodore J.; Hofer, Timothy P.

2016-01-01

Rationale: Identifying patients with acute respiratory distress syndrome (ARDS) is a recognized challenge. Experts often have only moderate agreement when applying the clinical definition of ARDS to patients. However, no study has fully examined the implications of low reliability measurement of ARDS on clinical studies. Objectives: To investigate how the degree of variability in ARDS measurement commonly reported in clinical studies affects study power, the accuracy of treatment effect estimates, and the measured strength of risk factor associations. Methods: We examined the effect of ARDS measurement error in randomized clinical trials (RCTs) of ARDS-specific treatments and cohort studies using simulations. We varied the reliability of ARDS diagnosis, quantified as the interobserver reliability (κ-statistic) between two reviewers. In RCT simulations, patients identified as having ARDS were enrolled, and when measurement error was present, patients without ARDS could be enrolled. In cohort studies, risk factors as potential predictors were analyzed using reviewer-identified ARDS as the outcome variable. Measurements and Main Results: Lower reliability measurement of ARDS during patient enrollment in RCTs seriously degraded study power. Holding effect size constant, the sample size necessary to attain adequate statistical power increased by more than 50% as reliability declined, although the result was sensitive to ARDS prevalence. In a 1,400-patient clinical trial, the sample size necessary to maintain similar statistical power increased to over 1,900 when reliability declined from perfect to substantial (κ = 0.72). Lower reliability measurement diminished the apparent effectiveness of an ARDS-specific treatment from a 15.2% (95% confidence interval, 9.4–20.9%) absolute risk reduction in mortality to 10.9% (95% confidence interval, 4.7–16.2%) when reliability declined to moderate (κ = 0.51). In cohort studies, the effect on risk factor associations was similar. Conclusions: ARDS measurement error can seriously degrade statistical power and effect size estimates of clinical studies. The reliability of ARDS measurement warrants careful attention in future ARDS clinical studies. PMID:27159648

Sample size determination for disease prevalence studies with partially validated data.

PubMed

Qiu, Shi-Fang; Poon, Wai-Yin; Tang, Man-Lai

2016-02-01

Disease prevalence is an important topic in medical research, and its study is based on data that are obtained by classifying subjects according to whether a disease has been contracted. Classification can be conducted with high-cost gold standard tests or low-cost screening tests, but the latter are subject to the misclassification of subjects. As a compromise between the two, many research studies use partially validated datasets in which all data points are classified by fallible tests, and some of the data points are validated in the sense that they are also classified by the completely accurate gold-standard test. In this article, we investigate the determination of sample sizes for disease prevalence studies with partially validated data. We use two approaches. The first is to find sample sizes that can achieve a pre-specified power of a statistical test at a chosen significance level, and the second is to find sample sizes that can control the width of a confidence interval with a pre-specified confidence level. Empirical studies have been conducted to demonstrate the performance of various testing procedures with the proposed sample sizes. The applicability of the proposed methods are illustrated by a real-data example. © The Author(s) 2012.

Heavy metals in the finest size fractions of road-deposited sediments.

PubMed

Lanzerstorfer, Christof

2018-08-01

The concentration of heavy metals in urban road-deposited sediments (RDS) can be used as an indicator for environmental pollution. Thus, their occurrence has been studied in whole road dust samples as well as in size fractions obtained by sieving. Because of the limitations of size separation by sieving little information is available about heavy metal concentrations in the road dust size fractions <20 μm. In this study air classification was applied for separation of dust size fractions smaller than 20 μm from RDS collected at different times during the year. The results showed only small seasonal variations in the heavy metals concentrations and size distribution. According to the Geoaccumulation Index the pollution of the road dust samples deceased in the following order: Sb » As > Cu ≈ Zn > Cr > Cd ≈ Pb ≈ Mn > Ni > Co ≈ V. For all heavy metals the concentration was higher in the fine size fractions compared to the coarse size fractions, while the concentration of Sr was size-independent. The enrichment of the heavy metals in the finest size fraction compared to the whole RDS <200 μm was up to 4.5-fold. The size dependence of the concentration decreased in the following order: Co ≈ Cd > Sb > (Cu) ≈ Zn ≈ Pb > As ≈ V » Mn. The approximation of the size dependence of the concentration as a function of the particle size by power functions worked very well. The correlation between particle size and concentration was high for all heavy metals. The increased heavy metals concentrations in the finest size fractions should be considered in the evaluation of the contribution of road dust re-suspension to the heavy metal contamination of atmospheric dust. Thereby, power functions can be used to describe the size dependence of the concentration. Copyright © 2018 Elsevier Ltd. All rights reserved.

Re-estimating sample size in cluster randomised trials with active recruitment within clusters.

PubMed

van Schie, S; Moerbeek, M

2014-08-30

Often only a limited number of clusters can be obtained in cluster randomised trials, although many potential participants can be recruited within each cluster. Thus, active recruitment is feasible within the clusters. To obtain an efficient sample size in a cluster randomised trial, the cluster level and individual level variance should be known before the study starts, but this is often not the case. We suggest using an internal pilot study design to address this problem of unknown variances. A pilot can be useful to re-estimate the variances and re-calculate the sample size during the trial. Using simulated data, it is shown that an initially low or high power can be adjusted using an internal pilot with the type I error rate remaining within an acceptable range. The intracluster correlation coefficient can be re-estimated with more precision, which has a positive effect on the sample size. We conclude that an internal pilot study design may be used if active recruitment is feasible within a limited number of clusters. Copyright © 2014 John Wiley & Sons, Ltd.

Discovering human germ cell mutagens with whole genome sequencing: Insights from power calculations reveal the importance of controlling for between-family variability.

PubMed

Webster, R J; Williams, A; Marchetti, F; Yauk, C L

2018-07-01

Mutations in germ cells pose potential genetic risks to offspring. However, de novo mutations are rare events that are spread across the genome and are difficult to detect. Thus, studies in this area have generally been under-powered, and no human germ cell mutagen has been identified. Whole Genome Sequencing (WGS) of human pedigrees has been proposed as an approach to overcome these technical and statistical challenges. WGS enables analysis of a much wider breadth of the genome than traditional approaches. Here, we performed power analyses to determine the feasibility of using WGS in human families to identify germ cell mutagens. Different statistical models were compared in the power analyses (ANOVA and multiple regression for one-child families, and mixed effect model sampling between two to four siblings per family). Assumptions were made based on parameters from the existing literature, such as the mutation-by-paternal age effect. We explored two scenarios: a constant effect due to an exposure that occurred in the past, and an accumulating effect where the exposure is continuing. Our analysis revealed the importance of modeling inter-family variability of the mutation-by-paternal age effect. Statistical power was improved by models accounting for the family-to-family variability. Our power analyses suggest that sufficient statistical power can be attained with 4-28 four-sibling families per treatment group, when the increase in mutations ranges from 40 to 10% respectively. Modeling family variability using mixed effect models provided a reduction in sample size compared to a multiple regression approach. Much larger sample sizes were required to detect an interaction effect between environmental exposures and paternal age. These findings inform study design and statistical modeling approaches to improve power and reduce sequencing costs for future studies in this area. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Internal pilots for a class of linear mixed models with Gaussian and compound symmetric data

PubMed Central

Gurka, Matthew J.; Coffey, Christopher S.; Muller, Keith E.

2015-01-01

SUMMARY An internal pilot design uses interim sample size analysis, without interim data analysis, to adjust the final number of observations. The approach helps to choose a sample size sufficiently large (to achieve the statistical power desired), but not too large (which would waste money and time). We report on recent research in cerebral vascular tortuosity (curvature in three dimensions) which would benefit greatly from internal pilots due to uncertainty in the parameters of the covariance matrix used for study planning. Unfortunately, observations correlated across the four regions of the brain and small sample sizes preclude using existing methods. However, as in a wide range of medical imaging studies, tortuosity data have no missing or mistimed data, a factorial within-subject design, the same between-subject design for all responses, and a Gaussian distribution with compound symmetry. For such restricted models, we extend exact, small sample univariate methods for internal pilots to linear mixed models with any between-subject design (not just two groups). Planning a new tortuosity study illustrates how the new methods help to avoid sample sizes that are too small or too large while still controlling the type I error rate. PMID:17318914

Mineralogy and characterization of deposited particles of the aero sediments collected in the vicinity of power plants and the open pit coal mine: Kolubara (Serbia).

PubMed

Cvetković, Željko; Logar, Mihovil; Rosić, Aleksandra

2013-05-01

In this paper, particular attention was paid to the presence of aerosol solid particles, which occurred mainly as a result of exploitation and coal combustion in the thermal power plants of the Kolubara basin. Not all of the particles created by this type of anthropogenic pollution have an equal impact on human health, but it largely depends on their size and shape. The mineralogical composition and particle size distribution in the samples of aero sediments were defined. The samples were collected close to the power plant and open pit coal mine, in the winter and summer period during the year 2007. The sampling was performed by using precipitators placed in eight locations within the territory of the Lazarevac municipality. In order to characterize the sedimentary particles, several methods were applied: microscopy, SEM-EDX and X-ray powder diffraction. The concentration of aero sediments was also determined during the test period. Variety in the mineralogical composition and particle size depends on the position of the measuring sites, geology of the locations, the annual period of collecting as well as possible interactions. By applying the mentioned methods, the presence of inhalational and respiratory particles variously distributed in the winter and in the summer period was established. The most common minerals are quartz and feldspar. The presence of gypsum, clay minerals, calcite and dolomite as secondary minerals was determined, as well as the participation of organic and inorganic amorphic matter. The presence of quartz as a toxic mineral has a particular impact on human health.

Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis.

PubMed

Oulhaj, Abderrahim; El Ghouch, Anouar; Holman, Rury R

2017-01-01

Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease.

Design, analysis and presentation of factorial randomised controlled trials

PubMed Central

Montgomery, Alan A; Peters, Tim J; Little, Paul

2003-01-01

Background The evaluation of more than one intervention in the same randomised controlled trial can be achieved using a parallel group design. However this requires increased sample size and can be inefficient, especially if there is also interest in considering combinations of the interventions. An alternative may be a factorial trial, where for two interventions participants are allocated to receive neither intervention, one or the other, or both. Factorial trials require special considerations, however, particularly at the design and analysis stages. Discussion Using a 2 × 2 factorial trial as an example, we present a number of issues that should be considered when planning a factorial trial. The main design issue is that of sample size. Factorial trials are most often powered to detect the main effects of interventions, since adequate power to detect plausible interactions requires greatly increased sample sizes. The main analytical issues relate to the investigation of main effects and the interaction between the interventions in appropriate regression models. Presentation of results should reflect the analytical strategy with an emphasis on the principal research questions. We also give an example of how baseline and follow-up data should be presented. Lastly, we discuss the implications of the design, analytical and presentational issues covered. Summary Difficulties in interpreting the results of factorial trials if an influential interaction is observed is the cost of the potential for efficient, simultaneous consideration of two or more interventions. Factorial trials can in principle be designed to have adequate power to detect realistic interactions, and in any case they are the only design that allows such effects to be investigated. PMID:14633287

Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation

NASA Astrophysics Data System (ADS)

Michalkiewicz, Mateusz; Pai, Akshay; Leung, Kelvin K.; Sommer, Stefan; Darkner, Sune; Sørensen, Lauge; Sporring, Jon; Nielsen, Mads

2016-03-01

Brain atrophy from structural magnetic resonance images (MRIs) is widely used as an imaging surrogate marker for Alzheimers disease. Their utility has been limited due to the large degree of variance and subsequently high sample size estimates. The only consistent and reasonably powerful atrophy estimation methods has been the boundary shift integral (BSI). In this paper, we first propose a tensor-based morphometry (TBM) method to measure voxel-wise atrophy that we combine with BSI. The combined model decreases the sample size estimates significantly when compared to BSI and TBM alone.

Breast Reference Set Application: Chris Li-FHCRC (2014) — EDRN Public Portal

Cancer.gov

This application proposes to use Reference Set #1. We request access to serum samples collected at the time of breast biopsy from subjects with IC (n=30) or benign disease without atypia (n=30). Statistical power: With 30 BC cases and 30 normal controls, a 25% difference in mean metabolite levels can be detected between groups with 80% power and α=0.05, assuming coefficients of variation of 30%, consistent with our past studies. These sample sizes appear sufficient to enable detection of changes similar in magnitude to those previously reported in pre-clinical (BC recurrence) specimens (20).

Adjustable Nyquist-rate System for Single-Bit Sigma-Delta ADC with Alternative FIR Architecture

NASA Astrophysics Data System (ADS)

Frick, Vincent; Dadouche, Foudil; Berviller, Hervé

2016-09-01

This paper presents a new smart and compact system dedicated to control the output sampling frequency of an analogue-to-digital converters (ADC) based on single-bit sigma-delta (ΣΔ) modulator. This system dramatically improves the spectral analysis capabilities of power network analysers (power meters) by adjusting the ADC's sampling frequency to the input signal's fundamental frequency with a few parts per million accuracy. The trade-off between straightforwardness and performance that motivated the choice of the ADC's architecture are preliminary discussed. It particularly comes along with design considerations of an ultra-steep direct-form FIR that is optimised in terms of size and operating speed. Thanks to compact standard VHDL language description, the architecture of the proposed system is particularly suitable for application-specific integrated circuit (ASIC) implementation-oriented low-power and low-cost power meter applications. Field programmable gate array (FPGA) prototyping and experimental results validate the adjustable sampling frequency concept. They also show that the system can perform better in terms of implementation and power capabilities compared to dedicated IP resources.

Controlling the type I error rate in two-stage sequential adaptive designs when testing for average bioequivalence.

PubMed

Maurer, Willi; Jones, Byron; Chen, Ying

2018-05-10

In a 2×2 crossover trial for establishing average bioequivalence (ABE) of a generic agent and a currently marketed drug, the recommended approach to hypothesis testing is the two one-sided test (TOST) procedure, which depends, among other things, on the estimated within-subject variability. The power of this procedure, and therefore the sample size required to achieve a minimum power, depends on having a good estimate of this variability. When there is uncertainty, it is advisable to plan the design in two stages, with an interim sample size reestimation after the first stage, using an interim estimate of the within-subject variability. One method and 3 variations of doing this were proposed by Potvin et al. Using simulation, the operating characteristics, including the empirical type I error rate, of the 4 variations (called Methods A, B, C, and D) were assessed by Potvin et al and Methods B and C were recommended. However, none of these 4 variations formally controls the type I error rate of falsely claiming ABE, even though the amount of inflation produced by Method C was considered acceptable. A major disadvantage of assessing type I error rate inflation using simulation is that unless all possible scenarios for the intended design and analysis are investigated, it is impossible to be sure that the type I error rate is controlled. Here, we propose an alternative, principled method of sample size reestimation that is guaranteed to control the type I error rate at any given significance level. This method uses a new version of the inverse-normal combination of p-values test, in conjunction with standard group sequential techniques, that is more robust to large deviations in initial assumptions regarding the variability of the pharmacokinetic endpoints. The sample size reestimation step is based on significance levels and power requirements that are conditional on the first-stage results. This necessitates a discussion and exploitation of the peculiar properties of the power curve of the TOST testing procedure. We illustrate our approach with an example based on a real ABE study and compare the operating characteristics of our proposed method with those of Method B of Povin et al. Copyright © 2018 John Wiley & Sons, Ltd.

The power and promise of RNA-seq in ecology and evolution.

PubMed

Todd, Erica V; Black, Michael A; Gemmell, Neil J

2016-03-01

Reference is regularly made to the power of new genomic sequencing approaches. Using powerful technology, however, is not the same as having the necessary power to address a research question with statistical robustness. In the rush to adopt new and improved genomic research methods, limitations of technology and experimental design may be initially neglected. Here, we review these issues with regard to RNA sequencing (RNA-seq). RNA-seq adds large-scale transcriptomics to the toolkit of ecological and evolutionary biologists, enabling differential gene expression (DE) studies in nonmodel species without the need for prior genomic resources. High biological variance is typical of field-based gene expression studies and means that larger sample sizes are often needed to achieve the same degree of statistical power as clinical studies based on data from cell lines or inbred animal models. Sequencing costs have plummeted, yet RNA-seq studies still underutilize biological replication. Finite research budgets force a trade-off between sequencing effort and replication in RNA-seq experimental design. However, clear guidelines for negotiating this trade-off, while taking into account study-specific factors affecting power, are currently lacking. Study designs that prioritize sequencing depth over replication fail to capitalize on the power of RNA-seq technology for DE inference. Significant recent research effort has gone into developing statistical frameworks and software tools for power analysis and sample size calculation in the context of RNA-seq DE analysis. We synthesize progress in this area and derive an accessible rule-of-thumb guide for designing powerful RNA-seq experiments relevant in eco-evolutionary and clinical settings alike. © 2016 John Wiley & Sons Ltd.

Carbon nanotube scaffolds with controlled porosity as electromagnetic absorbing materials in the gigahertz range

NASA Astrophysics Data System (ADS)

González, M.; Crespo, M.; Baselga, J.; Pozuelo, J.

2016-05-01

Control of the microscopic structure of CNT nanocomposites allows modulation of the electromagnetic shielding in the gigahertz range. The porosity of CNT scaffolds has been controlled by two freezing protocols and a subsequent lyophilization step: fast freezing in liquid nitrogen and slow freezing at -20 °C. Mercury porosimetry shows that slowly frozen specimens present a more open pore size (100-150 μm) with a narrow distribution whereas specimens frozen rapidly show a smaller pore size and a heterogeneous distribution. 3D-scaffolds containing 3, 4, 6 and 7% CNT were infiltrated with epoxy and specimens with 2, 5 and 8 mm thicknesses were characterized in the GHz range. Samples with the highest pore size and porosity presented the lowest reflected power (about 30%) and the highest absorbed power (about 70%), which allows considering them as electromagnetic radiation absorbing materials.Control of the microscopic structure of CNT nanocomposites allows modulation of the electromagnetic shielding in the gigahertz range. The porosity of CNT scaffolds has been controlled by two freezing protocols and a subsequent lyophilization step: fast freezing in liquid nitrogen and slow freezing at -20 °C. Mercury porosimetry shows that slowly frozen specimens present a more open pore size (100-150 μm) with a narrow distribution whereas specimens frozen rapidly show a smaller pore size and a heterogeneous distribution. 3D-scaffolds containing 3, 4, 6 and 7% CNT were infiltrated with epoxy and specimens with 2, 5 and 8 mm thicknesses were characterized in the GHz range. Samples with the highest pore size and porosity presented the lowest reflected power (about 30%) and the highest absorbed power (about 70%), which allows considering them as electromagnetic radiation absorbing materials. Electronic supplementary information (ESI) available: Scheme of hydrogenated derivative of diglycidyl ether of bisphenol-A (HDGEBA) and m-xylylenediamine; X-ray diffractograms of pristine CNT and oxidized CNT; glass transition temperatures of composites; electromagnetic shielding analysis in the 1-18 GHz frequency range. See DOI: 10.1039/c6nr02133f

Estimation of sample size and testing power (Part 3).

PubMed

Hu, Liang-ping; Bao, Xiao-lei; Guan, Xue; Zhou, Shi-guo

2011-12-01

This article introduces the definition and sample size estimation of three special tests (namely, non-inferiority test, equivalence test and superiority test) for qualitative data with the design of one factor with two levels having a binary response variable. Non-inferiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is not clinically inferior to that of the positive control drug. Equivalence test refers to the research design of which the objective is to verify that the experimental drug and the control drug have clinically equivalent efficacy. Superiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is clinically superior to that of the control drug. By specific examples, this article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.

A field instrument for quantitative determination of beryllium by activation analysis

USGS Publications Warehouse

Vaughn, William W.; Wilson, E.E.; Ohm, J.M.

1960-01-01

A low-cost instrument has been developed for quantitative determinations of beryllium in the field by activation analysis. The instrument makes use of the gamma-neutron reaction between gammas emitted by an artificially radioactive source (Sb124) and beryllium as it occurs in nature. The instrument and power source are mounted in a panel-type vehicle. Samples are prepared by hand-crushing the rock to approximately ?-inch mesh size and smaller. Sample volumes are kept constant by means of a standard measuring cup. Instrument calibration, made by using standards of known BeO content, indicates the analyses are reproducible and accurate to within ? 0.25 percent BeO in the range from 1 to 20 percent BeO with a sample counting time of 5 minutes. Sensitivity of the instrument maybe increased somewhat by increasing the source size, the sample size, or by enlarging the cross-sectional area of the neutron-sensitive phosphor normal to the neutron flux.

Powerful Statistical Inference for Nested Data Using Sufficient Summary Statistics

PubMed Central

Dowding, Irene; Haufe, Stefan

2018-01-01

Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not hold for such data, two important problems are to accurately estimate group-level effect sizes, and to obtain powerful statistical tests against group-level null hypotheses. A common approach is to summarize subject-level data by a single quantity per subject, which is often the mean or the difference between class means, and treat these as samples in a group-level t-test. This “naive” approach is, however, suboptimal in terms of statistical power, as it ignores information about the intra-subject variance. To address this issue, we review several approaches to deal with nested data, with a focus on methods that are easy to implement. With what we call the sufficient-summary-statistic approach, we highlight a computationally efficient technique that can improve statistical power by taking into account within-subject variances, and we provide step-by-step instructions on how to apply this approach to a number of frequently-used measures of effect size. The properties of the reviewed approaches and the potential benefits over a group-level t-test are quantitatively assessed on simulated data and demonstrated on EEG data from a simulated-driving experiment. PMID:29615885

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Systematic review finds major deficiencies in sample size methodology and reporting for stepped-wedge cluster randomised trials

PubMed Central

Martin, James; Taljaard, Monica; Girling, Alan; Hemming, Karla

2016-01-01

Background Stepped-wedge cluster randomised trials (SW-CRT) are increasingly being used in health policy and services research, but unless they are conducted and reported to the highest methodological standards, they are unlikely to be useful to decision-makers. Sample size calculations for these designs require allowance for clustering, time effects and repeated measures. Methods We carried out a methodological review of SW-CRTs up to October 2014. We assessed adherence to reporting each of the 9 sample size calculation items recommended in the 2012 extension of the CONSORT statement to cluster trials. Results We identified 32 completed trials and 28 independent protocols published between 1987 and 2014. Of these, 45 (75%) reported a sample size calculation, with a median of 5.0 (IQR 2.5–6.0) of the 9 CONSORT items reported. Of those that reported a sample size calculation, the majority, 33 (73%), allowed for clustering, but just 15 (33%) allowed for time effects. There was a small increase in the proportions reporting a sample size calculation (from 64% before to 84% after publication of the CONSORT extension, p=0.07). The type of design (cohort or cross-sectional) was not reported clearly in the majority of studies, but cohort designs seemed to be most prevalent. Sample size calculations in cohort designs were particularly poor with only 3 out of 24 (13%) of these studies allowing for repeated measures. Discussion The quality of reporting of sample size items in stepped-wedge trials is suboptimal. There is an urgent need for dissemination of the appropriate guidelines for reporting and methodological development to match the proliferation of the use of this design in practice. Time effects and repeated measures should be considered in all SW-CRT power calculations, and there should be clarity in reporting trials as cohort or cross-sectional designs. PMID:26846897

Methodological issues with adaptation of clinical trial design.

PubMed

Hung, H M James; Wang, Sue-Jane; O'Neill, Robert T

2006-01-01

Adaptation of clinical trial design generates many issues that have not been resolved for practical applications, though statistical methodology has advanced greatly. This paper focuses on some methodological issues. In one type of adaptation such as sample size re-estimation, only the postulated value of a parameter for planning the trial size may be altered. In another type, the originally intended hypothesis for testing may be modified using the internal data accumulated at an interim time of the trial, such as changing the primary endpoint and dropping a treatment arm. For sample size re-estimation, we make a contrast between an adaptive test weighting the two-stage test statistics with the statistical information given by the original design and the original sample mean test with a properly corrected critical value. We point out the difficulty in planning a confirmatory trial based on the crude information generated by exploratory trials. In regards to selecting a primary endpoint, we argue that the selection process that allows switching from one endpoint to the other with the internal data of the trial is not very likely to gain a power advantage over the simple process of selecting one from the two endpoints by testing them with an equal split of alpha (Bonferroni adjustment). For dropping a treatment arm, distributing the remaining sample size of the discontinued arm to other treatment arms can substantially improve the statistical power of identifying a superior treatment arm in the design. A common difficult methodological issue is that of how to select an adaptation rule in the trial planning stage. Pre-specification of the adaptation rule is important for the practicality consideration. Changing the originally intended hypothesis for testing with the internal data generates great concerns to clinical trial researchers.

Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern

USGS Publications Warehouse

Landguth, Erin L.; Gedy, Bradley C.; Oyler-McCance, Sara J.; Garey, Andrew L.; Emel, Sarah L.; Mumma, Matthew; Wagner, Helene H.; Fortin, Marie-Josée; Cushman, Samuel A.

2012-01-01

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals.

Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern

USGS Publications Warehouse

Landguth, E.L.; Fedy, B.C.; Oyler-McCance, S.J.; Garey, A.L.; Emel, S.L.; Mumma, M.; Wagner, H.H.; Fortin, M.-J.; Cushman, S.A.

2012-01-01

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals. ?? 2011 Blackwell Publishing Ltd.

Improving Aquatic Warbler Population Assessments by Accounting for Imperfect Detection

PubMed Central

Oppel, Steffen; Marczakiewicz, Piotr; Lachmann, Lars; Grzywaczewski, Grzegorz

2014-01-01

Monitoring programs designed to assess changes in population size over time need to account for imperfect detection and provide estimates of precision around annual abundance estimates. Especially for species dependent on conservation management, robust monitoring is essential to evaluate the effectiveness of management. Many bird species of temperate grasslands depend on specific conservation management to maintain suitable breeding habitat. One such species is the Aquatic Warbler (Acrocephalus paludicola), which breeds in open fen mires in Central Europe. Aquatic Warbler populations have so far been assessed using a complete survey that aims to enumerate all singing males over a large area. Because this approach provides no estimate of precision and does not account for observation error, detecting moderate population changes is challenging. From 2011 to 2013 we trialled a new line transect sampling monitoring design in the Biebrza valley, Poland, to estimate abundance of singing male Aquatic Warblers. We surveyed Aquatic Warblers repeatedly along 50 randomly placed 1-km transects, and used binomial mixture models to estimate abundances per transect. The repeated line transect sampling required 150 observer days, and thus less effort than the traditional ‘full count’ approach (175 observer days). Aquatic Warbler abundance was highest at intermediate water levels, and detection probability varied between years and was influenced by vegetation height. A power analysis indicated that our line transect sampling design had a power of 68% to detect a 20% population change over 10 years, whereas raw count data had a 9% power to detect the same trend. Thus, by accounting for imperfect detection we increased the power to detect population changes. We recommend to adopt the repeated line transect sampling approach for monitoring Aquatic Warblers in Poland and in other important breeding areas to monitor changes in population size and the effects of habitat management. PMID:24713994

Efficient design and inference for multistage randomized trials of individualized treatment policies.

PubMed

Dawson, Ree; Lavori, Philip W

2012-01-01

Clinical demand for individualized "adaptive" treatment policies in diverse fields has spawned development of clinical trial methodology for their experimental evaluation via multistage designs, building upon methods intended for the analysis of naturalistically observed strategies. Because often there is no need to parametrically smooth multistage trial data (in contrast to observational data for adaptive strategies), it is possible to establish direct connections among different methodological approaches. We show by algebraic proof that the maximum likelihood (ML) and optimal semiparametric (SP) estimators of the population mean of the outcome of a treatment policy and its standard error are equal under certain experimental conditions. This result is used to develop a unified and efficient approach to design and inference for multistage trials of policies that adapt treatment according to discrete responses. We derive a sample size formula expressed in terms of a parametric version of the optimal SP population variance. Nonparametric (sample-based) ML estimation performed well in simulation studies, in terms of achieved power, for scenarios most likely to occur in real studies, even though sample sizes were based on the parametric formula. ML outperformed the SP estimator; differences in achieved power predominately reflected differences in their estimates of the population mean (rather than estimated standard errors). Neither methodology could mitigate the potential for overestimated sample sizes when strong nonlinearity was purposely simulated for certain discrete outcomes; however, such departures from linearity may not be an issue for many clinical contexts that make evaluation of competitive treatment policies meaningful.

Sample size determination for estimating antibody seroconversion rate under stable malaria transmission intensity.

PubMed

Sepúlveda, Nuno; Drakeley, Chris

2015-04-03

In the last decade, several epidemiological studies have demonstrated the potential of using seroprevalence (SP) and seroconversion rate (SCR) as informative indicators of malaria burden in low transmission settings or in populations on the cusp of elimination. However, most of studies are designed to control ensuing statistical inference over parasite rates and not on these alternative malaria burden measures. SP is in essence a proportion and, thus, many methods exist for the respective sample size determination. In contrast, designing a study where SCR is the primary endpoint, is not an easy task because precision and statistical power are affected by the age distribution of a given population. Two sample size calculators for SCR estimation are proposed. The first one consists of transforming the confidence interval for SP into the corresponding one for SCR given a known seroreversion rate (SRR). The second calculator extends the previous one to the most common situation where SRR is unknown. In this situation, data simulation was used together with linear regression in order to study the expected relationship between sample size and precision. The performance of the first sample size calculator was studied in terms of the coverage of the confidence intervals for SCR. The results pointed out to eventual problems of under or over coverage for sample sizes ≤250 in very low and high malaria transmission settings (SCR ≤ 0.0036 and SCR ≥ 0.29, respectively). The correct coverage was obtained for the remaining transmission intensities with sample sizes ≥ 50. Sample size determination was then carried out for cross-sectional surveys using realistic SCRs from past sero-epidemiological studies and typical age distributions from African and non-African populations. For SCR < 0.058, African studies require a larger sample size than their non-African counterparts in order to obtain the same precision. The opposite happens for the remaining transmission intensities. With respect to the second sample size calculator, simulation unravelled the likelihood of not having enough information to estimate SRR in low transmission settings (SCR ≤ 0.0108). In that case, the respective estimates tend to underestimate the true SCR. This problem is minimized by sample sizes of no less than 500 individuals. The sample sizes determined by this second method highlighted the prior expectation that, when SRR is not known, sample sizes are increased in relation to the situation of a known SRR. In contrast to the first sample size calculation, African studies would now require lesser individuals than their counterparts conducted elsewhere, irrespective of the transmission intensity. Although the proposed sample size calculators can be instrumental to design future cross-sectional surveys, the choice of a particular sample size must be seen as a much broader exercise that involves weighting statistical precision with ethical issues, available human and economic resources, and possible time constraints. Moreover, if the sample size determination is carried out on varying transmission intensities, as done here, the respective sample sizes can also be used in studies comparing sites with different malaria transmission intensities. In conclusion, the proposed sample size calculators are a step towards the design of better sero-epidemiological studies. Their basic ideas show promise to be applied to the planning of alternative sampling schemes that may target or oversample specific age groups.

A Naturalistic Study of Driving Behavior in Older Adults and Preclinical Alzheimer Disease.

PubMed

Babulal, Ganesh M; Stout, Sarah H; Benzinger, Tammie L S; Ott, Brian R; Carr, David B; Webb, Mollie; Traub, Cindy M; Addison, Aaron; Morris, John C; Warren, David K; Roe, Catherine M

2017-01-01

A clinical consequence of symptomatic Alzheimer's disease (AD) is impaired driving performance. However, decline in driving performance may begin in the preclinical stage of AD. We used a naturalistic driving methodology to examine differences in driving behavior over one year in a small sample of cognitively normal older adults with ( n = 10) and without ( n = 10) preclinical AD. As expected with a small sample size, there were no statistically significant differences between the two groups, but older adults with preclinical AD drove less often, were less likely to drive at night, and had fewer aggressive behaviors such as hard braking, speeding, and sudden acceleration. The sample size required to power a larger study to determine differences was calculated.

Assessing the Power of Exome Chips.

PubMed

Page, Christian Magnus; Baranzini, Sergio E; Mevik, Bjørn-Helge; Bos, Steffan Daniel; Harbo, Hanne F; Andreassen, Bettina Kulle

2015-01-01

Genotyping chips for rare and low-frequent variants have recently gained popularity with the introduction of exome chips, but the utility of these chips remains unclear. These chips were designed using exome sequencing data from mainly American-European individuals, enriched for a narrow set of common diseases. In addition, it is well-known that the statistical power of detecting associations with rare and low-frequent variants is much lower compared to studies exclusively involving common variants. We developed a simulation program adaptable to any exome chip design to empirically evaluate the power of the exome chips. We implemented the main properties of the Illumina HumanExome BeadChip array. The simulated data sets were used to assess the power of exome chip based studies for varying effect sizes and causal variant scenarios. We applied two widely-used statistical approaches for rare and low-frequency variants, which collapse the variants into genetic regions or genes. Under optimal conditions, we found that a sample size between 20,000 to 30,000 individuals were needed in order to detect modest effect sizes (0.5% < PAR > 1%) with 80% power. For small effect sizes (PAR <0.5%), 60,000-100,000 individuals were needed in the presence of non-causal variants. In conclusion, we found that at least tens of thousands of individuals are necessary to detect modest effects under optimal conditions. In addition, when using rare variant chips on cohorts or diseases they were not originally designed for, the identification of associated variants or genes will be even more challenging.

Sampling design and required sample size for evaluating contamination levels of 137Cs in Japanese fir needles in a mixed deciduous forest stand in Fukushima, Japan.

PubMed

Oba, Yurika; Yamada, Toshihiro

2017-05-01

We estimated the sample size (the number of samples) required to evaluate the concentration of radiocesium ( 137 Cs) in Japanese fir (Abies firma Sieb. & Zucc.), 5 years after the outbreak of the Fukushima Daiichi Nuclear Power Plant accident. We investigated the spatial structure of the contamination levels in this species growing in a mixed deciduous broadleaf and evergreen coniferous forest stand. We sampled 40 saplings with a tree height of 150 cm-250 cm in a Fukushima forest community. The results showed that: (1) there was no correlation between the 137 Cs concentration in needles and soil, and (2) the difference in the spatial distribution pattern of 137 Cs concentration between needles and soil suggest that the contribution of root uptake to 137 Cs in new needles of this species may be minor in the 5 years after the radionuclides were released into the atmosphere. The concentration of 137 Cs in needles showed a strong positive spatial autocorrelation in the distance class from 0 to 2.5 m, suggesting that the statistical analysis of data should consider spatial autocorrelation in the case of an assessment of the radioactive contamination of forest trees. According to our sample size analysis, a sample size of seven trees was required to determine the mean contamination level within an error in the means of no more than 10%. This required sample size may be feasible for most sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Undersampling power-law size distributions: effect on the assessment of extreme natural hazards

USGS Publications Warehouse

Geist, Eric L.; Parsons, Thomas E.

2014-01-01

The effect of undersampling on estimating the size of extreme natural hazards from historical data is examined. Tests using synthetic catalogs indicate that the tail of an empirical size distribution sampled from a pure Pareto probability distribution can range from having one-to-several unusually large events to appearing depleted, relative to the parent distribution. Both of these effects are artifacts caused by limited catalog length. It is more difficult to diagnose the artificially depleted empirical distributions, since one expects that a pure Pareto distribution is physically limited in some way. Using maximum likelihood methods and the method of moments, we estimate the power-law exponent and the corner size parameter of tapered Pareto distributions for several natural hazard examples: tsunamis, floods, and earthquakes. Each of these examples has varying catalog lengths and measurement thresholds, relative to the largest event sizes. In many cases where there are only several orders of magnitude between the measurement threshold and the largest events, joint two-parameter estimation techniques are necessary to account for estimation dependence between the power-law scaling exponent and the corner size parameter. Results indicate that whereas the corner size parameter of a tapered Pareto distribution can be estimated, its upper confidence bound cannot be determined and the estimate itself is often unstable with time. Correspondingly, one cannot statistically reject a pure Pareto null hypothesis using natural hazard catalog data. Although physical limits to the hazard source size and by attenuation mechanisms from source to site constrain the maximum hazard size, historical data alone often cannot reliably determine the corner size parameter. Probabilistic assessments incorporating theoretical constraints on source size and propagation effects are preferred over deterministic assessments of extreme natural hazards based on historic data.

A powered vascular staple for the application of segmental bronchial closure in thoracoscopic anatomic segmentectomy.

PubMed

Kuroda, Hiroaki; Yoshida, Tatsuya; Sakao, Yukinori

2017-12-01

We used the powered vascular staple (PVS) instead of the conventional staple technique [the utilization of the powered linier cutter (PLC)] or ligation for total 23 segmental or subsegmental bronchi with less than 10 mm in the bronchial luminal size on computed tomography (CT) in thoracoscopic segmentectomy. Our results suggested that the availability of the PVS represents a novel advance in the armamentarium and may have a possibility of being pervasive widely however, more observative periods and further sample accumulation are needed.

On the repeated measures designs and sample sizes for randomized controlled trials.

PubMed

Tango, Toshiro

2016-04-01

For the analysis of longitudinal or repeated measures data, generalized linear mixed-effects models provide a flexible and powerful tool to deal with heterogeneity among subject response profiles. However, the typical statistical design adopted in usual randomized controlled trials is an analysis of covariance type analysis using a pre-defined pair of "pre-post" data, in which pre-(baseline) data are used as a covariate for adjustment together with other covariates. Then, the major design issue is to calculate the sample size or the number of subjects allocated to each treatment group. In this paper, we propose a new repeated measures design and sample size calculations combined with generalized linear mixed-effects models that depend not only on the number of subjects but on the number of repeated measures before and after randomization per subject used for the analysis. The main advantages of the proposed design combined with the generalized linear mixed-effects models are (1) it can easily handle missing data by applying the likelihood-based ignorable analyses under the missing at random assumption and (2) it may lead to a reduction in sample size, compared with the simple pre-post design. The proposed designs and the sample size calculations are illustrated with real data arising from randomized controlled trials. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Exploration of time-course combinations of outcome scales for use in a global test of stroke recovery.

PubMed

Goldie, Fraser C; Fulton, Rachael L; Dawson, Jesse; Bluhmki, Erich; Lees, Kennedy R

2014-08-01

Clinical trials for acute ischemic stroke treatment require large numbers of participants and are expensive to conduct. Methods that enhance statistical power are therefore desirable. We explored whether this can be achieved by a measure incorporating both early and late measures of outcome (e.g. seven-day NIH Stroke Scale combined with 90-day modified Rankin scale). We analyzed sensitivity to treatment effect, using proportional odds logistic regression for ordinal scales and generalized estimating equation method for global outcomes, with all analyses adjusted for baseline severity and age. We ran simulations to assess relations between sample size and power for ordinal scales and corresponding global outcomes. We used R version 2·12·1 (R Development Core Team. R Foundation for Statistical Computing, Vienna, Austria) for simulations and SAS 9·2 (SAS Institute Inc., Cary, NC, USA) for all other analyses. Each scale considered for combination was sensitive to treatment effect in isolation. The mRS90 and NIHSS90 had adjusted odds ratio of 1·56 and 1·62, respectively. Adjusted odds ratio for global outcomes of the combination of mRS90 with NIHSS7 and NIHSS90 with NIHSS7 were 1·69 and 1·73, respectively. The smallest sample sizes required to generate statistical power ≥80% for mRS90, NIHSS7, and global outcomes of mRS90 and NIHSS7 combined and NIHSS90 and NIHSS7 combined were 500, 490, 400, and 380, respectively. When data concerning both early and late outcomes are combined into a global measure, there is increased sensitivity to treatment effect compared with solitary ordinal scales. This delivers a 20% reduction in required sample size at 80% power. Combining early with late outcomes merits further consideration. © 2013 The Authors. International Journal of Stroke © 2013 World Stroke Organization.

Sampling methods, dispersion patterns, and fixed precision sequential sampling plans for western flower thrips (Thysanoptera: Thripidae) and cotton fleahoppers (Hemiptera: Miridae) in cotton.

PubMed

Parajulee, M N; Shrestha, R B; Leser, J F

2006-04-01

A 2-yr field study was conducted to examine the effectiveness of two sampling methods (visual and plant washing techniques) for western flower thrips, Frankliniella occidentalis (Pergande), and five sampling methods (visual, beat bucket, drop cloth, sweep net, and vacuum) for cotton fleahopper, Pseudatomoscelis seriatus (Reuter), in Texas cotton, Gossypium hirsutum (L.), and to develop sequential sampling plans for each pest. The plant washing technique gave similar results to the visual method in detecting adult thrips, but the washing technique detected significantly higher number of thrips larvae compared with the visual sampling. Visual sampling detected the highest number of fleahoppers followed by beat bucket, drop cloth, vacuum, and sweep net sampling, with no significant difference in catch efficiency between vacuum and sweep net methods. However, based on fixed precision cost reliability, the sweep net sampling was the most cost-effective method followed by vacuum, beat bucket, drop cloth, and visual sampling. Taylor's Power Law analysis revealed that the field dispersion patterns of both thrips and fleahoppers were aggregated throughout the crop growing season. For thrips management decision based on visual sampling (0.25 precision), 15 plants were estimated to be the minimum sample size when the estimated population density was one thrips per plant, whereas the minimum sample size was nine plants when thrips density approached 10 thrips per plant. The minimum visual sample size for cotton fleahoppers was 16 plants when the density was one fleahopper per plant, but the sample size decreased rapidly with an increase in fleahopper density, requiring only four plants to be sampled when the density was 10 fleahoppers per plant. Sequential sampling plans were developed and validated with independent data for both thrips and cotton fleahoppers.

Population-Based Resequencing of Experimentally Evolved Populations Reveals the Genetic Basis of Body Size Variation in Drosophila melanogaster

PubMed Central

Turner, Thomas L.; Stewart, Andrew D.; Fields, Andrew T.; Rice, William R.; Tarone, Aaron M.

2011-01-01

Body size is a classic quantitative trait with evolutionarily significant variation within many species. Locating the alleles responsible for this variation would help understand the maintenance of variation in body size in particular, as well as quantitative traits in general. However, successful genome-wide association of genotype and phenotype may require very large sample sizes if alleles have low population frequencies or modest effects. As a complementary approach, we propose that population-based resequencing of experimentally evolved populations allows for considerable power to map functional variation. Here, we use this technique to investigate the genetic basis of natural variation in body size in Drosophila melanogaster. Significant differentiation of hundreds of loci in replicate selection populations supports the hypothesis that the genetic basis of body size variation is very polygenic in D. melanogaster. Significantly differentiated variants are limited to single genes at some loci, allowing precise hypotheses to be formed regarding causal polymorphisms, while other significant regions are large and contain many genes. By using significantly associated polymorphisms as a priori candidates in follow-up studies, these data are expected to provide considerable power to determine the genetic basis of natural variation in body size. PMID:21437274

Rapid Active Sampling Package

NASA Technical Reports Server (NTRS)

Peters, Gregory

2010-01-01

A field-deployable, battery-powered Rapid Active Sampling Package (RASP), originally designed for sampling strong materials during lunar and planetary missions, shows strong utility for terrestrial geological use. The technology is proving to be simple and effective for sampling and processing materials of strength. Although this originally was intended for planetary and lunar applications, the RASP is very useful as a powered hand tool for geologists and the mining industry to quickly sample and process rocks in the field on Earth. The RASP allows geologists to surgically acquire samples of rock for later laboratory analysis. This tool, roughly the size of a wrench, allows the user to cut away swaths of weathering rinds, revealing pristine rock surfaces for observation and subsequent sampling with the same tool. RASPing deeper (.3.5 cm) exposes single rock strata in-situ. Where a geologist fs hammer can only expose unweathered layers of rock, the RASP can do the same, and then has the added ability to capture and process samples into powder with particle sizes less than 150 microns, making it easier for XRD/XRF (x-ray diffraction/x-ray fluorescence). The tool uses a rotating rasp bit (or two counter-rotating bits) that resides inside or above the catch container. The container has an open slot to allow the bit to extend outside the container and to allow cuttings to enter and be caught. When the slot and rasp bit are in contact with a substrate, the bit is plunged into it in a matter of seconds to reach pristine rock. A user in the field may sample a rock multiple times at multiple depths in minutes, instead of having to cut out huge, heavy rock samples for transport back to a lab for analysis. Because of the speed and accuracy of the RASP, hundreds of samples can be taken in one day. RASP-acquired samples are small and easily carried. A user can characterize more area in less time than by using conventional methods. The field-deployable RASP used a Ni/Cad rechargeable battery. Power usage was less than 1 Wh/ cm3 even when sampling strong basalts, so many samples could be taken on a single battery charge.

Statistical analyses to support guidelines for marine avian sampling. Final report

USGS Publications Warehouse

Kinlan, Brian P.; Zipkin, Elise; O'Connell, Allan F.; Caldow, Chris

2012-01-01

Interest in development of offshore renewable energy facilities has led to a need for high-quality, statistically robust information on marine wildlife distributions. A practical approach is described to estimate the amount of sampling effort required to have sufficient statistical power to identify species-specific “hotspots” and “coldspots” of marine bird abundance and occurrence in an offshore environment divided into discrete spatial units (e.g., lease blocks), where “hotspots” and “coldspots” are defined relative to a reference (e.g., regional) mean abundance and/or occurrence probability for each species of interest. For example, a location with average abundance or occurrence that is three times larger the mean (3x effect size) could be defined as a “hotspot,” and a location that is three times smaller than the mean (1/3x effect size) as a “coldspot.” The choice of the effect size used to define hot and coldspots will generally depend on a combination of ecological and regulatory considerations. A method is also developed for testing the statistical significance of possible hotspots and coldspots. Both methods are illustrated with historical seabird survey data from the USGS Avian Compendium Database. Our approach consists of five main components: 1. A review of the primary scientific literature on statistical modeling of animal group size and avian count data to develop a candidate set of statistical distributions that have been used or may be useful to model seabird counts. 2. Statistical power curves for one-sample, one-tailed Monte Carlo significance tests of differences of observed small-sample means from a specified reference distribution. These curves show the power to detect "hotspots" or "coldspots" of occurrence and abundance at a range of effect sizes, given assumptions which we discuss. 3. A model selection procedure, based on maximum likelihood fits of models in the candidate set, to determine an appropriate statistical distribution to describe counts of a given species in a particular region and season. 4. Using a large database of historical at-sea seabird survey data, we applied this technique to identify appropriate statistical distributions for modeling a variety of species, allowing the distribution to vary by season. For each species and season, we used the selected distribution to calculate and map retrospective statistical power to detect hotspots and coldspots, and map pvalues from Monte Carlo significance tests of hotspots and coldspots, in discrete lease blocks designated by the U.S. Department of Interior, Bureau of Ocean Energy Management (BOEM). 5. Because our definition of hotspots and coldspots does not explicitly include variability over time, we examine the relationship between the temporal scale of sampling and the proportion of variance captured in time series of key environmental correlates of marine bird abundance, as well as available marine bird abundance time series, and use these analyses to develop recommendations for the temporal distribution of sampling to adequately represent both shortterm and long-term variability. We conclude by presenting a schematic “decision tree” showing how this power analysis approach would fit in a general framework for avian survey design, and discuss implications of model assumptions and results. We discuss avenues for future development of this work, and recommendations for practical implementation in the context of siting and wildlife assessment for offshore renewable energy development projects.

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations.

PubMed

Kunz, Cornelia U; Stallard, Nigel; Parsons, Nicholas; Todd, Susan; Friede, Tim

2017-03-01

Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under- or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re-assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. Their performance with respect to bias and standard error is compared to the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Simulation results show that the naïve (one-sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups. © 2016 The Authors. Biometrical Journal Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations

PubMed Central

Stallard, Nigel; Parsons, Nicholas; Todd, Susan; Friede, Tim

2016-01-01

Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under‐ or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re‐assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. Their performance with respect to bias and standard error is compared to the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Simulation results show that the naïve (one‐sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups. PMID:27886393

Chandra Observations of Three Newly Discovered Quadruply Gravitationally Lensed Quasars

NASA Astrophysics Data System (ADS)

Pooley, David

2017-09-01

Our previous work has shown the unique power of Chandra observations of quadruply gravitationally lensed quasars to address several fundamental astrophysical issues. We have used these observations to (1) determine the cause of flux ratio anomalies, (2) measure the sizes of quasar accretion disks, (3) determine the dark matter content of the lensing galaxies, and (4) measure the stellar mass-to-light ratio (in fact, this is the only way to measure the stellar mass-to-light ratio beyond the solar neighborhood). In all cases, the main source of uncertainty in our results is the small size of the sample of known quads; only 15 systems are available for study with Chandra. We propose Chandra observations of three newly discovered quads, increasing the sample size by 20%

A Multi-Week Behavioral Sampling Tag for Sound Effects Studies: Design Trade-Offs and Prototype Evaluation

DTIC Science & Technology

2013-09-30

performance of algorithms detecting dives, strokes , clicks, respiration and gait changes. (ii) Calibration errors: Size and power constraints in...acceptance parameters used to detect and classify events. For example, swim stroke detection requires parameters defining the minimum magnitude and the min...and max duration of a stroke . Species dependent parameters can be selected from existing DTAG data but other parameters depend on the size of the

Aircraft Particle Emissions eXperiment (APEX)

NASA Technical Reports Server (NTRS)

Wey, C. C.; Anderson, B. E.; Hudgins, C.; Wey, C.; Li-Jones, X.; Winstead, E.; Thornhill, L. K.; Lobo, P.; Hagen, D.; Whitefield, P.

2006-01-01

APEX systematically investigated the gas-phase and particle emissions from a CFM56-2C1 engine on NASA's DC-8 aircraft as functions of engine power, fuel composition, and exhaust plumage. Emissions parameters were measured at 11 engine power, settings, ranging from idle to maximum thrust, in samples collected at 1, 10, and 30 m downstream of the exhaust plane as the aircraft burned three fuels to stress relevant chemistry. Gas-phase emission indices measured at 1 m were in good agreement with the ICAO data and predictions provided by GEAE empirical modeling tools. Soot particles emitted by the engine exhibited a log-normal size distribution peaked between 15 and 40 nm, depending on engine power. Samples collected 30 m downstream of the engine exhaust plane exhibited a prominent nucleation mode.

Design and implementation of a dental caries prevention trial in remote Canadian Aboriginal communities.

PubMed

Harrison, Rosamund; Veronneau, Jacques; Leroux, Brian

2010-05-13

The goal of this cluster randomized trial is to test the effectiveness of a counseling approach, Motivational Interviewing, to control dental caries in young Aboriginal children. Motivational Interviewing, a client-centred, directive counseling style, has not yet been evaluated as an approach for promotion of behaviour change in indigenous communities in remote settings. Aboriginal women were hired from the 9 communities to recruit expectant and new mothers to the trial, administer questionnaires and deliver the counseling to mothers in the test communities. The goal is for mothers to receive the intervention during pregnancy and at their child's immunization visits. Data on children's dental health status and family dental health practices will be collected when children are 30-months of age. The communities were randomly allocated to test or control group by a random "draw" over community radio. Sample size and power were determined based on an anticipated 20% reduction in caries prevalence. Randomization checks were conducted between groups. In the 5 test and 4 control communities, 272 of the original target sample size of 309 mothers have been recruited over a two-and-a-half year period. A power calculation using the actual attained sample size showed power to be 79% to detect a treatment effect. If an attrition fraction of 4% per year is maintained, power will remain at 80%. Power will still be > 90% to detect a 25% reduction in caries prevalence. The distribution of most baseline variables was similar for the two randomized groups of mothers. However, despite the random assignment of communities to treatment conditions, group differences exist for stage of pregnancy and prior tooth extractions in the family. Because of the group imbalances on certain variables, control of baseline variables will be done in the analyses of treatment effects. This paper explains the challenges of conducting randomized trials in remote settings, the importance of thorough community collaboration, and also illustrates the likelihood that some baseline variables that may be clinically important will be unevenly split in group-randomized trials when the number of groups is small. This trial is registered as ISRCTN41467632.

Design and implementation of a dental caries prevention trial in remote Canadian Aboriginal communities

PubMed Central

2010-01-01

Background The goal of this cluster randomized trial is to test the effectiveness of a counseling approach, Motivational Interviewing, to control dental caries in young Aboriginal children. Motivational Interviewing, a client-centred, directive counseling style, has not yet been evaluated as an approach for promotion of behaviour change in indigenous communities in remote settings. Methods/design Aboriginal women were hired from the 9 communities to recruit expectant and new mothers to the trial, administer questionnaires and deliver the counseling to mothers in the test communities. The goal is for mothers to receive the intervention during pregnancy and at their child's immunization visits. Data on children's dental health status and family dental health practices will be collected when children are 30-months of age. The communities were randomly allocated to test or control group by a random "draw" over community radio. Sample size and power were determined based on an anticipated 20% reduction in caries prevalence. Randomization checks were conducted between groups. Discussion In the 5 test and 4 control communities, 272 of the original target sample size of 309 mothers have been recruited over a two-and-a-half year period. A power calculation using the actual attained sample size showed power to be 79% to detect a treatment effect. If an attrition fraction of 4% per year is maintained, power will remain at 80%. Power will still be > 90% to detect a 25% reduction in caries prevalence. The distribution of most baseline variables was similar for the two randomized groups of mothers. However, despite the random assignment of communities to treatment conditions, group differences exist for stage of pregnancy and prior tooth extractions in the family. Because of the group imbalances on certain variables, control of baseline variables will be done in the analyses of treatment effects. This paper explains the challenges of conducting randomized trials in remote settings, the importance of thorough community collaboration, and also illustrates the likelihood that some baseline variables that may be clinically important will be unevenly split in group-randomized trials when the number of groups is small. Trial registration This trial is registered as ISRCTN41467632. PMID:20465831

Inference and sample size calculation for clinical trials with incomplete observations of paired binary outcomes.

PubMed

Zhang, Song; Cao, Jing; Ahn, Chul

2017-02-20

We investigate the estimation of intervention effect and sample size determination for experiments where subjects are supposed to contribute paired binary outcomes with some incomplete observations. We propose a hybrid estimator to appropriately account for the mixed nature of observed data: paired outcomes from those who contribute complete pairs of observations and unpaired outcomes from those who contribute either pre-intervention or post-intervention outcomes. We theoretically prove that if incomplete data are evenly distributed between the pre-intervention and post-intervention periods, the proposed estimator will always be more efficient than the traditional estimator. A numerical research shows that when the distribution of incomplete data is unbalanced, the proposed estimator will be superior when there is moderate-to-strong positive within-subject correlation. We further derive a closed-form sample size formula to help researchers determine how many subjects need to be enrolled in such studies. Simulation results suggest that the calculated sample size maintains the empirical power and type I error under various design configurations. We demonstrate the proposed method using a real application example. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Sample Size Methods for Estimating HIV Incidence from Cross-Sectional Surveys

PubMed Central

Brookmeyer, Ron

2015-01-01

Summary Understanding HIV incidence, the rate at which new infections occur in populations, is critical for tracking and surveillance of the epidemic. In this paper we derive methods for determining sample sizes for cross-sectional surveys to estimate incidence with sufficient precision. We further show how to specify sample sizes for two successive cross-sectional surveys to detect changes in incidence with adequate power. In these surveys biomarkers such as CD4 cell count, viral load, and recently developed serological assays are used to determine which individuals are in an early disease stage of infection. The total number of individuals in this stage, divided by the number of people who are uninfected, is used to approximate the incidence rate. Our methods account for uncertainty in the durations of time spent in the biomarker defined early disease stage. We find that failure to account for this uncertainty when designing surveys can lead to imprecise estimates of incidence and underpowered studies. We evaluated our sample size methods in simulations and found that they performed well in a variety of underlying epidemics. Code for implementing our methods in R is available with this paper at the Biometrics website on Wiley Online Library. PMID:26302040

Sample size methods for estimating HIV incidence from cross-sectional surveys.

PubMed

Konikoff, Jacob; Brookmeyer, Ron

2015-12-01

Understanding HIV incidence, the rate at which new infections occur in populations, is critical for tracking and surveillance of the epidemic. In this article, we derive methods for determining sample sizes for cross-sectional surveys to estimate incidence with sufficient precision. We further show how to specify sample sizes for two successive cross-sectional surveys to detect changes in incidence with adequate power. In these surveys biomarkers such as CD4 cell count, viral load, and recently developed serological assays are used to determine which individuals are in an early disease stage of infection. The total number of individuals in this stage, divided by the number of people who are uninfected, is used to approximate the incidence rate. Our methods account for uncertainty in the durations of time spent in the biomarker defined early disease stage. We find that failure to account for this uncertainty when designing surveys can lead to imprecise estimates of incidence and underpowered studies. We evaluated our sample size methods in simulations and found that they performed well in a variety of underlying epidemics. Code for implementing our methods in R is available with this article at the Biometrics website on Wiley Online Library. © 2015, The International Biometric Society.

Dynamic relationships between body size, species richness, abundance, and energy use in a shallow marine epibenthic faunal community

PubMed Central

Labra, Fabio A; Hernández-Miranda, Eduardo; Quiñones, Renato A

2015-01-01

We study the temporal variation in the empirical relationships among body size (S), species richness (R), and abundance (A) in a shallow marine epibenthic faunal community in Coliumo Bay, Chile. We also extend previous analyses by calculating individual energy use (E) and test whether its bivariate and trivariate relationships with S and R are in agreement with expectations derived from the energetic equivalence rule. Carnivorous and scavenger species representing over 95% of sample abundance and biomass were studied. For each individual, body size (g) was measured and E was estimated following published allometric relationships. Data for each sample were tabulated into exponential body size bins, comparing species-averaged values with individual-based estimates which allow species to potentially occupy multiple size classes. For individual-based data, both the number of individuals and species across body size classes are fit by a Weibull function rather than by a power law scaling. Species richness is also a power law of the number of individuals. Energy use shows a piecewise scaling relationship with body size, with energetic equivalence holding true only for size classes above the modal abundance class. Species-based data showed either weak linear or no significant patterns, likely due to the decrease in the number of data points across body size classes. Hence, for individual-based size spectra, the SRA relationship seems to be general despite seasonal forcing and strong disturbances in Coliumo Bay. The unimodal abundance distribution results in a piecewise energy scaling relationship, with small individuals showing a positive scaling and large individuals showing energetic equivalence. Hence, strict energetic equivalence should not be expected for unimodal abundance distributions. On the other hand, while species-based data do not show unimodal SRA relationships, energy use across body size classes did not show significant trends, supporting energetic equivalence. PMID:25691966

How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

PubMed

Veturi, Yogasudha; Ritchie, Marylyn D

2018-01-01

Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.

Tungsten Carbide Grain Size Computation for WC-Co Dissimilar Welds

NASA Astrophysics Data System (ADS)

Zhou, Dongran; Cui, Haichao; Xu, Peiquan; Lu, Fenggui

2016-06-01

A "two-step" image processing method based on electron backscatter diffraction in scanning electron microscopy was used to compute the tungsten carbide (WC) grain size distribution for tungsten inert gas (TIG) welds and laser welds. Twenty-four images were collected on randomly set fields per sample located at the top, middle, and bottom of a cross-sectional micrograph. Each field contained 500 to 1500 WC grains. The images were recognized through clustering-based image segmentation and WC grain growth recognition. According to the WC grain size computation and experiments, a simple WC-WC interaction model was developed to explain the WC dissolution, grain growth, and aggregation in welded joints. The WC-WC interaction and blunt corners were characterized using scanning and transmission electron microscopy. The WC grain size distribution and the effects of heat input E on grain size distribution for the laser samples were discussed. The results indicate that (1) the grain size distribution follows a Gaussian distribution. Grain sizes at the top of the weld were larger than those near the middle and weld root because of power attenuation. (2) Significant WC grain growth occurred during welding as observed in the as-welded micrographs. The average grain size was 11.47 μm in the TIG samples, which was much larger than that in base metal 1 (BM1 2.13 μm). The grain size distribution curves for the TIG samples revealed a broad particle size distribution without fine grains. The average grain size (1.59 μm) in laser samples was larger than that in base metal 2 (BM2 1.01 μm). (3) WC-WC interaction exhibited complex plane, edge, and blunt corner characteristics during grain growth. A WC ( { 1 {bar{{1}}}00} ) to WC ( {0 1 1 {bar{{0}}}} ) edge disappeared and became a blunt plane WC ( { 10 1 {bar{{0}}}} ) , several grains with two- or three-sided planes and edges disappeared into a multi-edge, and a WC-WC merged.

Determining the sample size required to establish whether a medical device is non-inferior to an external benchmark.

PubMed

Sayers, Adrian; Crowther, Michael J; Judge, Andrew; Whitehouse, Michael R; Blom, Ashley W

2017-08-28

The use of benchmarks to assess the performance of implants such as those used in arthroplasty surgery is a widespread practice. It provides surgeons, patients and regulatory authorities with the reassurance that implants used are safe and effective. However, it is not currently clear how or how many implants should be statistically compared with a benchmark to assess whether or not that implant is superior, equivalent, non-inferior or inferior to the performance benchmark of interest.We aim to describe the methods and sample size required to conduct a one-sample non-inferiority study of a medical device for the purposes of benchmarking. Simulation study. Simulation study of a national register of medical devices. We simulated data, with and without a non-informative competing risk, to represent an arthroplasty population and describe three methods of analysis (z-test, 1-Kaplan-Meier and competing risks) commonly used in surgical research. We evaluate the performance of each method using power, bias, root-mean-square error, coverage and CI width. 1-Kaplan-Meier provides an unbiased estimate of implant net failure, which can be used to assess if a surgical device is non-inferior to an external benchmark. Small non-inferiority margins require significantly more individuals to be at risk compared with current benchmarking standards. A non-inferiority testing paradigm provides a useful framework for determining if an implant meets the required performance defined by an external benchmark. Current contemporary benchmarking standards have limited power to detect non-inferiority, and substantially larger samples sizes, in excess of 3200 procedures, are required to achieve a power greater than 60%. It is clear when benchmarking implant performance, net failure estimated using 1-KM is preferential to crude failure estimated by competing risk models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

High-resolution, submicron particle size distribution analysis using gravitational-sweep sedimentation.

PubMed Central

Mächtle, W

1999-01-01

Sedimentation velocity is a powerful tool for the analysis of complex solutions of macromolecules. However, sample turbidity imposes an upper limit to the size of molecular complexes currently amenable to such analysis. Furthermore, the breadth of the particle size distribution, combined with possible variations in the density of different particles, makes it difficult to analyze extremely complex mixtures. These same problems are faced in the polymer industry, where dispersions of latices, pigments, lacquers, and emulsions must be characterized. There is a rich history of methods developed for the polymer industry finding use in the biochemical sciences. Two such methods are presented. These use analytical ultracentrifugation to determine the density and size distributions for submicron-sized particles. Both methods rely on Stokes' equations to estimate particle size and density, whereas turbidity, corrected using Mie's theory, provides the concentration measurement. The first method uses the sedimentation time in dispersion media of different densities to evaluate the particle density and size distribution. This method works provided the sample is chemically homogeneous. The second method splices together data gathered at different sample concentrations, thus permitting the high-resolution determination of the size distribution of particle diameters ranging from 10 to 3000 nm. By increasing the rotor speed exponentially from 0 to 40,000 rpm over a 1-h period, size distributions may be measured for extremely broadly distributed dispersions. Presented here is a short history of particle size distribution analysis using the ultracentrifuge, along with a description of the newest experimental methods. Several applications of the methods are provided that demonstrate the breadth of its utility, including extensions to samples containing nonspherical and chromophoric particles. PMID:9916040

Nanoliter hemolymph sampling and analysis of individual adult Drosophila melanogaster.

PubMed

Piyankarage, Sujeewa C; Featherstone, David E; Shippy, Scott A

2012-05-15

The fruit fly (Drosophila melanogaster) is an extensively used and powerful, genetic model organism. However, chemical studies using individual flies have been limited by the animal's small size. Introduced here is a method to sample nanoliter hemolymph volumes from individual adult fruit-flies for chemical analysis. The technique results in an ability to distinguish hemolymph chemical variations with developmental stage, fly sex, and sampling conditions. Also presented is the means for two-point monitoring of hemolymph composition for individual flies.

78 FR 74175 - Agency Information Collection Activities: Proposed Collection; Comment Request; Generic Clearance...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-10

... precision requirements or power calculations that justify the proposed sample size, the expected response...: Proposed Collection; Comment Request; Generic Clearance for the Collection of Qualitative Feedback on... Information Collection Request (Generic ICR): ``Generic Clearance for the Collection of Qualitative Feedback...

Meta-analytic evidence of low convergence between implicit and explicit measures of the needs for achievement, affiliation, and power

PubMed Central

Köllner, Martin G.; Schultheiss, Oliver C.

2014-01-01

The correlation between implicit and explicit motive measures and potential moderators of this relationship were examined meta-analytically, using Hunter and Schmidt's (2004) approach. Studies from a comprehensive search in PsycINFO, data sets of our research group, a literature list compiled by an expert, and the results of a request for gray literature were examined for relevance and coded. Analyses were based on 49 papers, 56 independent samples, 6151 subjects, and 167 correlations. The correlations (ρ) between implicit and explicit measures were 0.130 (CI: 0.077–0.183) for the overall relationship, 0.116 (CI: 0.050–0.182) for affiliation, 0.139 (CI: 0.080–0.198) for achievement, and 0.038 (CI: −0.055–0.131) for power. Participant age did not moderate the size of these relationships. However, a greater proportion of males in the samples and an earlier publication year were associated with larger effect sizes. PMID:25152741

Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis.

PubMed

Emond, Mary J; Louie, Tin; Emerson, Julia; Zhao, Wei; Mathias, Rasika A; Knowles, Michael R; Wright, Fred A; Rieder, Mark J; Tabor, Holly K; Nickerson, Deborah A; Barnes, Kathleen C; Gibson, Ronald L; Bamshad, Michael J

2012-07-08

Exome sequencing has become a powerful and effective strategy for the discovery of genes underlying Mendelian disorders. However, use of exome sequencing to identify variants associated with complex traits has been more challenging, partly because the sample sizes needed for adequate power may be very large. One strategy to increase efficiency is to sequence individuals who are at both ends of a phenotype distribution (those with extreme phenotypes). Because the frequency of alleles that contribute to the trait are enriched in one or both phenotype extremes, a modest sample size can potentially be used to identify novel candidate genes and/or alleles. As part of the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP), we used an extreme phenotype study design to discover that variants in DCTN4, encoding a dynactin protein, are associated with time to first P. aeruginosa airway infection, chronic P. aeruginosa infection and mucoid P. aeruginosa in individuals with cystic fibrosis.

Developing optimum sample size and multistage sampling plans for Lobesia botrana (Lepidoptera: Tortricidae) larval infestation and injury in northern Greece.

PubMed

Ifoulis, A A; Savopoulou-Soultani, M

2006-10-01

The purpose of this research was to quantify the spatial pattern and develop a sampling program for larvae of Lobesia botrana Denis and Schiffermüller (Lepidoptera: Tortricidae), an important vineyard pest in northern Greece. Taylor's power law and Iwao's patchiness regression were used to model the relationship between the mean and the variance of larval counts. Analysis of covariance was carried out, separately for infestation and injury, with combined second and third generation data, for vine and half-vine sample units. Common regression coefficients were estimated to permit use of the sampling plan over a wide range of conditions. Optimum sample sizes for infestation and injury, at three levels of precision, were developed. An investigation of a multistage sampling plan with a nested analysis of variance showed that if the goal of sampling is focusing on larval infestation, three grape clusters should be sampled in a half-vine; if the goal of sampling is focusing on injury, then two grape clusters per half-vine are recommended.

Nonlinear vibrational microscopy

DOEpatents

Holtom, Gary R.; Xie, Xiaoliang Sunney; Zumbusch, Andreas

2000-01-01

The present invention is a method and apparatus for microscopic vibrational imaging using coherent Anti-Stokes Raman Scattering or Sum Frequency Generation. Microscopic imaging with a vibrational spectroscopic contrast is achieved by generating signals in a nonlinear optical process and spatially resolved detection of the signals. The spatial resolution is attained by minimizing the spot size of the optical interrogation beams on the sample. Minimizing the spot size relies upon a. directing at least two substantially co-axial laser beams (interrogation beams) through a microscope objective providing a focal spot on the sample; b. collecting a signal beam together with a residual beam from the at least two co-axial laser beams after passing through the sample; c. removing the residual beam; and d. detecting the signal beam thereby creating said pixel. The method has significantly higher spatial resolution then IR microscopy and higher sensitivity than spontaneous Raman microscopy with much lower average excitation powers. CARS and SFG microscopy does not rely on the presence of fluorophores, but retains the resolution and three-dimensional sectioning capability of confocal and two-photon fluorescence microscopy. Complementary to these techniques, CARS and SFG microscopy provides a contrast mechanism based on vibrational spectroscopy. This vibrational contrast mechanism, combined with an unprecedented high sensitivity at a tolerable laser power level, provides a new approach for microscopic investigations of chemical and biological samples.

System design of an optical interferometer based on compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Gang; Wen, De-Sheng; Song, Zong-Xi

2018-07-01

In this paper, we develop a new optical interferometric telescope architecture based on compressive sensing (CS) theory. Traditional optical telescopes with large apertures must be large in size, heavy and have high-power consumption, which limits the development of space-based telescopes. A turning point has occurred in the advent of imaging technology that utilizes Fourier-domain interferometry. This technology can reduce the system size, weight and power consumption by an order of magnitude compared to traditional optical telescopes at the same resolution. CS theory demonstrates that incomplete and noisy Fourier measurements may suffice for the exact reconstruction of sparse or compressible signals. Our proposed architecture combines advantages from the two frameworks, and the performance is evaluated through simulations. The results indicate the ability to efficiently sample spatial frequencies, while being lightweight and compact in size. Another attractive property of our architecture is the strong denoising ability for Gaussian noise.

Carbon nanotube scaffolds with controlled porosity as electromagnetic absorbing materials in the gigahertz range.

PubMed

González, M; Crespo, M; Baselga, J; Pozuelo, J

2016-05-19

Control of the microscopic structure of CNT nanocomposites allows modulation of the electromagnetic shielding in the gigahertz range. The porosity of CNT scaffolds has been controlled by two freezing protocols and a subsequent lyophilization step: fast freezing in liquid nitrogen and slow freezing at -20 °C. Mercury porosimetry shows that slowly frozen specimens present a more open pore size (100-150 μm) with a narrow distribution whereas specimens frozen rapidly show a smaller pore size and a heterogeneous distribution. 3D-scaffolds containing 3, 4, 6 and 7% CNT were infiltrated with epoxy and specimens with 2, 5 and 8 mm thicknesses were characterized in the GHz range. Samples with the highest pore size and porosity presented the lowest reflected power (about 30%) and the highest absorbed power (about 70%), which allows considering them as electromagnetic radiation absorbing materials.

Low-temperature thermoelectric power factor enhancement by controlling nanoparticle size distribution.

PubMed

Zebarjadi, Mona; Esfarjani, Keivan; Bian, Zhixi; Shakouri, Ali

2011-01-12

Coherent potential approximation is used to study the effect of adding doped spherical nanoparticles inside a host matrix on the thermoelectric properties. This takes into account electron multiple scatterings that are important in samples with relatively high volume fraction of nanoparticles (>1%). We show that with large fraction of uniform small size nanoparticles (∼1 nm), the power factor can be enhanced significantly. The improvement could be large (up to 450% for GaAs) especially at low temperatures when the mobility is limited by impurity or nanoparticle scattering. The advantage of doping via embedded nanoparticles compared to the conventional shallow impurities is quantified. At the optimum thermoelectric power factor, the electrical conductivity of the nanoparticle-doped material is larger than that of impurity-doped one at the studied temperature range (50-500 K) whereas the Seebeck coefficient of the nanoparticle doped material is enhanced only at low temperatures (∼50 K).

Power and sample size evaluation for the Cochran-Mantel-Haenszel mean score (Wilcoxon rank sum) test and the Cochran-Armitage test for trend.

PubMed

Lachin, John M

2011-11-10

The power of a chi-square test, and thus the required sample size, are a function of the noncentrality parameter that can be obtained as the limiting expectation of the test statistic under an alternative hypothesis specification. Herein, we apply this principle to derive simple expressions for two tests that are commonly applied to discrete ordinal data. The Wilcoxon rank sum test for the equality of distributions in two groups is algebraically equivalent to the Mann-Whitney test. The Kruskal-Wallis test applies to multiple groups. These tests are equivalent to a Cochran-Mantel-Haenszel mean score test using rank scores for a set of C-discrete categories. Although various authors have assessed the power function of the Wilcoxon and Mann-Whitney tests, herein it is shown that the power of these tests with discrete observations, that is, with tied ranks, is readily provided by the power function of the corresponding Cochran-Mantel-Haenszel mean scores test for two and R > 2 groups. These expressions yield results virtually identical to those derived previously for rank scores and also apply to other score functions. The Cochran-Armitage test for trend assesses whether there is an monotonically increasing or decreasing trend in the proportions with a positive outcome or response over the C-ordered categories of an ordinal independent variable, for example, dose. Herein, it is shown that the power of the test is a function of the slope of the response probabilities over the ordinal scores assigned to the groups that yields simple expressions for the power of the test. Copyright © 2011 John Wiley & Sons, Ltd.

Power Calculations to Select Instruments for Clinical Trial Secondary Endpoints. A Case Study of Instrument Selection for Post-Traumatic Stress Symptoms in Subjects with Acute Respiratory Distress Syndrome.

PubMed

Sjoding, Michael W; Schoenfeld, David A; Brown, Samuel M; Hough, Catherine L; Yealy, Donald M; Moss, Marc; Angus, Derek C; Iwashyna, Theodore J

2017-01-01

After the sample size of a randomized clinical trial (RCT) is set by the power requirement of its primary endpoint, investigators select secondary endpoints while unable to further adjust sample size. How the sensitivity and specificity of an instrument used to measure these outcomes, together with their expected underlying event rates, affect an RCT's power to measure significant differences in these outcomes is poorly understood. Motivated by the design of an RCT of neuromuscular blockade in acute respiratory distress syndrome, we examined how power to detect a difference in secondary endpoints varies with the sensitivity and specificity of the instrument used to measure such outcomes. We derived a general formula and Stata code for calculating an RCT's power to detect differences in binary outcomes when such outcomes are measured with imperfect sensitivity and specificity. The formula informed the choice of instrument for measuring post-traumatic stress-like symptoms in the Reevaluation of Systemic Early Neuromuscular Blockade RCT ( www.clinicaltrials.gov identifier NCT02509078). On the basis of published sensitivities and specificities, the Impact of Events Scale-Revised was predicted to measure a 36% symptom rate, whereas the Post-Traumatic Stress Symptoms instrument was predicted to measure a 23% rate, if the true underlying rate of post-traumatic stress symptoms were 25%. Despite its lower sensitivity, the briefer Post-Traumatic Stress Symptoms instrument provided superior power to detect a difference in rates between trial arms, owing to its higher specificity. Examining instruments' power to detect differences in outcomes may guide their selection when multiple instruments exist, each with different sensitivities and specificities.

Power Calculations to Select Instruments for Clinical Trial Secondary Endpoints. A Case Study of Instrument Selection for Post-Traumatic Stress Symptoms in Subjects with Acute Respiratory Distress Syndrome

PubMed Central

Schoenfeld, David A.; Brown, Samuel M.; Hough, Catherine L.; Yealy, Donald M.; Moss, Marc; Angus, Derek C.; Iwashyna, Theodore J.

2017-01-01

Rationale: After the sample size of a randomized clinical trial (RCT) is set by the power requirement of its primary endpoint, investigators select secondary endpoints while unable to further adjust sample size. How the sensitivity and specificity of an instrument used to measure these outcomes, together with their expected underlying event rates, affect an RCT’s power to measure significant differences in these outcomes is poorly understood. Objectives: Motivated by the design of an RCT of neuromuscular blockade in acute respiratory distress syndrome, we examined how power to detect a difference in secondary endpoints varies with the sensitivity and specificity of the instrument used to measure such outcomes. Methods: We derived a general formula and Stata code for calculating an RCT’s power to detect differences in binary outcomes when such outcomes are measured with imperfect sensitivity and specificity. The formula informed the choice of instrument for measuring post-traumatic stress–like symptoms in the Reevaluation of Systemic Early Neuromuscular Blockade RCT (www.clinicaltrials.gov identifier NCT02509078). Measurements and Main Results: On the basis of published sensitivities and specificities, the Impact of Events Scale-Revised was predicted to measure a 36% symptom rate, whereas the Post-Traumatic Stress Symptoms instrument was predicted to measure a 23% rate, if the true underlying rate of post-traumatic stress symptoms were 25%. Despite its lower sensitivity, the briefer Post-Traumatic Stress Symptoms instrument provided superior power to detect a difference in rates between trial arms, owing to its higher specificity. Conclusions: Examining instruments’ power to detect differences in outcomes may guide their selection when multiple instruments exist, each with different sensitivities and specificities. PMID:27788018

Value of information methods to design a clinical trial in a small population to optimise a health economic utility function.

PubMed

Pearce, Michael; Hee, Siew Wan; Madan, Jason; Posch, Martin; Day, Simon; Miller, Frank; Zohar, Sarah; Stallard, Nigel

2018-02-08

Most confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population. We considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future. The optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored. Decision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.

Thiol-Capped Gold Nanoparticles Swell-Encapsulated into Polyurethane as Powerful Antibacterial Surfaces Under Dark and Light Conditions

PubMed Central

Macdonald, Thomas J.; Wu, Ke; Sehmi, Sandeep K.; Noimark, Sacha; Peveler, William J.; du Toit, Hendrik; Voelcker, Nicolas H.; Allan, Elaine; MacRobert, Alexander J.; Gavriilidis, Asterios; Parkin, Ivan P.

2016-01-01

A simple procedure to develop antibacterial surfaces using thiol-capped gold nanoparticles (AuNPs) is shown, which effectively kill bacteria under dark and light conditions. The effect of AuNP size and concentration on photo-activated antibacterial surfaces is reported and we show significant size effects, as well as bactericidal activity with crystal violet (CV) coated polyurethane. These materials have been proven to be powerful antibacterial surfaces against both Gram-positive and Gram-negative bacteria. AuNPs of 2, 3 or 5 nm diameter were swell-encapsulated into PU before a coating of CV was applied (known as PU-AuNPs-CV). The antibacterial activity of PU-AuNPs-CV samples was tested against Staphylococcus aureus and Escherichia coli as representative Gram-positive and Gram-negative bacteria under dark and light conditions. All light conditions in this study simulated a typical white-light hospital environment. This work demonstrates that the antibacterial activity of PU-AuNPs-CV samples and the synergistic enhancement of photoactivity of triarylmethane type dyes is highly dependent on nanoparticle size and concentration. The most powerful PU-AuNPs-CV antibacterial surfaces were achieved using 1.0 mg mL−1 swell encapsulation concentrations of 2 nm AuNPs. After two hours, Gram-positive and Gram-negative bacteria were reduced to below the detection limit (>4 log) under dark and light conditions. PMID:27982122

Strong surface enhanced Raman scattering from gold nanoarrays obtained by direct laser writing

NASA Astrophysics Data System (ADS)

Ivanov, V. G.; Todorov, N. D.; Petrov, L. S.; Ritacco, T.; Giocondo, M.; Vlakhov, E. S.

2016-10-01

We report for surface enhanced Raman scattering (SERS) from arrays of gold nanoparticles produced by 2-photons photo-reduction of the metallic precursor (HAuCl4) hosted in a Poly-Vinyl Alcohol (PVA) matrix, on glass substrates. Samples with the same pattern but featuring different nanoparticles size and density were obtained by varying the writing laser power and scanning speed. The Raman spectra were recorded from samples immersed in a solution of rhodamine-6G (R6G), as well as, after exposure of the samples in xylene. SERS enhancement factors of up to ∼104 were obtained for both analytes. The measurements show that the SERS enhancement is maximized on golden strips produced at higher writing laser power and lower scanning speed, where closer nanoparticles packing is obtained..

Moderating the Covariance Between Family Member’s Substance Use Behavior

PubMed Central

Eaves, Lindon J.; Neale, Michael C.

2014-01-01

Twin and family studies implicitly assume that the covariation between family members remains constant across differences in age between the members of the family. However, age-specificity in gene expression for shared environmental factors could generate higher correlations between family members who are more similar in age. Cohort effects (cohort × genotype or cohort × common environment) could have the same effects, and both potentially reduce effect sizes estimated in genome-wide association studies where the subjects are heterogeneous in age. In this paper we describe a model in which the covariance between twins and non-twin siblings is moderated as a function of age difference. We describe the details of the model and simulate data using a variety of different parameter values to demonstrate that model fitting returns unbiased parameter estimates. Power analyses are then conducted to estimate the sample sizes required to detect the effects of moderation in a design of twins and siblings. Finally, the model is applied to data on cigarette smoking. We find that (1) the model effectively recovers the simulated parameters, (2) the power is relatively low and therefore requires large sample sizes before small to moderate effect sizes can be found reliably, and (3) the genetic covariance between siblings for smoking behavior decays very rapidly. Result 3 implies that, e.g., genome-wide studies of smoking behavior that use individuals assessed at different ages, or belonging to different birth-year cohorts may have had substantially reduced power to detect effects of genotype on cigarette use. It also implies that significant special twin environmental effects can be explained by age-moderation in some cases. This effect likely contributes to the missing heritability paradox. PMID:24647834

Food selection and feeding relationships of yellow perch 'Perca flavescens' (mitchell), white bass 'Morone chrysops' (rafinesque), freshwater drum 'Aplodinotus grunniens' (rafinesque), and goldfish 'Carassius auratus' (linneaus) in western Lake Erie. Interim report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kenaga, D.E.; Cole, R.A.

1975-10-01

The study was undertaken as part of an investigation of the impact of once through cooling at a large power plant in western Lake Erie and is an attempt to assess the relationship among fish based on foods consumed. Potential food organisms and stomach contents of yellow perch, white bass, freshwater drum and goldfish were sampled and compared over a two year period. On the basis of differences in food size alone, young of the year fish did not appear to be in competition but as they became larger, all but goldfish consumed the same mean size foods. Within amore » fish species, mean prey size varied little in fish older than age class zero. Goldfish differed markedly by lacking the prey size selectivity demonstrated by the other fish species. Some ramifications of food size and prey selectivity in relation to trophic dynamics, feeding efficiency, composition and distribution of fish species, and the use of cooling water by large power plants and their possible impact upon prey sizes are discussed. (GRA)« less

Automated system measuring triple oxygen and nitrogen isotope ratios in nitrate using the bacterial method and N2 O decomposition by microwave discharge.

PubMed

Hattori, Shohei; Savarino, Joel; Kamezaki, Kazuki; Ishino, Sakiko; Dyckmans, Jens; Fujinawa, Tamaki; Caillon, Nicolas; Barbero, Albane; Mukotaka, Arata; Toyoda, Sakae; Well, Reinhard; Yoshida, Naohiro

2016-12-30

Triple oxygen and nitrogen isotope ratios in nitrate are powerful tools for assessing atmospheric nitrate formation pathways and their contribution to ecosystems. N 2 O decomposition using microwave-induced plasma (MIP) has been used only for measurements of oxygen isotopes to date, but it is also possible to measure nitrogen isotopes during the same analytical run. The main improvements to a previous system are (i) an automated distribution system of nitrate to the bacterial medium, (ii) N 2 O separation by gas chromatography before N 2 O decomposition using the MIP, (iii) use of a corundum tube for microwave discharge, and (iv) development of an automated system for isotopic measurements. Three nitrate standards with sample sizes of 60, 80, 100, and 120 nmol were measured to investigate the sample size dependence of the isotope measurements. The δ 17 O, δ 18 O, and Δ 17 O values increased with increasing sample size, although the δ 15 N value showed no significant size dependency. Different calibration slopes and intercepts were obtained with different sample amounts. The slopes and intercepts for the regression lines in different sample amounts were dependent on sample size, indicating that the extent of oxygen exchange is also dependent on sample size. The sample-size-dependent slopes and intercepts were fitted using natural log (ln) regression curves, and the slopes and intercepts can be estimated to apply to any sample size corrections. When using 100 nmol samples, the standard deviations of residuals from the regression lines for this system were 0.5‰, 0.3‰, and 0.1‰, respectively, for the δ 18 O, Δ 17 O, and δ 15 N values, results that are not inferior to those from other systems using gold tube or gold wire. An automated system was developed to measure triple oxygen and nitrogen isotopes in nitrate using N 2 O decomposition by MIP. This system enables us to measure both triple oxygen and nitrogen isotopes in nitrate with comparable precision and sample throughput (23 min per sample on average), and minimal manual treatment. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Cracks and nanodroplets produced on tungsten surface samples by dense plasma jets

NASA Astrophysics Data System (ADS)

Ticoş, C. M.; Galaţanu, M.; Galaţanu, A.; Luculescu, C.; Scurtu, A.; Udrea, N.; Ticoş, D.; Dumitru, M.

2018-03-01

Small samples of 12.5 mm in diameter made from pure tungsten were exposed to a dense plasma jet produced by a coaxial plasma gun operated at 2 kJ. The surface of the samples was analyzed using a scanning electron microscope (SEM) before and after applying consecutive plasma shots. Cracks and craters were produced in the surface due to surface tensions during plasma heating. Nanodroplets and micron size droplets could be observed on the samples surface. An energy-dispersive spectroscopy (EDS) analysis revealed that the composition of these droplets coincided with that of the gun electrode material. Four types of samples were prepared by spark plasma sintering from powders with the average particle size ranging from 70 nanometers up to 80 μm. The plasma power load to the sample surface was estimated to be ≈4.7 MJ m-2 s-1/2 per shot. The electron temperature and density in the plasma jet had peak values 17 eV and 1.6 × 1022 m-3, respectively.

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GUIDELINES FOR THE APPLICATION OF SEM/EDX ANALYTICAL TECHNIQUES FOR FINE AND COARSE PM SAMPLES

EPA Science Inventory

Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray analysis (EDX) is a powerful tool in the characterization and source apportionment of environmental particulate matter (PM), providing size, chemistry, and morphology of particles as small as a few tenths ...

Underestimating extreme events in power-law behavior due to machine-dependent cutoffs

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2014-11-01

Power-law distributions are typical macroscopic features occurring in almost all complex systems observable in nature. As a result, researchers in quantitative analyses must often generate random synthetic variates obeying power-law distributions. The task is usually performed through standard methods that map uniform random variates into the desired probability space. Whereas all these algorithms are theoretically solid, in this paper we show that they are subject to severe machine-dependent limitations. As a result, two dramatic consequences arise: (i) the sampling in the tail of the distribution is not random but deterministic; (ii) the moments of the sample distribution, which are theoretically expected to diverge as functions of the sample sizes, converge instead to finite values. We provide quantitative indications for the range of distribution parameters that can be safely handled by standard libraries used in computational analyses. Whereas our findings indicate possible reinterpretations of numerical results obtained through flawed sampling methodologies, they also pave the way for the search for a concrete solution to this central issue shared by all quantitative sciences dealing with complexity.

Continuous Time Level Crossing Sampling ADC for Bio-Potential Recording Systems

PubMed Central

Tang, Wei; Osman, Ahmad; Kim, Dongsoo; Goldstein, Brian; Huang, Chenxi; Martini, Berin; Pieribone, Vincent A.

2013-01-01

In this paper we present a fixed window level crossing sampling analog to digital convertor for bio-potential recording sensors. This is the first proposed and fully implemented fixed window level crossing ADC without local DACs and clocks. The circuit is designed to reduce data size, power, and silicon area in future wireless neurophysiological sensor systems. We built a testing system to measure bio-potential signals and used it to evaluate the performance of the circuit. The bio-potential amplifier offers a gain of 53 dB within a bandwidth of 200 Hz-20 kHz. The input-referred rms noise is 2.8 µV. In the asynchronous level crossing ADC, the minimum delta resolution is 4 mV. The input signal frequency of the ADC is up to 5 kHz. The system was fabricated using the AMI 0.5 µm CMOS process. The chip size is 1.5 mm by 1.5 mm. The power consumption of the 4-channel system from a 3.3 V supply is 118.8 µW in the static state and 501.6 µW with a 240 kS/s sampling rate. The conversion efficiency is 1.6 nJ/conversion. PMID:24163640

Effect of Cr3+ substitution on AC susceptibility of Ba hexaferrite nanoparticles

NASA Astrophysics Data System (ADS)

Slimani, Y.; Baykal, A.; Manikandan, A.

2018-07-01

In this study, nano-sized particles of BaCrxFe12-xO19 (0.0 ≤ x ≤ 1.0) hexaferrite were fabricated through citrate auto gel combustion process and the impact of Cr-ion substitution on ac magnetic susceptibility properties of Ba-hexaferrite were explored. X-ray powder diffraction (XRD) measurements approved the purity of prepared samples and showed a reduction of the average crystallite size with increasing the content of Cr. Transmission electron microscopy (TEM) observation indicated the hexagonal morphology of all samples. AC susceptibility measurements displayed a frequency dependence of the magnetic responses. These measurements indicated that there are strong magnetic interactions (which is the highest for BaCr0.3Fe11.7O19 NP) between particles which cause a superspin glass-like (SSG) behavior at low temperatures. Estimating the values of loss power density revealed an increase of loss power density with increasing Cr-substitution element. The relative sensitivity of the prepared MNPs to the variation of applied frequency is very influenced by Cr-substitution and is highest in BaCr0.3Fe11.7O19 MNPs, suggesting that this sample can be considered as magnetic nanomaterial for hyperthermia and for many other applications.

Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes.

PubMed

Chen, Xiao; Lu, Bin; Yan, Chao-Gan

2018-01-01

Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect "true" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Estimating sample size for landscape-scale mark-recapture studies of North American migratory tree bats

USGS Publications Warehouse

Ellison, Laura E.; Lukacs, Paul M.

2014-01-01

Concern for migratory tree-roosting bats in North America has grown because of possible population declines from wind energy development. This concern has driven interest in estimating population-level changes. Mark-recapture methodology is one possible analytical framework for assessing bat population changes, but sample size requirements to produce reliable estimates have not been estimated. To illustrate the sample sizes necessary for a mark-recapture-based monitoring program we conducted power analyses using a statistical model that allows reencounters of live and dead marked individuals. We ran 1,000 simulations for each of five broad sample size categories in a Burnham joint model, and then compared the proportion of simulations in which 95% confidence intervals overlapped between and among years for a 4-year study. Additionally, we conducted sensitivity analyses of sample size to various capture probabilities and recovery probabilities. More than 50,000 individuals per year would need to be captured and released to accurately determine 10% and 15% declines in annual survival. To detect more dramatic declines of 33% or 50% survival over four years, then sample sizes of 25,000 or 10,000 per year, respectively, would be sufficient. Sensitivity analyses reveal that increasing recovery of dead marked individuals may be more valuable than increasing capture probability of marked individuals. Because of the extraordinary effort that would be required, we advise caution should such a mark-recapture effort be initiated because of the difficulty in attaining reliable estimates. We make recommendations for what techniques show the most promise for mark-recapture studies of bats because some techniques violate the assumptions of mark-recapture methodology when used to mark bats.

Test equality between two binary screening tests with a confirmatory procedure restricted on screen positives.

PubMed

Lui, Kung-Jong; Chang, Kuang-Chao

2015-01-01

In studies of screening accuracy, we may commonly encounter the data in which a confirmatory procedure is administered to only those subjects with screen positives for ethical concerns. We focus our discussion on simultaneously testing equality of sensitivity and specificity between two binary screening tests when only subjects with screen positives receive the confirmatory procedure. We develop four asymptotic test procedures and one exact test procedure. We derive sample size calculation formula for a desired power of detecting a difference at a given nominal [Formula: see text]-level. We employ Monte Carlo simulation to evaluate the performance of these test procedures and the accuracy of the sample size calculation formula developed here in a variety of situations. Finally, we use the data obtained from a study of the prostate-specific-antigen test and digital rectal examination test on 949 Black men to illustrate the practical use of these test procedures and the sample size calculation formula.

A Modified Kolmogorov-Smirnov, Anderson-Darling, and Cramer-Von Mises Test for the Cauchy Distribution with Unknown Location and Scale Parameters.

DTIC Science & Technology

1985-12-01

statistics, each of the a levels fall. The mirror image of this is to work with the percentiles, or the I - a levels . These then become the minimum...To be valid, the power would have to be close to the *-levels, and that Is the case. The powers are not exactly equal to the a - levels , but that is a...Information available increases with sample size. When a - levels are analyzed, for a = .0 1, the only reasonable power Is 33 L 4 against the

Micro Electron MicroProbe and Sample Analyzer

NASA Technical Reports Server (NTRS)

Manohara, Harish; Bearman, Gregory; Douglas, Susanne; Bronikowski, Michael; Urgiles, Eduardo; Kowalczyk, Robert; Bryson, Charles

2009-01-01

A proposed, low-power, backpack-sized instrument, denoted the micro electron microprobe and sample analyzer (MEMSA), would serve as a means of rapidly performing high-resolution microscopy and energy-dispersive x-ray spectroscopy (EDX) of soil, dust, and rock particles in the field. The MEMSA would be similar to an environmental scanning electron microscope (ESEM) but would be much smaller and designed specifically for field use in studying effects of geological alteration at the micrometer scale. Like an ESEM, the MEMSA could be used to examine uncoated, electrically nonconductive specimens. In addition to the difference in size, other significant differences between the MEMSA and an ESEM lie in the mode of scanning and the nature of the electron source.

Properties of low power spark ablation in aqueous solution for dissolution of precious metals and alloys

NASA Astrophysics Data System (ADS)

Goltz, Douglas; Boileau, Michael; Plews, Ian; Charleton, Kimberly; Hinds, Michael W.

2006-07-01

Spark ablation or electric dispersion of metal samples in aqueous solution can be a useful approach for sample preparation. The ablated metal forms a stable suspension that has been described as colloidal, which is easily dissolved with a small amount of concentrated (16 M) HNO 3. In this study, we have examined some of the properties of the spark ablation process for a variety of metals (Rh and Au) and alloys (stainless steel) using a low power spark (100-300 W). Particle size distributions and conductivity measurements were carried out on selected metals to characterize the stable suspensions. A LASER diffraction particle size analyzer was useful for showing that ablated particles varied in size from 1 to 30 μm for both the silver and the nickel alloy, Inconel. In terms of weight percent most of the particles were between 10 and 30 μm. Conductivity of the spark ablation solution was found to increase linearly for approximately 3 min before leveling off at approximately 300 S cm 3. These measurements suggest that a significant portion of the ablated metal is also ionic in nature. Scanning electron microscope measurements revealed that a low power spark is much less damaging to the metal surface than a high power spark. Crater formation of the low power spark was found in a wider area than expected with the highest concentration where the spark was directed. The feasibility of using spark ablation for metal dissolution of a valuable artifact such as gold was also performed. Determinations of Ag (4-12%) and Cu (1-3%) in Bullion Reference Material (BRM) gave results that were in very good agreement with the certified values. The precision was ± 0.27% for Ag at 4.15% (RSD = 6.5%) and ± 0.09% for Cu at 1% (RSD = 9.0%).

Oxidation behaviour of Fe-Ni alloy nanoparticles synthesized by thermal plasma route

NASA Astrophysics Data System (ADS)

Ghodke, Neha; Kamble, Shalaka; Raut, Suyog; Puranik, Shridhar; Bhoraskar, S. V.; Rayaprol, Sudhindra; Mathe, V. L.

2018-04-01

Here we report synthesis of Fe-Ni nanoparticles using thermal plasma route. In thermal plasma, gas phase nucleation and growth at sufficiently higher temperature is observed. The synthesized Fe-Ni nanoparticles are examined by X-ray Diffraction, Raman Spectroscopy, Vibrating Sample Magnetometer and Thermo gravimetric Analysis. Formation of 16-21 nm sized Fe-Ni nanoparticles having surface oxidation show maximum value of magnetization of ˜107 emu/g. The sample synthesized at relatively low power (4kW) show presence of carbonaceous species whereas the high power (6 kW) synthesis does not depicts carbonaceous species. The presence of carbonaceous species protects oxidation of the nanoparticles significantly as evidenced from TGA data.

The Universal Multizone Crystallizator (UMC) Furnace: An International Cooperative Agreement

NASA Technical Reports Server (NTRS)

Watring, D. A.; Su, C.-H.; Gillies, D.; Roosz, T.; Babcsan, N.

1996-01-01

The Universal Multizone Crystallizator (UMC) is a special apparatus for crystal growth under terrestrial and microgravity conditions. The use of twenty-five zones allows the UMC to be used for several normal freezing growth techniques. The thermal profile is electronically translated along the stationary sample by systematically reducing the power to the control zones. Elimination of mechanical translation devices increases the systems reliability while simultaneously reducing the size and weight. This paper addresses the UMC furnace design, sample cartridge and typical thermal profiles and corresponding power requirements necessary for the dynamic gradient freeze crystal growth technique. Results from physical vapor transport and traveling heater method crystal growth experiments are also discussed.

Water column and bed-sediment core samples collected from Brownlee Reservoir near Oxbow, Oregon, 2012

USGS Publications Warehouse

Fosness, Ryan L.; Naymik, Jesse; Hopkins, Candice B.; DeWild, John F.

2013-01-01

The U.S. Geological Survey, in cooperation with Idaho Power Company, collected water-column and bed-sediment core samples from eight sites in Brownlee Reservoir near Oxbow, Oregon, during May 5–7, 2012. Water-column and bed-sediment core samples were collected at each of the eight sites and analyzed for total mercury and methylmercury. Additional bed-sediment core samples, collected from three of the eight sites, were analyzed for pesticides and other organic compounds, trace metals, and physical characteristics, such as particle size. Total mercury and methylmercury were detected in each of the water column and bed-sediment core samples. Only 17 of the 417 unique pesticide and organic compounds were detected in bed-sediment core samples. Concentrations of most organic wastewater compounds detected in bed sediment were less than the reporting level. Trace metals detected were greater than the reporting level in all the bed-sediment core samples submitted for analysis. The particle size distribution of bed-sediment core samples was predominantly clay mixed with silt.

POWER AND SAMPLE SIZE CALCULATIONS FOR LINEAR HYPOTHESES ASSOCIATED WITH MIXTURES OF MANY COMPONENTS USING FIXED-RATIO RAY DESIGNS

EPA Science Inventory

Response surface methodology, often supported by factorial designs, is the classical experimental approach that is widely accepted for detecting and characterizing interactions among chemicals in a mixture. In an effort to reduce the experimental effort as the number of compound...

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THE POWER TO DETECT A DIFFERENCE: DETERMINING SAMPLE SIZE REQUIREMENTS FOR EVALUATION OF REPRODUCTIVE/DEVELOPMENTAL EFFECTS FROM EXPOSURE TO COMPLEX MIXTURES OF DISINFECTION BYPRODUCTS

EPA Science Inventory

Toxicological assessment of environmentally-realistic complex mixtures of drinking-water disinfection byproducts (DBPs) are needed to address concerns raised by some epidemiological studies showing associations between exposure to chemically disinfected water and adverse reproduc...

Intraclass Correlation Values for Planning Group-Randomized Trials in Education

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, E. C.

2007-01-01

Experiments that assign intact groups to treatment conditions are increasingly common in social research. In educational research, the groups assigned are often schools. The design of group-randomized experiments requires knowledge of the intraclass correlation structure to compute statistical power and sample sizes required to achieve adequate…

On the Treatment of Authors, Outliers, and Purchasing Power Parity Exchange Rates.

ERIC Educational Resources Information Center

Jaeger, Richard M.

1993-01-01

Ruth Stott violates canons of scholarly debate by attacking author's October 1992 "Kappan" article on world-class academic standards. Average class size predicted only 10% of variation in 13 year-olds' mean mathematics scores in 14 nations supplying reasonable comprehensive sampling frames for International Assessment of Academic…

TNOs as probes of planet building: the Plutino size- & colour-distributions

NASA Astrophysics Data System (ADS)

Alexandersen, Mike; Gladman, Brett; Kavelaars, JJ; Petit, Jean-Marc; Gwyn, Stephen; Pike, Rosemary E.; Shankman, Cory

2015-01-01

Planetesimals are the building blocks of giant planet cores; some are preserved as large transneptunian objects (TNOs). Previous work concluded steep power-law size-distributions for TNOs of diameters > 100 km. Recent results claim a dramatic roll-over or divot (sudden drop in number of objects at a transition size) in the size-distribution of Neptunian Trojans and scattering TNOs, with a significant lack of intermediate-size D<100 km planetesimals. One theoretical explanation is that planetesimals were born big, skipping the intermediate sizes, contrary to the expectation of bottom-up planetesimal formation.Using the Canada-France-Hawaii Telescope, our 32 sq.deg. survey, near RA=2 hr with limiting magnitude m_r=24.6, detected and tracked 77 TNOs and Centaurs for up to 28 months, providing both the high-quality orbits and the quantitative detection efficiency needed for precise modelling. We used the 18 Plutinos (3:2 Neptunian mean motion resonance) from our survey to constrain the size- and orbital-distribution model of this population. We show that the Plutino size-distribution cannot continue as a rising power-law past H_r ˜ 8.3 (D˜ 100 km); a sharp dramatic change must occur near this point. A single power-law is rejectable at >99% confidence; a double power law cannot be rejected outright, but appears to be a uncomfortable match to the available data. A divot, with the parameters found independently for scattering TNOs by Shankman et al. (2013, ApJ vol 764), provides an excellent match; the best match, found from an extensive parameter search, comes with only slightly different parameters; this size-distribution also satisfies the known Neptunian Trojan data.We also present g-r photometric colours for our Plutino sample, obtained with the Gemini North telescope in 2013-2014.Both large TNOs and small nearby Centaurs are known to feature a bimodal colour-distribution; however, recent work (Peixinho et al. 2012, A&A vol 546) has suggested that intermediate-size TNOs may not show bimodality. Our telescopically-expensive endeavour has provided us with unique insight into the colour-distribution of the physically smallest Plutinos.

TNOs as probes of planet building: the Plutino size- & colour-distributions

NASA Astrophysics Data System (ADS)

Alexandersen, Mike; Gladman, Brett; Kavelaars, Jj; Petit, Jean-Marc; Gwyn, Stephen; Shankman, Cory; Pike, Rosemary

2014-11-01

Planetesimals are the building blocks of giant planet cores; some are preserved as large transneptunian objects (TNOs). Previous work concluded steep power-law size-distributions for TNOs of diameters > 100 km. Recent results claim a dramatic roll-over or divot (sudden drop in number of objects at a transition size) in the size-distribution of Neptunian Trojans and scattering TNOs, with a significant lack of intermediate-size D<100 km planetesimals. One theoretical explanation is that planetesimals were born big, skipping the intermediate sizes, contrary to the expectation of bottom-up planetesimal formation. Using the Canada-France-Hawaii Telescope, our 32 sq.deg. survey, near RA=2 hr with limiting magnitude m_r=24.6, detected and tracked 77 TNOs and Centaurs for up to 28 months, providing both the high-quality orbits and the quantitative detection efficiency needed for precise modelling. We used the 18 Plutinos (3:2 Neptunian resonance) from our survey to constrain the size- and orbital-distribution model of this population. We show that the Plutino size-distribution cannot continue as a rising power-law past H_r˜ 8.3 (D˜ 100 km); a sharp dramatic change must occur near this point. A single power-law is rejectable at >99% confidence; a double power law cannot be rejected outright, but appears to be a uncomfortable match to the available data. A divot, with the parameters found independently for scattering TNOs by Shankman et al. (2013, ApJ vol 764), provides an excellent match; the best match, found from an extensive parameter search, comes with only slightly different parameters; this size-distribution also satisfies the known Neptunian Trojan data. Both large TNOs and small nearby Centaurs are known to feature a bimodal colour-distribution; however, recent work (Peixinho et al. 2012, A&A vol 546) has suggested that intermediate-size TNOs may not show bimodality. We present g-r photometric colours for our Plutino sample, obtained with the Gemini North telescope in 2013-2014. This telescopically-expensive endeavour has provided us with unique insight into the colour-distribution of the physically smallest Plutinos.

Sampling through time and phylodynamic inference with coalescent and birth–death models

PubMed Central

Volz, Erik M.; Frost, Simon D. W.

2014-01-01

Many population genetic models have been developed for the purpose of inferring population size and growth rates from random samples of genetic data. We examine two popular approaches to this problem, the coalescent and the birth–death-sampling model (BDM), in the context of estimating population size and birth rates in a population growing exponentially according to the birth–death branching process. For sequences sampled at a single time, we found the coalescent and the BDM gave virtually indistinguishable results in terms of the growth rates and fraction of the population sampled, even when sampling from a small population. For sequences sampled at multiple time points, we find that the birth–death model estimators are subject to large bias if the sampling process is misspecified. Since BDMs incorporate a model of the sampling process, we show how much of the statistical power of BDMs arises from the sequence of sample times and not from the genealogical tree. This motivates the development of a new coalescent estimator, which is augmented with a model of the known sampling process and is potentially more precise than the coalescent that does not use sample time information. PMID:25401173

FT-IR and Zeta potential measurements on TiO nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Jaiveer; Rathore, Ravi; Kaurav, Netram, E-mail: netramkaurav@yahoo.co.uk

2016-05-23

In the present investigation, ultrafine TiO particles have been synthesized successfully by thermal decomposition method. The sample was characterized by X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. As-synthesized TiO nanoparticles have a cubic structure as characterized by power X-ray diffraction (XRD), which shows that TiO nanoparticles have narrow size distribution with particle size 11.5 nm. FTIR data shows a strong peak at 1300 cm{sup −1}, assignable to the Ti-O stretching vibrations mode.

Potential Reporting Bias in Neuroimaging Studies of Sex Differences.

PubMed

David, Sean P; Naudet, Florian; Laude, Jennifer; Radua, Joaquim; Fusar-Poli, Paolo; Chu, Isabella; Stefanick, Marcia L; Ioannidis, John P A

2018-04-17

Numerous functional magnetic resonance imaging (fMRI) studies have reported sex differences. To empirically evaluate for evidence of excessive significance bias in this literature, we searched for published fMRI studies of human brain to evaluate sex differences, regardless of the topic investigated, in Medline and Scopus over 10 years. We analyzed the prevalence of conclusions in favor of sex differences and the correlation between study sample sizes and number of significant foci identified. In the absence of bias, larger studies (better powered) should identify a larger number of significant foci. Across 179 papers, median sample size was n = 32 (interquartile range 23-47.5). A median of 5 foci related to sex differences were reported (interquartile range, 2-9.5). Few articles (n = 2) had titles focused on no differences or on similarities (n = 3) between sexes. Overall, 158 papers (88%) reached "positive" conclusions in their abstract and presented some foci related to sex differences. There was no statistically significant relationship between sample size and the number of foci (-0.048% increase for every 10 participants, p = 0.63). The extremely high prevalence of "positive" results and the lack of the expected relationship between sample size and the number of discovered foci reflect probable reporting bias and excess significance bias in this literature.

Estimating the breeding population of long-billed curlew in the United States

USGS Publications Warehouse

Stanley, T.R.; Skagen, S.K.

2007-01-01

Determining population size and long-term trends in population size for species of high concern is a priority of international, national, and regional conservation plans. Long-billed curlews (Numenius americanus) are a species of special concern in North America due to apparent declines in their population. Because long-billed curlews are not adequately monitored by existing programs, we undertook a 2-year study with the goals of 1) determining present long-billed curlew distribution and breeding population size in the United States and 2) providing recommendations for a long-term long-billed curlew monitoring protocol. We selected a stratified random sample of survey routes in 16 western states for sampling in 2004 and 2005, and we analyzed count data from these routes to estimate detection probabilities and abundance. In addition, we evaluated habitat along roadsides to determine how well roadsides represented habitat throughout the sampling units. We estimated there were 164,515 (SE = 42,047) breeding long-billed curlews in 2004, and 109,533 (SE = 31,060) breeding individuals in 2005. These estimates far exceed currently accepted estimates based on expert opinion. We found that habitat along roadsides was representative of long-billed curlew habitat in general. We make recommendations for improving sampling methodology, and we present power curves to provide guidance on minimum sample sizes required to detect trends in abundance.

Temporal change in the size distribution of airborne Radiocesium derived from the Fukushima accident

NASA Astrophysics Data System (ADS)

Kaneyasu, Naoki; Ohashi, Hideo; Suzuki, Fumie; Okuda, Tomoaki; Ikemori, Fumikazu; Akata, Naofumi

2013-04-01

The accident of Fukushima Dai-ichi nuclear power plant discharged a large amount of radioactive materials into the environment. After 40 days of the accident, we started to collect the size-segregated aerosol at Tsukuba City, Japan, located 170 km south of the plant, by use of a low-pressure cascade impactor. The sampling continued from April 28, through October 26, 2011. The number of sample sets collected in total was 8. The radioactivity of 134Cs and 137Cs in aerosols collected at each stage were determined by gamma-ray with a high sensitivity Germanic detector. After the gamma-ray spectrometry analysis, the chemical species in the aerosols were analyzed. The analyses of first (April 28-May 12) and second (May 12-26) samples showed that the activity size distributions of 134Cs and 137Cs in aerosols reside mostly in the accumulation mode size range. These activity size distributions almost overlapped with the mass size distribution of non-sea-salt sulfate aerosol. From the results, we regarded that sulfate is the main transport medium of these radionuclides, and re-suspended soil particles that attached radionuclides were not the major airborne radioactive substances by the end of May, 2011 (Kaneyasu et al., 2012). We further conducted the successive extraction experiment of radiocesium from the aerosol deposits on the aluminum sheet substrate (8th stage of the first aerosol sample, 0.5-0.7 μm in aerodynamic diameter) with water and 0.1M HCl. In contrast to the relatively insoluble property of Chernobyl radionuclides, those in aerosols collected at Tsukuba in fine mode are completely water-soluble (100%). From the third aerosol sample, the activity size distributions started to change, i.e., the major peak in the accumulation mode size range seen in the first and second aerosol samples became smaller and an additional peak appeared in the coarse mode size range. The comparison of the activity size distributions of radiocesium and the mass size distributions of major aerosol components collected by the end of August, 2011, (i.e., sample No.5) and its implication will be discussed in the presentation. Reference Kaneyasu et al., Environ. Sci. Technol. 46, 5720-5726 (2012).

High Ice Water Content at Low Radar Reflectivity near Deep Convection. Part I ; Consistency of In Situ and Remote-Sensing Observations with Stratiform Rain Column Simulations

NASA Technical Reports Server (NTRS)

Fridlind, A. M.; Ackerman, A. S.; Grandin, A.; Dezitter, F.; Weber, M.; Strapp, J. W.; Korolev, A. V.; Williams, C. R.

2015-01-01

Occurrences of jet engine power loss and damage have been associated with flight through fully glaciated deep convection at -10 to -50 degrees Centigrade. Power loss events commonly occur during flight through radar reflectivity (Zeta (sub e)) less than 20-30 decibels relative to Zeta (dBZ - radar returns) and no more than moderate turbulence, often overlying moderate to heavy rain near the surface. During 2010-2012, Airbus carried out flight tests seeking to characterize the highest ice water content (IWC) in such low-radar-reflectivity regions of large, cold-topped storm systems in the vicinity of Cayenne, Darwin, and Santiago. Within the highest IWC regions encountered, at typical sampling elevations (circa 11 kilometers), the measured ice size distributions exhibit a notably narrow concentration of mass over area-equivalent diameters of 100-500 micrometers. Given substantial and poorly quantified measurement uncertainties, here we evaluate the consistency of the Airbus in situ measurements with ground-based profiling radar observations obtained under quasi-steady, heavy stratiform rain conditions in one of the Airbus-sampled locations. We find that profiler-observed radar reflectivities and mean Doppler velocities at Airbus sampling temperatures are generally consistent with those calculated from in situ size-distribution measurements. We also find that column simulations using the in situ size distributions as an upper boundary condition are generally consistent with observed profiles of radar reflectivity (Ze), mean Doppler velocity (MDV), and retrieved rain rate. The results of these consistency checks motivate an examination of the microphysical pathways that could be responsible for the observed size-distribution features in Ackerman et al. (2015).

Nintendo Wii Fit as an adjunct to physiotherapy following lower limb fractures: preliminary feasibility, safety and sample size considerations.

PubMed

McPhail, S M; O'Hara, M; Gane, E; Tonks, P; Bullock-Saxton, J; Kuys, S S

2016-06-01

The Nintendo Wii Fit integrates virtual gaming with body movement, and may be suitable as an adjunct to conventional physiotherapy following lower limb fractures. This study examined the feasibility and safety of using the Wii Fit as an adjunct to outpatient physiotherapy following lower limb fractures, and reports sample size considerations for an appropriately powered randomised trial. Ambulatory patients receiving physiotherapy following a lower limb fracture participated in this study (n=18). All participants received usual care (individual physiotherapy). The first nine participants also used the Wii Fit under the supervision of their treating clinician as an adjunct to usual care. Adverse events, fracture malunion or exacerbation of symptoms were recorded. Pain, balance and patient-reported function were assessed at baseline and discharge from physiotherapy. No adverse events were attributed to either the usual care physiotherapy or Wii Fit intervention for any patient. Overall, 15 (83%) participants completed both assessments and interventions as scheduled. For 80% power in a clinical trial, the number of complete datasets required in each group to detect a small, medium or large effect of the Wii Fit at a post-intervention assessment was calculated at 175, 63 and 25, respectively. The Nintendo Wii Fit was safe and feasible as an adjunct to ambulatory physiotherapy in this sample. When considering a likely small effect size and the 17% dropout rate observed in this study, 211 participants would be required in each clinical trial group. A larger effect size or multiple repeated measures design would require fewer participants. Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Multi-locus analysis of genomic time series data from experimental evolution.

PubMed

Terhorst, Jonathan; Schlötterer, Christian; Song, Yun S

2015-04-01

Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. We first use simulated data to demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. We also explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate. Then, we apply our method to analyze genome-wide data from a real E&R experiment designed to study the adaptation of D. melanogaster to a new laboratory environment with alternating cold and hot temperatures.

EFFECT OF ENVIRONMENT ON GALAXIES' MASS-SIZE DISTRIBUTION: UNVEILING THE TRANSITION FROM OUTSIDE-IN TO INSIDE-OUT EVOLUTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cappellari, Michele

2013-11-20

The distribution of galaxies on the mass-size plane as a function of redshift or environment is a powerful test for galaxy formation models. Here we use integral-field stellar kinematics to interpret the variation of the mass-size distribution in two galaxy samples spanning extreme environmental densities. The samples are both identically and nearly mass-selected (stellar mass M {sub *} ≳ 6 × 10{sup 9} M {sub ☉}) and volume-limited. The first consists of nearby field galaxies from the ATLAS{sup 3D} parent sample. The second consists of galaxies in the Coma Cluster (Abell 1656), one of the densest environments for which good, resolvedmore » spectroscopy can be obtained. The mass-size distribution in the dense environment differs from the field one in two ways: (1) spiral galaxies are replaced by bulge-dominated disk-like fast-rotator early-type galaxies (ETGs), which follow the same mass-size relation and have the same mass distribution as in the field sample; (2) the slow-rotator ETGs are segregated in mass from the fast rotators, with their size increasing proportionally to their mass. A transition between the two processes appears around the stellar mass M {sub crit} ≈ 2 × 10{sup 11} M {sub ☉}. We interpret this as evidence for bulge growth (outside-in evolution) and bulge-related environmental quenching dominating at low masses, with little influence from merging. In contrast, significant dry mergers (inside-out evolution) and halo-related quenching drives the mass and size growth at the high-mass end. The existence of these two processes naturally explains the diverse size evolution of galaxies of different masses and the separability of mass and environmental quenching.« less

Differential expression analysis for RNAseq using Poisson mixed models

PubMed Central

Sun, Shiquan; Hood, Michelle; Scott, Laura; Peng, Qinke; Mukherjee, Sayan; Tung, Jenny

2017-01-01

Abstract Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html. PMID:28369632

Quality control considerations for size exclusion chromatography with online ICP-MS: a powerful tool for evaluating the size dependence of metal-organic matter complexation.

PubMed

McKenzie, Erica R; Young, Thomas M

2013-01-01

Size exclusion chromatography (SEC), which separates molecules based on molecular volume, can be coupled with online inductively coupled plasma mass spectrometry (ICP-MS) to explore size-dependent metal-natural organic matter (NOM) complexation. To make effective use of this analytical dual detector system, the operator should be mindful of quality control measures. Al, Cr, Fe, Se, and Sn all exhibited columnless attenuation, which indicated unintended interactions with system components. Based on signal-to-noise ratio and peak reproducibility between duplicate analyses of environmental samples, consistent peak time and height were observed for Mg, Cl, Mn, Cu, Br, and Pb. Al, V, Fe, Co, Ni, Zn, Se, Cd, Sn, and Sb were less consistent overall, but produced consistent measurements in select samples. Ultrafiltering and centrifuging produced similar peak distributions, but glass fiber filtration produced more high molecular weight (MW) peaks. Storage in glass also produced more high MW peaks than did plastic bottles.

Is psychology suffering from a replication crisis? What does "failure to replicate" really mean?

PubMed

Maxwell, Scott E; Lau, Michael Y; Howard, George S

2015-09-01

Psychology has recently been viewed as facing a replication crisis because efforts to replicate past study findings frequently do not show the same result. Often, the first study showed a statistically significant result but the replication does not. Questions then arise about whether the first study results were false positives, and whether the replication study correctly indicates that there is truly no effect after all. This article suggests these so-called failures to replicate may not be failures at all, but rather are the result of low statistical power in single replication studies, and the result of failure to appreciate the need for multiple replications in order to have enough power to identify true effects. We provide examples of these power problems and suggest some solutions using Bayesian statistics and meta-analysis. Although the need for multiple replication studies may frustrate those who would prefer quick answers to psychology's alleged crisis, the large sample sizes typically needed to provide firm evidence will almost always require concerted efforts from multiple investigators. As a result, it remains to be seen how many of the recently claimed failures to replicate will be supported or instead may turn out to be artifacts of inadequate sample sizes and single study replications. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Prevalence of diseases and statistical power of the Japan Nurses' Health Study.

PubMed

Fujita, Toshiharu; Hayashi, Kunihiko; Katanoda, Kota; Matsumura, Yasuhiro; Lee, Jung Su; Takagi, Hirofumi; Suzuki, Shosuke; Mizunuma, Hideki; Aso, Takeshi

2007-10-01

The Japan Nurses' Health Study (JNHS) is a long-term, large-scale cohort study investigating the effects of various lifestyle factors and healthcare habits on the health of Japanese women. Based on currently limited statistical data regarding the incidence of disease among Japanese women, our initial sample size was tentatively set at 50,000 during the design phase. The actual number of women who agreed to participate in follow-up surveys was approximately 18,000. Taking into account the actual sample size and new information on disease frequency obtained during the baseline component, we established the prevalence of past diagnoses of target diseases, predicted their incidence, and calculated the statistical power for JNHS follow-up surveys. For all diseases except ovarian cancer, the prevalence of a past diagnosis increased markedly with age, and incidence rates could be predicted based on the degree of increase in prevalence between two adjacent 5-yr age groups. The predicted incidence rate for uterine myoma, hypercholesterolemia, and hypertension was > or =3.0 (per 1,000 women, per year), while the rate of thyroid disease, hepatitis, gallstone disease, and benign breast tumor was predicted to be > or =1.0. For these diseases, the statistical power to detect risk factors with a relative risk of 1.5 or more within ten years, was 70% or higher.

Smart Sampling and HPC-based Probabilistic Look-ahead Contingency Analysis Implementation and its Evaluation with Real-world Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yousu; Etingov, Pavel V.; Ren, Huiying

This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested withmore » real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.« less

Sample size calculations for stepped wedge and cluster randomised trials: a unified approach

PubMed Central

Hemming, Karla; Taljaard, Monica

2016-01-01

Objectives To clarify and illustrate sample size calculations for the cross-sectional stepped wedge cluster randomized trial (SW-CRT) and to present a simple approach for comparing the efficiencies of competing designs within a unified framework. Study Design and Setting We summarize design effects for the SW-CRT, the parallel cluster randomized trial (CRT), and the parallel cluster randomized trial with before and after observations (CRT-BA), assuming cross-sectional samples are selected over time. We present new formulas that enable trialists to determine the required cluster size for a given number of clusters. We illustrate by example how to implement the presented design effects and give practical guidance on the design of stepped wedge studies. Results For a fixed total cluster size, the choice of study design that provides the greatest power depends on the intracluster correlation coefficient (ICC) and the cluster size. When the ICC is small, the CRT tends to be more efficient; when the ICC is large, the SW-CRT tends to be more efficient and can serve as an alternative design when the CRT is an infeasible design. Conclusion Our unified approach allows trialists to easily compare the efficiencies of three competing designs to inform the decision about the most efficient design in a given scenario. PMID:26344808

Using simulation to aid trial design: Ring-vaccination trials.

PubMed

Hitchings, Matt David Thomas; Grais, Rebecca Freeman; Lipsitch, Marc

2017-03-01

The 2014-6 West African Ebola epidemic highlights the need for rigorous, rapid clinical trial methods for vaccines. A challenge for trial design is making sample size calculations based on incidence within the trial, total vaccine effect, and intracluster correlation, when these parameters are uncertain in the presence of indirect effects of vaccination. We present a stochastic, compartmental model for a ring vaccination trial. After identification of an index case, a ring of contacts is recruited and either vaccinated immediately or after 21 days. The primary outcome of the trial is total vaccine effect, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. Simulation results are used to calculate necessary sample size and estimated vaccine effect. Under baseline assumptions about vaccine properties, monthly incidence in unvaccinated rings and trial design, a standard sample-size calculation neglecting dynamic effects estimated that 7,100 participants would be needed to achieve 80% power to detect a difference in attack rate between arms, while incorporating dynamic considerations in the model increased the estimate to 8,900. This approach replaces assumptions about parameters at the ring level with assumptions about disease dynamics and vaccine characteristics at the individual level, so within this framework we were able to describe the sensitivity of the trial power and estimated effect to various parameters. We found that both of these quantities are sensitive to properties of the vaccine, to setting-specific parameters over which investigators have little control, and to parameters that are determined by the study design. Incorporating simulation into the trial design process can improve robustness of sample size calculations. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting.

Numerical study of ultra-low field nuclear magnetic resonance relaxometry utilizing a single axis magnetometer for signal detection.

PubMed

Vogel, Michael W; Vegh, Viktor; Reutens, David C

2013-05-01

This paper investigates optimal placement of a localized single-axis magnetometer for ultralow field (ULF) relaxometry in view of various sample shapes and sizes. The authors used finite element method for the numerical analysis to determine the sample magnetic field environment and evaluate the optimal location of the single-axis magnetometer. Given the different samples, the authors analysed the magnetic field distribution around the sample and determined the optimal orientation and possible positions of the sensor to maximize signal strength, that is, the power of the free induction decay. The authors demonstrate that a glass vial with flat bottom and 10 ml volume is the best structure to achieve the highest signal out of samples studied. This paper demonstrates the importance of taking into account the combined effects of sensor configuration and sample parameters for signal generation prior to designing and constructing ULF systems with a single-axis magnetometer. Through numerical simulations the authors were able to optimize structural parameters, such as sample shape and size, sensor orientation and location, to maximize the measured signal in ultralow field relaxometry.

Regulatory environment and its impact on the market value of investor-owned electric utilities

NASA Astrophysics Data System (ADS)

Vishwanathan, Raman

While other regulated industries have one by one been exposed to competitive reform, electric power, for over eighty years, has remained a great monopoly. For all those years, the vertically integrated suppliers of electricity in the United States have been assigned exclusive territorial (consumer) franchises and have been closely regulated. This environment is in the process change because the electric power industry is currently undergoing some dramatic adjustments. Since 1992, a number of states have initiated regulatory reform and are moving to allow retail customers to choose their energy supplier. There has also been a considerable federal government role in encouraging competition in the generation and transmission of electricity. The objective of this research is to investigate the reaction of investors to the prevailing regulatory environment in the electric utility industry by analyzing the market-to-book value for investor-owned electric utilities in the United States as a gauge of investor concern or support for change. In this study, the variable of interest is the market valuation of utilities, as it captures investor confidence to changes in the regulatory environment. Initially a classic regression model is analyzed on the full sample (of the 96 investor-owned utilities for the years 1992 through 1996), providing a total number of 480 (96 firms over 5 years) observations. Later fixed- and random-effects models are analyzed for the same full-sample model specified in the previous analysis. Also, the analysis is carried forward to examine the impact of the size of the utility and its degree of reliability on nuclear power generation on market values. In the period of this study, 1992--1996, the financial security markets downgraded utilities that were still operating in a regulated environment or had a substantial percentage of their power generation from nuclear power plants. It was also found that the financial market was sensitive to the size of the electric utility. The negative impact of the regulatory environment declined with the increase in the size of the utility, indicating favorable treatment for larger utilities by financial markets. Similarly, for the electric utility industry as a whole, financial markets reacted negatively to nuclear power generation.

A new study of muons in air showers by NBU air shower array

NASA Technical Reports Server (NTRS)

Chaudhuri, N.; Mukherjee, N.; Sarkar, S.; Basak, D. K.; Ghosh, B.

1985-01-01

The North Bengal University (NBU) air shower array has been in operation in conjunction with two muon magnetic spectrographs. The array incorporates 21 particle density sampling detectors around the magnetic spectrographs covering an area of 900 sq m. The layout of the array is based on the arrangement of detectors in a square symmetry. The array set up on the ground level is around a 10 m high magnetic spectrograph housing. This magnetic spectrograph housing limits the zenith angular acceptance of the incident showers to a few degrees. Three hundred muons in the fitted showers of size range 10 to the 4th power to 10 to the 5th power particles have so far been scanned and the momenta determined in the momentum range 2 - 440 GeV/c. More than 1500 recorded showers are now in the process of scanning and fitting. A lateral distribution of muons of energy greater than 300 MeV in the shower size range 10 to the 5th power to 7 x 10 to the 5th power has been obtained.

[Effects of temperature increase on zooplankton size spectra in thermal discharge seawaters near a power plant, China].

PubMed

Yu, Jing; Zhu, Yi Feng; Dai, Mei Xia; Lin, Xia; Mao, Shuo Qian

2017-05-18

Utilizing the plankton 1 (505 Μm), 2 (160 Μm), 3 (77 Μm) nets to seasonally collect zooplankton samples at 10 stations and the corresponding abundance data was obtained. Based on individual zooplankton biovolume, size groups were classified to test the changes in spatiotemporal characteristics of both Sheldon and normalized biovolume size spectra in thermal discharge seawaters near the Guohua Power Plant, so as to explore the effects of temperature increase on zooplankton size spectra in the seawaters. The results showed that the individual biovolume of zooplankton ranged from 0.00012 to 127.0 mm 3 ·ind -1 , which could be divided into 21 size groups, and corresponding logarithmic ranges were from -13.06 to 6.99. According to Sheldon size spectra, the predominant species to form main peaks of the size spectrum in different months were Copepodite larvae, Centropages mcmurrichi, Calanus sinicus, fish larvae, Sagitta bedoti, Sagitta nagae and Pleurobrachia globosa, and minor peaks mostly consisted of individuals with smaller larvae, Cyclops and Paracalanus aculeatus. In different warming sections, Copepodite larvae, fish eggs and Cyclops were mostly unaffected by the temperature increase, while the macrozooplankton such as S. bedoti, S. nagae, P. globosa, C. sinicus and Beroe cucumis had an obvious tendency to avoid the outfall of the power plant. Based on the results of normalized size spectra, the intercepts from low to high occurred in November, February, May and August, respectively. At the same time, the minimum slope was found in February, and similarly bigger slopes were observed in May and August. These results indicated that the proportion of small zooplankton was highest in February, while the proportions of the meso- and macro-zooplankton were relatively high in May and August. Among different sections, the slope in the 0.2 km section was minimum, which increased with the increase of section distance to the outfall. The result obviously demonstrated that the closer the distance was from outfall of the power plant, the smaller the zooplankton became. On the whole, the average intercept of normalized size spectrum in Xiangshan Bay was 4.68, and the slope was -0.655.

Hindlimb muscle architecture in non-human great apes and a comparison of methods for analysing inter-species variation

PubMed Central

Myatt, Julia P; Crompton, Robin H; Thorpe, Susannah K S

2011-01-01

By relating an animal's morphology to its functional role and the behaviours performed, we can further develop our understanding of the selective factors and constraints acting on the adaptations of great apes. Comparison of muscle architecture between different ape species, however, is difficult because only small sample sizes are ever available. Further, such samples are often comprised of different age–sex classes, so studies have to rely on scaling techniques to remove body mass differences. However, the reliability of such scaling techniques has been questioned. As datasets increase in size, more reliable statistical analysis may eventually become possible. Here we employ geometric and allometric scaling techniques, and ancovas (a form of general linear model, GLM) to highlight and explore the different methods available for comparing functional morphology in the non-human great apes. Our results underline the importance of regressing data against a suitable body size variable to ascertain the relationship (geometric or allometric) and of choosing appropriate exponents by which to scale data. ancova models, while likely to be more robust than scaling for species comparisons when sample sizes are high, suffer from reduced power when sample sizes are low. Therefore, until sample sizes are radically increased it is preferable to include scaling analyses along with ancovas in data exploration. Overall, the results obtained from the different methods show little significant variation, whether in muscle belly mass, fascicle length or physiological cross-sectional area between the different species. This may reflect relatively close evolutionary relationships of the non-human great apes; a universal influence on morphology of generalised orthograde locomotor behaviours or, quite likely, both. PMID:21507000

The impact of hypnotic suggestibility in clinical care settings.

PubMed

Montgomery, Guy H; Schnur, Julie B; David, Daniel

2011-07-01

Hypnotic suggestibility has been described as a powerful predictor of outcomes associated with hypnotic interventions. However, there have been no systematic approaches to quantifying this effect across the literature. This meta-analysis evaluates the magnitude of the effect of hypnotic suggestibility on hypnotic outcomes in clinical settings. PsycINFO and PubMed were searched from their inception through July 2009. Thirty-four effects from 10 studies and 283 participants are reported. Results revealed a statistically significant overall effect size in the small to medium range (r = .24; 95% Confidence Interval = -0.28 to 0.75), indicating that greater hypnotic suggestibility led to greater effects of hypnosis interventions. Hypnotic suggestibility accounted for 6% of the variance in outcomes. Smaller sample size studies, use of the SHCS, and pediatric samples tended to result in larger effect sizes. The authors question the usefulness of assessing hypnotic suggestibility in clinical contexts.

The impact of hypnotic suggestibility in clinical care settings

PubMed Central

Montgomery, Guy H.; Schnur, Julie B.; David, Daniel

2013-01-01

Hypnotic suggestibility has been described as a powerful predictor of outcomes associated with hypnotic interventions. However, there have been no systematic approaches to quantifying this effect across the literature. The present meta-analysis evaluates the magnitude of the effect of hypnotic suggestibility on hypnotic outcomes in clinical settings. PsycINFO and PubMed were searched from their inception through July 2009. Thirty-four effects from ten studies and 283 participants are reported. Results revealed a statistically significant overall effect size in the small to medium range (r = 0.24; 95% Confidence Interval = −0.28 to 0.75), indicating that greater hypnotic suggestibility led to greater effects of hypnosis interventions. Hypnotic suggestibility accounted for 6% of the variance in outcomes. Smaller sample size studies, use of the SHCS, and pediatric samples tended to result in larger effect sizes. Results question the usefulness of assessing hypnotic suggestibility in clinical contexts. PMID:21644122

Adjusting for multiple prognostic factors in the analysis of randomised trials

PubMed Central

2013-01-01

Background When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size. PMID:23898993

Spatial heterogeneity in statistical power to detect changes in lake area in Alaskan National Wildlife Refuges

USGS Publications Warehouse

Nicol, Samuel; Roach, Jennifer K.; Griffith, Brad

2013-01-01

Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.

Simulation program for estimating statistical power of Cox's proportional hazards model assuming no specific distribution for the survival time.

PubMed

Akazawa, K; Nakamura, T; Moriguchi, S; Shimada, M; Nose, Y

1991-07-01

Small sample properties of the maximum partial likelihood estimates for Cox's proportional hazards model depend on the sample size, the true values of regression coefficients, covariate structure, censoring pattern and possibly baseline hazard functions. Therefore, it would be difficult to construct a formula or table to calculate the exact power of a statistical test for the treatment effect in any specific clinical trial. The simulation program, written in SAS/IML, described in this paper uses Monte-Carlo methods to provide estimates of the exact power for Cox's proportional hazards model. For illustrative purposes, the program was applied to real data obtained from a clinical trial performed in Japan. Since the program does not assume any specific function for the baseline hazard, it is, in principle, applicable to any censored survival data as long as they follow Cox's proportional hazards model.

Statistical power comparisons at 3T and 7T with a GO / NOGO task.

PubMed

Torrisi, Salvatore; Chen, Gang; Glen, Daniel; Bandettini, Peter A; Baker, Chris I; Reynolds, Richard; Yen-Ting Liu, Jeffrey; Leshin, Joseph; Balderston, Nicholas; Grillon, Christian; Ernst, Monique

2018-07-15

The field of cognitive neuroscience is weighing evidence about whether to move from standard field strength to ultra-high field (UHF). The present study contributes to the evidence by comparing a cognitive neuroscience paradigm at 3 Tesla (3T) and 7 Tesla (7T). The goal was to test and demonstrate the practical effects of field strength on a standard GO/NOGO task using accessible preprocessing and analysis tools. Two independent matched healthy samples (N = 31 each) were analyzed at 3T and 7T. Results show gains at 7T in statistical strength, the detection of smaller effects and group-level power. With an increased availability of UHF scanners, these gains may be exploited by cognitive neuroscientists and other neuroimaging researchers to develop more efficient or comprehensive experimental designs and, given the same sample size, achieve greater statistical power at 7T. Published by Elsevier Inc.

Experimental determination of the frequency and field dependence of Specific Loss Power in Magnetic Fluid Hyperthermia

NASA Astrophysics Data System (ADS)

Cobianchi, M.; Guerrini, A.; Avolio, M.; Innocenti, C.; Corti, M.; Arosio, P.; Orsini, F.; Sangregorio, C.; Lascialfari, A.

2017-12-01

Magnetic nanoparticles are promising systems for biomedical applications and in particular for Magnetic Fluid Hyperthermia, a therapy that utilizes the heat released by such systems to damage tumor cells. We present an experimental study of the physical properties that influences the capability of heat release, i.e. the Specific Loss Power, SLP, of three biocompatible ferrofluid samples having a magnetic core of maghemite with different diameter d = 10.2, 14.6 and 19.7 nm. The SLP was measured as a function of frequency f and intensity H of the applied alternating magnetic field, and it turned out to depend on the core diameter, as expected. The results allowed us to highlight experimentally that the physical mechanism responsible for the heating is size-dependent and to establish, at applied constant frequency, the phenomenological functional relationship SLP = c·Hx, with 2 ≤ x<3 for all samples. The x-value depends on sample size and field frequency, here chosen in the typical range of operating magnetic hyperthermia devices. For the smallest sample, the effective relaxation time τeff ≈ 19.5 ns obtained from SLP data is in agreement with the value estimated from magnetization data, thus confirming the validity of the Linear Response Theory model for this system at properly chosen field intensity and frequency.

STAR FORMATION LAWS: THE EFFECTS OF GAS CLOUD SAMPLING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Calzetti, D.; Liu, G.; Koda, J., E-mail: calzetti@astro.umass.edu

Recent observational results indicate that the functional shape of the spatially resolved star formation-molecular gas density relation depends on the spatial scale considered. These results may indicate a fundamental role of sampling effects on scales that are typically only a few times larger than those of the largest molecular clouds. To investigate the impact of this effect, we construct simple models for the distribution of molecular clouds in a typical star-forming spiral galaxy and, assuming a power-law relation between star formation rate (SFR) and cloud mass, explore a range of input parameters. We confirm that the slope and the scattermore » of the simulated SFR-molecular gas surface density relation depend on the size of the sub-galactic region considered, due to stochastic sampling of the molecular cloud mass function, and the effect is larger for steeper relations between SFR and molecular gas. There is a general trend for all slope values to tend to {approx}unity for region sizes larger than 1-2 kpc, irrespective of the input SFR-cloud relation. The region size of 1-2 kpc corresponds to the area where the cloud mass function becomes fully sampled. We quantify the effects of selection biases in data tracing the SFR, either as thresholds (i.e., clouds smaller than a given mass value do not form stars) or as backgrounds (e.g., diffuse emission unrelated to current star formation is counted toward the SFR). Apparently discordant observational results are brought into agreement via this simple model, and the comparison of our simulations with data for a few galaxies supports a steep (>1) power-law index between SFR and molecular gas.« less

Microwave Heating of Crystals with Gold Nanoparticles and Synovial Fluid under Synthetic Skin Patches

PubMed Central

2017-01-01

Gout is a disease with elusive treatment options. Reduction of the size of l-alanine crystals as a model crystal for gouty tophi with the use of a monomode solid-state microwave was examined as a possible therapeutic aid. The effect of microwave heating on l-alanine crystals in the presence of gold nanoparticles (Au NPs) in solution and synovial fluid (SF) in a plastic pouch through a synthetic skin patch was investigated. In this regard, three experimental paradigms were employed: Paradigm 1 includes the effect of variable microwave power (5–10 W) and variable heating time (5–60 s) and Au NPs in water (20 nm size, volume of 10 μL) in a plastic pouch (1 × 2 cm2 in size). Paradigm 2 includes the effect of a variable volume of 20 nm Au NPs in a variable volume of SF up to 100 μL in a plastic pouch at a constant microwave power (10 W) for 30 s. Paradigm 3 includes the effect of constant microwave power (10 W) and microwave heating time (30 s), constant volume of Au NPs (100 μL), and variable size of Au NPs (20–200 nm) placed in a plastic pouch through a synthetic skin patch. In these experiments, an average of 60–100% reduction in the size of an l-alanine crystal (initial size = 450 μm) without damage to the synthetic skin or increasing the temperature of the samples beyond the physiological range was reported. PMID:28983527

The Power of Neuroimaging Biomarkers for Screening Frontotemporal Dementia

PubMed Central

McMillan, Corey T.; Avants, Brian B.; Cook, Philip; Ungar, Lyle; Trojanowski, John Q.; Grossman, Murray

2014-01-01

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer’s disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N=93) with cerebrospinal fluid, gray matter (GM) MRI, and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes (mSS) required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% SENSITIVE; 89% SPECIFIC) and required a lower minimum sample size (N=26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach employing Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. PMID:24687814

Vocational Education and Training: A Review of World Bank Investment. World Bank Discussion Papers 51.

ERIC Educational Resources Information Center

Middleton, John; Demsky, Terry

A study of a representative sample of 121 World Bank-funded vocational education and training components suggests that the level of economic development and consequent size and dynamism of industrial employment powerfully influence the outcome of such education and training. Consequently, future investment strategies should differ among countries…

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…

The Effect of the Multivariate Box-Cox Transformation on the Power of MANOVA.

ERIC Educational Resources Information Center

Kirisci, Levent; Hsu, Tse-Chi

Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…

The application of nirvana to silvicultural studies

Treesearch

Chi-Leung So; Thomas Elder; Leslie Groom; John S. Kush; Jennifer Myszewski; Todd Shupe

2006-01-01

Previous results from this laboratory have shown that near infrared (NIR) spectroscopy, coupled with multivariate analysis, can be a powerful tool for the prediction of wood quality. While wood quality measurements are of utility, their determination can be both time and labor intensive, thus limiting their use where large sample sizes are concerned. This paper will...

Robustness of methods for blinded sample size re-estimation with overdispersed count data.

PubMed

Schneider, Simon; Schmidli, Heinz; Friede, Tim

2013-09-20

Counts of events are increasingly common as primary endpoints in randomized clinical trials. With between-patient heterogeneity leading to variances in excess of the mean (referred to as overdispersion), statistical models reflecting this heterogeneity by mixtures of Poisson distributions are frequently employed. Sample size calculation in the planning of such trials requires knowledge on the nuisance parameters, that is, the control (or overall) event rate and the overdispersion parameter. Usually, there is only little prior knowledge regarding these parameters in the design phase resulting in considerable uncertainty regarding the sample size. In this situation internal pilot studies have been found very useful and very recently several blinded procedures for sample size re-estimation have been proposed for overdispersed count data, one of which is based on an EM-algorithm. In this paper we investigate the EM-algorithm based procedure with respect to aspects of their implementation by studying the algorithm's dependence on the choice of convergence criterion and find that the procedure is sensitive to the choice of the stopping criterion in scenarios relevant to clinical practice. We also compare the EM-based procedure to other competing procedures regarding their operating characteristics such as sample size distribution and power. Furthermore, the robustness of these procedures to deviations from the model assumptions is explored. We find that some of the procedures are robust to at least moderate deviations. The results are illustrated using data from the US National Heart, Lung and Blood Institute sponsored Asymptomatic Cardiac Ischemia Pilot study. Copyright © 2013 John Wiley & Sons, Ltd.

Laser Surface Modification of H13 Die Steel using Different Laser Spot Sizes

NASA Astrophysics Data System (ADS)

Aqida, S. N.; Naher, S.; Brabazon, D.

2011-05-01

This paper presents a laser surface modification process of AISI H13 tool steel using three sizes of laser spot with an aim to achieve reduced grain size and surface roughness. A Rofin DC-015 diffusion-cooled CO2 slab laser was used to process AISI H13 tool steel samples. Samples of 10 mm diameter were sectioned to 100 mm length in order to process a predefined circumferential area. The parameters selected for examination were laser peak power, overlap percentage and pulse repetition frequency (PRF). Metallographic study and image analysis were done to measure the grain size and the modified surface roughness was measured using two-dimensional surface profilometer. From metallographic study, the smallest grain sizes measured by laser modified surface were between 0.51 μm and 2.54 μm. The minimum surface roughness, Ra, recorded was 3.0 μm. This surface roughness of the modified die steel is similar to the surface quality of cast products. The grain size correlation with hardness followed the findings correlate with Hall-Petch relationship. The potential found for increase in surface hardness represents an important method to sustain tooling life.

How conservative is Fisher's exact test? A quantitative evaluation of the two-sample comparative binomial trial.

PubMed

Crans, Gerald G; Shuster, Jonathan J

2008-08-15

The debate as to which statistical methodology is most appropriate for the analysis of the two-sample comparative binomial trial has persisted for decades. Practitioners who favor the conditional methods of Fisher, Fisher's exact test (FET), claim that only experimental outcomes containing the same amount of information should be considered when performing analyses. Hence, the total number of successes should be fixed at its observed level in hypothetical repetitions of the experiment. Using conditional methods in clinical settings can pose interpretation difficulties, since results are derived using conditional sample spaces rather than the set of all possible outcomes. Perhaps more importantly from a clinical trial design perspective, this test can be too conservative, resulting in greater resource requirements and more subjects exposed to an experimental treatment. The actual significance level attained by FET (the size of the test) has not been reported in the statistical literature. Berger (J. R. Statist. Soc. D (The Statistician) 2001; 50:79-85) proposed assessing the conservativeness of conditional methods using p-value confidence intervals. In this paper we develop a numerical algorithm that calculates the size of FET for sample sizes, n, up to 125 per group at the two-sided significance level, alpha = 0.05. Additionally, this numerical method is used to define new significance levels alpha(*) = alpha+epsilon, where epsilon is a small positive number, for each n, such that the size of the test is as close as possible to the pre-specified alpha (0.05 for the current work) without exceeding it. Lastly, a sample size and power calculation example are presented, which demonstrates the statistical advantages of implementing the adjustment to FET (using alpha(*) instead of alpha) in the two-sample comparative binomial trial. 2008 John Wiley & Sons, Ltd

Confidence crisis of results in biomechanics research.

PubMed

Knudson, Duane

2017-11-01

Many biomechanics studies have small sample sizes and incorrect statistical analyses, so reporting of inaccurate inferences and inflated magnitude of effects are common in the field. This review examines these issues in biomechanics research and summarises potential solutions from research in other fields to increase the confidence in the experimental effects reported in biomechanics. Authors, reviewers and editors of biomechanics research reports are encouraged to improve sample sizes and the resulting statistical power, improve reporting transparency, improve the rigour of statistical analyses used, and increase the acceptance of replication studies to improve the validity of inferences from data in biomechanics research. The application of sports biomechanics research results would also improve if a larger percentage of unbiased effects and their uncertainty were reported in the literature.

Biostatistical analysis of quantitative immunofluorescence microscopy images.

PubMed

Giles, C; Albrecht, M A; Lam, V; Takechi, R; Mamo, J C

2016-12-01

Semiquantitative immunofluorescence microscopy has become a key methodology in biomedical research. Typical statistical workflows are considered in the context of avoiding pseudo-replication and marginalising experimental error. However, immunofluorescence microscopy naturally generates hierarchically structured data that can be leveraged to improve statistical power and enrich biological interpretation. Herein, we describe a robust distribution fitting procedure and compare several statistical tests, outlining their potential advantages/disadvantages in the context of biological interpretation. Further, we describe tractable procedures for power analysis that incorporates the underlying distribution, sample size and number of images captured per sample. The procedures outlined have significant potential for increasing understanding of biological processes and decreasing both ethical and financial burden through experimental optimization. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Uncertainties in detecting decadal change in extractable soil elements in Northern Forests

NASA Astrophysics Data System (ADS)

Bartlett, O.; Bailey, S. W.; Ducey, M. J.

2016-12-01

Northern Forest ecosystems have been or are being impacted by land use change, forest harvesting, acid deposition, atmospheric CO2 enrichment, and climate change. Each of these has the potential to modify soil forming processes, and the resulting chemical stocks. Horizontal and vertical variations in concentrations complicate determination of temporal change. This study evaluates sample design, sample size, and differences among observers as sources of uncertainty when quantifying soil temporal change over regional scales. Forty permanent, northern hardwood, monitoring plots were established on the White Mountain National Forest in central New Hampshire and western Maine. Soil pits were characterized and sampled by genetic horizon at plot center in 2001 and resampled again in 2014 two-meters on contour from the original sampling location. Each soil horizon was characterized by depth, color, texture, structure, consistency, boundaries, coarse fragments, and roots from the forest floor to the upper C horizon, the relatively unaltered glacial till parent material. Laboratory analyses included pH in 0.01 M CaCl2 solution and extractable Ca, Mg, Na, K, Al, Mn, and P in 1 M NH4OAc solution buffered at pH 4.8. Significant elemental differences were identified by genetic horizon from paired t-tests (p ≤ 0.05) indicate temporal change across the study region. Power analysis, 0.9 power (α = 0.05), revealed sampling size was appropriate within this region to detect concentration change by genetic horizon using a stratified sample design based on topographic metrics. There were no significant differences between observers' descriptions of physical properties. As physical properties would not be expected to change over a decade, this suggests spatial variation in physical properties between the pairs of sampling pits did not detract from our ability to detect temporal change. These results suggest that resampling efforts within a site, repeated across a region, to quantify elemental change by carefully described genetic horizons is an appropriate method of detecting soil temporal change in this region. Sample size and design considerations from this project will have direct implications for future monitoring programs to characterize change in soil chemistry.

Analysis of Clinical Cohort Data Using Nested Case-control and Case-cohort Sampling Designs. A Powerful and Economical Tool.

PubMed

Ohneberg, K; Wolkewitz, M; Beyersmann, J; Palomar-Martinez, M; Olaechea-Astigarraga, P; Alvarez-Lerma, F; Schumacher, M

2015-01-01

Sampling from a large cohort in order to derive a subsample that would be sufficient for statistical analysis is a frequently used method for handling large data sets in epidemiological studies with limited resources for exposure measurement. For clinical studies however, when interest is in the influence of a potential risk factor, cohort studies are often the first choice with all individuals entering the analysis. Our aim is to close the gap between epidemiological and clinical studies with respect to design and power considerations. Schoenfeld's formula for the number of events required for a Cox' proportional hazards model is fundamental. Our objective is to compare the power of analyzing the full cohort and the power of a nested case-control and a case-cohort design. We compare formulas for power for sampling designs and cohort studies. In our data example we simultaneously apply a nested case-control design with a varying number of controls matched to each case, a case cohort design with varying subcohort size, a random subsample and a full cohort analysis. For each design we calculate the standard error for estimated regression coefficients and the mean number of distinct persons, for whom covariate information is required. The formula for the power of a nested case-control design and the power of a case-cohort design is directly connected to the power of a cohort study using the well known Schoenfeld formula. The loss in precision of parameter estimates is relatively small compared to the saving in resources. Nested case-control and case-cohort studies, but not random subsamples yield an attractive alternative for analyzing clinical studies in the situation of a low event rate. Power calculations can be conducted straightforwardly to quantify the loss of power compared to the savings in the num-ber of patients using a sampling design instead of analyzing the full cohort.

Image acquisition system using on sensor compressed sampling technique

NASA Astrophysics Data System (ADS)

Gupta, Pravir Singh; Choi, Gwan Seong

2018-01-01

Advances in CMOS technology have made high-resolution image sensors possible. These image sensors pose significant challenges in terms of the amount of raw data generated, energy efficiency, and frame rate. This paper presents a design methodology for an imaging system and a simplified image sensor pixel design to be used in the system so that the compressed sensing (CS) technique can be implemented easily at the sensor level. This results in significant energy savings as it not only cuts the raw data rate but also reduces transistor count per pixel; decreases pixel size; increases fill factor; simplifies analog-to-digital converter, JPEG encoder, and JPEG decoder design; decreases wiring; and reduces the decoder size by half. Thus, CS has the potential to increase the resolution of image sensors for a given technology and die size while significantly decreasing the power consumption and design complexity. We show that it has potential to reduce power consumption by about 23% to 65%.

Journal news

USGS Publications Warehouse

Conroy, M.J.; Samuel, M.D.; White, Joanne C.

1995-01-01

Statistical power (and conversely, Type II error) is often ignored by biologists. Power is important to consider in the design of studies, to ensure that sufficient resources are allocated to address a hypothesis under examination. Deter- mining appropriate sample size when designing experiments or calculating power for a statistical test requires an investigator to consider the importance of making incorrect conclusions about the experimental hypothesis and the biological importance of the alternative hypothesis (or the biological effect size researchers are attempting to measure). Poorly designed studies frequently provide results that are at best equivocal, and do little to advance science or assist in decision making. Completed studies that fail to reject Ho should consider power and the related probability of a Type II error in the interpretation of results, particularly when implicit or explicit acceptance of Ho is used to support a biological hypothesis or management decision. Investigators must consider the biological question they wish to answer (Tacha et al. 1982) and assess power on the basis of biologically significant differences (Taylor and Gerrodette 1993). Power calculations are somewhat subjective, because the author must specify either f or the minimum difference that is biologically important. Biologists may have different ideas about what values are appropriate. While determining biological significance is of central importance in power analysis, it is also an issue of importance in wildlife science. Procedures, references, and computer software to compute power are accessible; therefore, authors should consider power. We welcome comments or suggestions on this subject.

Study Design Rigor in Animal-Experimental Research Published in Anesthesia Journals.

PubMed

Hoerauf, Janine M; Moss, Angela F; Fernandez-Bustamante, Ana; Bartels, Karsten

2018-01-01

Lack of reproducibility of preclinical studies has been identified as an impediment for translation of basic mechanistic research into effective clinical therapies. Indeed, the National Institutes of Health has revised its grant application process to require more rigorous study design, including sample size calculations, blinding procedures, and randomization steps. We hypothesized that the reporting of such metrics of study design rigor has increased over time for animal-experimental research published in anesthesia journals. PubMed was searched for animal-experimental studies published in 2005, 2010, and 2015 in primarily English-language anesthesia journals. A total of 1466 publications were graded on the performance of sample size estimation, randomization, and blinding. Cochran-Armitage test was used to assess linear trends over time for the primary outcome of whether or not a metric was reported. Interrater agreement for each of the 3 metrics (power, randomization, and blinding) was assessed using the weighted κ coefficient in a 10% random sample of articles rerated by a second investigator blinded to the ratings of the first investigator. A total of 1466 manuscripts were analyzed. Reporting for all 3 metrics of experimental design rigor increased over time (2005 to 2010 to 2015): for power analysis, from 5% (27/516), to 12% (59/485), to 17% (77/465); for randomization, from 41% (213/516), to 50% (243/485), to 54% (253/465); and for blinding, from 26% (135/516), to 38% (186/485), to 47% (217/465). The weighted κ coefficients and 98.3% confidence interval indicate almost perfect agreement between the 2 raters beyond that which occurs by chance alone (power, 0.93 [0.85, 1.0], randomization, 0.91 [0.85, 0.98], and blinding, 0.90 [0.84, 0.96]). Our hypothesis that reported metrics of rigor in animal-experimental studies in anesthesia journals have increased during the past decade was confirmed. More consistent reporting, or explicit justification for absence, of sample size calculations, blinding techniques, and randomization procedures could better enable readers to evaluate potential sources of bias in animal-experimental research manuscripts. Future studies should assess whether such steps lead to improved translation of animal-experimental anesthesia research into successful clinical trials.

Effect of Microstructural Interfaces on the Mechanical Response of Crystalline Metallic Materials

NASA Astrophysics Data System (ADS)

Aitken, Zachary H.

Advances in nano-scale mechanical testing have brought about progress in the understanding of physical phenomena in materials and a measure of control in the fabrication of novel materials. In contrast to bulk materials that display size-invariant mechanical properties, sub-micron metallic samples show a critical dependence on sample size. The strength of nano-scale single crystalline metals is well-described by a power-law function, sigma ∝ D-n, where D is a critical sample size and n is a experimentally-fit positive exponent. This relationship is attributed to source-driven plasticity and demonstrates a strengthening as the decreasing sample size begins to limit the size and number of dislocation sources. A full understanding of this size-dependence is complicated by the presence of microstructural features such as interfaces that can compete with the dominant dislocation-based deformation mechanisms. In this thesis, the effects of microstructural features such as grain boundaries and anisotropic crystallinity on nano-scale metals are investigated through uniaxial compression testing. We find that nano-sized Cu covered by a hard coating displays a Bauschinger effect and the emergence of this behavior can be explained through a simple dislocation-based analytic model. Al nano-pillars containing a single vertically-oriented coincident site lattice grain boundary are found to show similar deformation to single-crystalline nano-pillars with slip traces passing through the grain boundary. With increasing tilt angle of the grain boundary from the pillar axis, we observe a transition from dislocation-dominated deformation to grain boundary sliding. Crystallites are observed to shear along the grain boundary and molecular dynamics simulations reveal a mechanism of atomic migration that accommodates boundary sliding. We conclude with an analysis of the effects of inherent crystal anisotropy and alloying on the mechanical behavior of the Mg alloy, AZ31. Through comparison to pure Mg, we show that the size effect dominates the strength of samples below 10 microm, that differences in the size effect between hexagonal slip systems is due to the inherent crystal anisotropy, suggesting that the fundamental mechanism of the size effect in these slip systems is the same.

A two-stage Monte Carlo approach to the expression of uncertainty with finite sample sizes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crowder, Stephen Vernon; Moyer, Robert D.

2005-05-01

Proposed supplement I to the GUM outlines a 'propagation of distributions' approach to deriving the distribution of a measurand for any non-linear function and for any set of random inputs. The supplement's proposed Monte Carlo approach assumes that the distributions of the random inputs are known exactly. This implies that the sample sizes are effectively infinite. In this case, the mean of the measurand can be determined precisely using a large number of Monte Carlo simulations. In practice, however, the distributions of the inputs will rarely be known exactly, but must be estimated using possibly small samples. If these approximatedmore » distributions are treated as exact, the uncertainty in estimating the mean is not properly taken into account. In this paper, we propose a two-stage Monte Carlo procedure that explicitly takes into account the finite sample sizes used to estimate parameters of the input distributions. We will illustrate the approach with a case study involving the efficiency of a thermistor mount power sensor. The performance of the proposed approach will be compared to the standard GUM approach for finite samples using simple non-linear measurement equations. We will investigate performance in terms of coverage probabilities of derived confidence intervals.« less

Precision, Reliability, and Effect Size of Slope Variance in Latent Growth Curve Models: Implications for Statistical Power Analysis

PubMed Central

Brandmaier, Andreas M.; von Oertzen, Timo; Ghisletta, Paolo; Lindenberger, Ulman; Hertzog, Christopher

2018-01-01

Latent Growth Curve Models (LGCM) have become a standard technique to model change over time. Prediction and explanation of inter-individual differences in change are major goals in lifespan research. The major determinants of statistical power to detect individual differences in change are the magnitude of true inter-individual differences in linear change (LGCM slope variance), design precision, alpha level, and sample size. Here, we show that design precision can be expressed as the inverse of effective error. Effective error is determined by instrument reliability and the temporal arrangement of measurement occasions. However, it also depends on another central LGCM component, the variance of the latent intercept and its covariance with the latent slope. We derive a new reliability index for LGCM slope variance—effective curve reliability (ECR)—by scaling slope variance against effective error. ECR is interpretable as a standardized effect size index. We demonstrate how effective error, ECR, and statistical power for a likelihood ratio test of zero slope variance formally relate to each other and how they function as indices of statistical power. We also provide a computational approach to derive ECR for arbitrary intercept-slope covariance. With practical use cases, we argue for the complementary utility of the proposed indices of a study's sensitivity to detect slope variance when making a priori longitudinal design decisions or communicating study designs. PMID:29755377

Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks.

PubMed

Vázquez-Baeza, Yoshiki; Hyde, Embriette R; Suchodolski, Jan S; Knight, Rob

2016-10-03

Inflammatory bowel disease (IBD) is an autoimmune condition that is difficult to diagnose, and animal models of this disease have questionable human relevance 1 . Here, we show that the dysbiosis network underlying IBD in dogs differs from that in humans, with some bacteria such as Fusobacterium switching roles between the two species (as Bacteroides fragilis switches roles between humans and mice) 2 . For example, a dysbiosis index trained on humans fails when applied to dogs, but a dog-specific dysbiosis index achieves high correlations with the overall dog microbial community diversity patterns. In addition, a random forest classifier trained on dog-specific samples achieves high discriminatory power, even when using stool samples rather than the mucosal biopsies required for high discriminatory power in humans 2 . These relationships were not detected in previously published dog IBD data sets due to their limited sample size and statistical power 3 . Taken together, these results reveal the need to train host-specific dysbiosis networks and point the way towards a generalized understanding of IBD across different mammalian models.

Oxygen partial pressure dependence of thermoelectric power factor in polycrystalline n-type SrTiO3: Consequences for long term stability in thermoelectric oxides

NASA Astrophysics Data System (ADS)

Sharma, Peter A.; Brown-Shaklee, Harlan J.; Ihlefeld, Jon F.

2017-04-01

The Seebeck coefficient and electrical conductivity have been measured as functions of oxygen partial pressure over the range of 10-22 to 10-1 atm at 1173 K for a 10% niobium-doped SrTiO3 ceramic with a grain size comparable to the oxygen diffusion length. Temperature-dependent measurements performed from 320 to 1275 K for as-prepared samples reveal metallic-like conduction and good thermoelectric properties. However, upon exposure to progressively increasing oxygen partial pressure, the thermoelectric power factor decreased over time scales of 24 h, culminating in a three order of magnitude reduction over the entire operating range. Identical measurements on single crystal samples show negligible changes in the power factor so that the instability of ceramic samples is primarily tied to the kinetics of grain boundary diffusion. This work provides a framework for understanding the stability of thermoelectric properties in oxides under different atmospheric conditions. The control of the oxygen atmosphere remains a significant challenge in oxide thermoelectrics.

Application of ultra-high energy hollow cathode helium-silver laser (224.3 nm) as Jc's, grain size surface's promoter for Ir-optimally doped-Mg0.94Ir0.06B2 superconductors

NASA Astrophysics Data System (ADS)

Elsabawy, Khaled M.; Fallatah, Ahmed M.; Alharthi, Salman S.

2018-07-01

For the first time high energy Helium-Silver laser which belongs to the category of metal-vapor lasers applied as microstructure promoter for optimally Ir-doped-MgB2sample. The Ir-optimally doped-Mg0.94Ir 0.06B2 superconducting sample was selected from previously published article for one of authors themselves. The samples were irradiated by a three different doses 1, 2 and 3 h from an ultrahigh energy He-Ag-Laser with average power of 103 W/cm2 at distance of 3 cm. Superconducting measurements and micro-structural features were investigated as function of He-Ag Laser irradiation doses. Results indicated that irradiations via an ultrahigh energy He-Ag-Laser promoted grains to lower sizes and consequently measured Jc's values enhanced and increased. Furthermore Tc-offsets for all irradiated samples are better than non-irradiated Mg0.94Ir 0.06B2.

Process dependent thermoelectric properties of EDTA assisted bismuth telluride

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kulsi, Chiranjit; Banerjee, Dipali, E-mail: dipalibanerjeebesu@gmail.com; Kargupta, Kajari

2016-04-13

Comparison between the structure and thermoelectric properties of EDTA (Ethylene-diamine-tetra-acetic acid) assisted bismuth telluride prepared by electrochemical deposition and hydrothermal route is reported in the present work. The prepared samples have been structurally characterized by high resolution X-ray diffraction spectra (HRXRD), field emission scanning electron microscopy (FESEM) and high resolution transmission electron microscopic images (HRTEM). Crystallite size and strain have been determined from Williamson-Hall plot of XRD which is in conformity with TEM images. Measurement of transport properties show sample in the pellet form (S{sub 1}) prepared via hydrothermal route has higher value of thermoelectric power (S) than the electrodepositedmore » film (S{sub 2}). But due to a substantial increase in the electrical conductivity (σ) of the film (S{sub 2}) over the pellet (S{sub 1}), the power factor and the figure of merit is higher for sample S{sub 2} than the sample S{sub 1} at room temperature.« less

A comparative review of methods for comparing means using partially paired data.

PubMed

Guo, Beibei; Yuan, Ying

2017-06-01

In medical experiments with the objective of testing the equality of two means, data are often partially paired by design or because of missing data. The partially paired data represent a combination of paired and unpaired observations. In this article, we review and compare nine methods for analyzing partially paired data, including the two-sample t-test, paired t-test, corrected z-test, weighted t-test, pooled t-test, optimal pooled t-test, multiple imputation method, mixed model approach, and the test based on a modified maximum likelihood estimate. We compare the performance of these methods through extensive simulation studies that cover a wide range of scenarios with different effect sizes, sample sizes, and correlations between the paired variables, as well as true underlying distributions. The simulation results suggest that when the sample size is moderate, the test based on the modified maximum likelihood estimator is generally superior to the other approaches when the data is normally distributed and the optimal pooled t-test performs the best when the data is not normally distributed, with well-controlled type I error rates and high statistical power; when the sample size is small, the optimal pooled t-test is to be recommended when both variables have missing data and the paired t-test is to be recommended when only one variable has missing data.

Noninferiority trial designs for odds ratios and risk differences.

PubMed

Hilton, Joan F

2010-04-30

This study presents constrained maximum likelihood derivations of the design parameters of noninferiority trials for binary outcomes with the margin defined on the odds ratio (ψ) or risk-difference (δ) scale. The derivations show that, for trials in which the group-specific response rates are equal under the point-alternative hypothesis, the common response rate, π(N), is a fixed design parameter whose value lies between the control and experimental rates hypothesized at the point-null, {π(C), π(E)}. We show that setting π(N) equal to the value of π(C) that holds under H(0) underestimates the overall sample size requirement. Given {π(C), ψ} or {π(C), δ} and the type I and II error rates, or algorithm finds clinically meaningful design values of π(N), and the corresponding minimum asymptotic sample size, N=n(E)+n(C), and optimal allocation ratio, γ=n(E)/n(C). We find that optimal allocations are increasingly imbalanced as ψ increases, with γ(ψ)<1 and γ(δ)≈1/γ(ψ), and that ranges of allocation ratios map to the minimum sample size. The latter characteristic allows trialists to consider trade-offs between optimal allocation at a smaller N and a preferred allocation at a larger N. For designs with relatively large margins (e.g. ψ>2.5), trial results that are presented on both scales will differ in power, with more power lost if the study is designed on the risk-difference scale and reported on the odds ratio scale than vice versa. 2010 John Wiley & Sons, Ltd.

On the impact of relatedness on SNP association analysis.

PubMed

Gross, Arnd; Tönjes, Anke; Scholz, Markus

2017-12-06

When testing for SNP (single nucleotide polymorphism) associations in related individuals, observations are not independent. Simple linear regression assuming independent normally distributed residuals results in an increased type I error and the power of the test is also affected in a more complicate manner. Inflation of type I error is often successfully corrected by genomic control. However, this reduces the power of the test when relatedness is of concern. In the present paper, we derive explicit formulae to investigate how heritability and strength of relatedness contribute to variance inflation of the effect estimate of the linear model. Further, we study the consequences of variance inflation on hypothesis testing and compare the results with those of genomic control correction. We apply the developed theory to the publicly available HapMap trio data (N=129), the Sorbs (a self-contained population with N=977 characterised by a cryptic relatedness structure) and synthetic family studies with different sample sizes (ranging from N=129 to N=999) and different degrees of relatedness. We derive explicit and easily to apply approximation formulae to estimate the impact of relatedness on the variance of the effect estimate of the linear regression model. Variance inflation increases with increasing heritability. Relatedness structure also impacts the degree of variance inflation as shown for example family structures. Variance inflation is smallest for HapMap trios, followed by a synthetic family study corresponding to the trio data but with larger sample size than HapMap. Next strongest inflation is observed for the Sorbs, and finally, for a synthetic family study with a more extreme relatedness structure but with similar sample size as the Sorbs. Type I error increases rapidly with increasing inflation. However, for smaller significance levels, power increases with increasing inflation while the opposite holds for larger significance levels. When genomic control is applied, type I error is preserved while power decreases rapidly with increasing variance inflation. Stronger relatedness as well as higher heritability result in increased variance of the effect estimate of simple linear regression analysis. While type I error rates are generally inflated, the behaviour of power is more complex since power can be increased or reduced in dependence on relatedness and the heritability of the phenotype. Genomic control cannot be recommended to deal with inflation due to relatedness. Although it preserves type I error, the loss in power can be considerable. We provide a simple formula for estimating variance inflation given the relatedness structure and the heritability of a trait of interest. As a rule of thumb, variance inflation below 1.05 does not require correction and simple linear regression analysis is still appropriate.

Accounting for treatment by center interaction in sample size determinations and the use of surrogate outcomes in the pessary for the prevention of preterm birth trial: a simulation study.

PubMed

Willan, Andrew R

2016-07-05

The Pessary for the Prevention of Preterm Birth Study (PS3) is an international, multicenter, randomized clinical trial designed to examine the effectiveness of the Arabin pessary in preventing preterm birth in pregnant women with a short cervix. During the design of the study two methodological issues regarding power and sample size were raised. Since treatment in the Standard Arm will vary between centers, it is anticipated that so too will the probability of preterm birth in that arm. This will likely result in a treatment by center interaction, and the issue of how this will affect the sample size requirements was raised. The sample size requirements to examine the effect of the pessary on the baby's clinical outcome was prohibitively high, so the second issue is how best to examine the effect on clinical outcome. The approaches taken to address these issues are presented. Simulation and sensitivity analysis were used to address the sample size issue. The probability of preterm birth in the Standard Arm was assumed to vary between centers following a Beta distribution with a mean of 0.3 and a coefficient of variation of 0.3. To address the second issue a Bayesian decision model is proposed that combines the information regarding the between-treatment difference in the probability of preterm birth from PS3 with the data from the Multiple Courses of Antenatal Corticosteroids for Preterm Birth Study that relate preterm birth and perinatal mortality/morbidity. The approach provides a between-treatment comparison with respect to the probability of a bad clinical outcome. The performance of the approach was assessed using simulation and sensitivity analysis. Accounting for a possible treatment by center interaction increased the sample size from 540 to 700 patients per arm for the base case. The sample size requirements increase with the coefficient of variation and decrease with the number of centers. Under the same assumptions used for determining the sample size requirements, the simulated mean probability that pessary reduces the risk of perinatal mortality/morbidity is 0.98. The simulated mean decreased with coefficient of variation and increased with the number of clinical sites. Employing simulation and sensitivity analysis is a useful approach for determining sample size requirements while accounting for the additional uncertainty due to a treatment by center interaction. Using a surrogate outcome in conjunction with a Bayesian decision model is an efficient way to compare important clinical outcomes in a randomized clinical trial in situations where the direct approach requires a prohibitively high sample size.

Increasing point-count duration increases standard error

USGS Publications Warehouse

Smith, W.P.; Twedt, D.J.; Hamel, P.B.; Ford, R.P.; Wiedenfeld, D.A.; Cooper, R.J.

1998-01-01

We examined data from point counts of varying duration in bottomland forests of west Tennessee and the Mississippi Alluvial Valley to determine if counting interval influenced sampling efficiency. Estimates of standard error increased as point count duration increased both for cumulative number of individuals and species in both locations. Although point counts appear to yield data with standard errors proportional to means, a square root transformation of the data may stabilize the variance. Using long (>10 min) point counts may reduce sample size and increase sampling error, both of which diminish statistical power and thereby the ability to detect meaningful changes in avian populations.

Differential expression analysis for RNAseq using Poisson mixed models.

PubMed

Sun, Shiquan; Hood, Michelle; Scott, Laura; Peng, Qinke; Mukherjee, Sayan; Tung, Jenny; Zhou, Xiang

2017-06-20

Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Development of a Multiple-Stage Differential Mobility Analyzer (MDMA)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Da-Ren; Cheng, Mengdawn

2007-01-01

A new DMA column has been designed with the capability of simultaneously extracting monodisperse particles of different sizes in multiple stages. We call this design a multistage DMA, or MDMA. A prototype MDMA has been constructed and experimentally evaluated in this study. The new column enables the fast measurement of particles in a wide size range, while preserving the powerful particle classification function of a DMA. The prototype MDMA has three sampling stages, capable of classifying monodisperse particles of three different sizes simultaneously. The scanning voltage operation of a DMA can be applied to this new column. Each stage ofmore » MDMA column covers a fraction of the entire particle size range to be measured. The covered size fractions of two adjacent stages of the MDMA are designed somewhat overlapped. The arrangement leads to the reduction of scanning voltage range and thus the cycling time of the measurement. The modular sampling stage design of the MDMA allows the flexible configuration of desired particle classification lengths and variable number of stages in the MDMA. The design of our MDMA also permits operation at high sheath flow, enabling high-resolution particle size measurement and/or reduction of the lower sizing limit. Using the tandem DMA technique, the performance of the MDMA, i.e., sizing accuracy, resolution, and transmission efficiency, was evaluated at different ratios of aerosol and sheath flowrates. Two aerosol sampling schemes were investigated. One was to extract aerosol flows at an evenly partitioned flowrate at each stage, and the other was to extract aerosol at a rate the same as the polydisperse aerosol flowrate at each stage. We detail the prototype design of the MDMA and the evaluation result on the transfer functions of the MDMA at different particle sizes and operational conditions.« less

Bayesian Power Prior Analysis and Its Application to Operational Risk and Rasch Model

ERIC Educational Resources Information Center

Zhang, Honglian

2010-01-01

When sample size is small, informative priors can be valuable in increasing the precision of estimates. Pooling historical data and current data with equal weights under the assumption that both of them are from the same population may be misleading when heterogeneity exists between historical data and current data. This is particularly true when…

77 FR 47590 - Notice of Request for a Revision to and Extension of Approval of an Information Collection...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-09

... requirements or power calculations that justify the proposed sample size, the expected response rate, methods...] Notice of Request for a Revision to and Extension of Approval of an Information Collection; Qualitative... associated with qualitative customer and stakeholder feedback on service delivery by the Animal and Plant...

Optimal Sample Size Determinations for the Heteroscedastic Two One-Sided Tests of Mean Equivalence: Design Schemes and Software Implementations

ERIC Educational Resources Information Center

Jan, Show-Li; Shieh, Gwowen

2017-01-01

Equivalence assessment is becoming an increasingly important topic in many application areas including behavioral and social sciences research. Although there exist more powerful tests, the two one-sided tests (TOST) procedure is a technically transparent and widely accepted method for establishing statistical equivalence. Alternatively, a direct…

Feedback Augmented Sub-Ranging (FASR) Quantizer

NASA Technical Reports Server (NTRS)

Guilligan, Gerard

2012-01-01

This innovation is intended to reduce the size, power, and complexity of pipeline analog-to-digital converters (ADCs) that require high resolution and speed along with low power. Digitizers are important components in any application where analog signals (such as light, sound, temperature, etc.) need to be digitally processed. The innovation implements amplification of a sampled residual voltage in a switched capacitor amplifier stage that does not depend on charge redistribution. The result is less sensitive to capacitor mismatches that cause gain errors, which are the main limitation of such amplifiers in pipeline ADCs. The residual errors due to mismatch are reduced by at least a factor of 16, which is equivalent to at least 4 bits of improvement. The settling time is also faster because of a higher feedback factor. In traditional switched capacitor residue amplifiers, closed-loop amplification of a sampled and held residue signal is achieved by redistributing sampled charge onto a feedback capacitor around a high-gain transconductance amplifier. The residual charge that was sampled during the acquisition or sampling phase is stored on two or more capacitors, often equal in value or integral multiples of each other. During the hold or amplification phase, all of the charge is redistributed onto one capacitor in the feedback loop of the amplifier to produce an amplified voltage. The key error source is the non-ideal ratios of feedback and input capacitors caused by manufacturing tolerances, called mismatches. The mismatches cause non-ideal closed-loop gain, leading to higher differential non-linearity. Traditional solutions to the mismatch errors are to use larger capacitor values (than dictated by thermal noise requirements) and/or complex calibration schemes, both of which increase the die size and power dissipation. The key features of this innovation are (1) the elimination of the need for charge redistribution to achieve an accurate closed-loop gain of two, (2) a higher feedback factor in the amplifier stage giving a higher closed-loop bandwidth compared to the prior art, and (3) reduced requirement for calibration. The accuracy of the new amplifier is mainly limited by the sampling networks parasitic capacitances, which should be minimized in relation to the sampling capacitors.

Coulomb Mechanics And Landscape Geometry Explain Landslide Size Distribution

NASA Astrophysics Data System (ADS)

Jeandet, L.; Steer, P.; Lague, D.; Davy, P.

2017-12-01

It is generally observed that the dimensions of large bedrock landslides follow power-law scaling relationships. In particular, the non-cumulative frequency distribution (PDF) of bedrock landslide area is well characterized by a negative power-law above a critical size, with an exponent 2.4. However, the respective role of bedrock mechanical properties, landscape shape and triggering mechanisms on the scaling properties of landslide dimensions are still poorly understood. Yet, unravelling the factors that control this distribution is required to better estimate the total volume of landslides triggered by large earthquakes or storms. To tackle this issue, we develop a simple probabilistic 1D approach to compute the PDF of rupture depths in a given landscape. The model is applied to randomly sampled points along hillslopes of studied digital elevation models. At each point location, the model determines the range of depth and angle leading to unstable rupture planes, by applying a simple Mohr-Coulomb rupture criterion only to the rupture planes that intersect downhill surface topography. This model therefore accounts for both rock mechanical properties, friction and cohesion, and landscape shape. We show that this model leads to realistic landslide depth distribution, with a power-law arising when the number of samples is high enough. The modeled PDF of landslide size obtained for several landscapes match the ones from earthquakes-driven landslides catalogues for the same landscape. In turn, this allows us to invert landslide effective mechanical parameters, friction and cohesion, associated to those specific events, including Chi-Chi, Wenchuan, Niigata and Gorkha earthquakes. The cohesion and friction ranges (25-35 degrees and 5-20 kPa) are in good agreement with previously inverted values. Our results demonstrate that reduced complexity mechanics is efficient to model the distribution of unstable depths, and show the role of landscape variability in landslide size distribution.

Sample size, power calculations, and their implications for the cost of thorough studies of drug induced QT interval prolongation.

PubMed

Malik, Marek; Hnatkova, Katerina; Batchvarov, Velislav; Gang, Yi; Smetana, Peter; Camm, A John

2004-12-01

Regulatory authorities require new drugs to be investigated using a so-called "thorough QT/QTc study" to identify compounds with a potential of influencing cardiac repolarization in man. Presently drafted regulatory consensus requires these studies to be powered for the statistical detection of QTc interval changes as small as 5 ms. Since this translates into a noticeable drug development burden, strategies need to be identified allowing the size and thus the cost of thorough QT/QTc studies to be minimized. This study investigated the influence of QT and RR interval data quality and the precision of heart rate correction on the sample sizes of thorough QT/QTc studies. In 57 healthy subjects (26 women, age range 19-42 years), a total of 4,195 drug-free digital electrocardiograms (ECG) were obtained (65-84 ECGs per subject). All ECG parameters were measured manually using the most accurate approach with reconciliation of measurement differences between different cardiologists and aligning the measurements of corresponding ECG patterns. From the data derived in this measurement process, seven different levels of QT/RR data quality were obtained, ranging from the simplest approach of measuring 3 beats in one ECG lead to the most exact approach. Each of these QT/RR data-sets was processed with eight different heart rate corrections ranging from Bazett and Fridericia corrections to the individual QT/RR regression modelling with optimization of QT/RR curvature. For each combination of data quality and heart rate correction, standard deviation of individual mean QTc values and mean of individual standard deviations of QTc values were calculated and used to derive the size of thorough QT/QTc studies with an 80% power to detect 5 ms QTc changes at the significance level of 0.05. Irrespective of data quality and heart rate corrections, the necessary sample sizes of studies based on between-subject comparisons (e.g., parallel studies) are very substantial requiring >140 subjects per group. However, the required study size may be substantially reduced in investigations based on within-subject comparisons (e.g., crossover studies or studies of several parallel groups each crossing over an active treatment with placebo). While simple measurement approaches with ad-hoc heart rate correction still lead to requirements of >150 subjects, the combination of best data quality with most accurate individualized heart rate correction decreases the variability of QTc measurements in each individual very substantially. In the data of this study, the average of standard deviations of QTc values calculated separately in each individual was only 5.2 ms. Such a variability in QTc data translates to only 18 subjects per study group (e.g., the size of a complete one-group crossover study) to detect 5 ms QTc change with an 80% power. Cost calculations show that by involving the most stringent ECG handling and measurement, the cost of a thorough QT/QTc study may be reduced to approximately 25%-30% of the cost imposed by the simple ECG reading (e.g., three complexes in one lead only).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Terai, Tsuyoshi; Takahashi, Jun; Itoh, Yoichi, E-mail: tsuyoshi.terai@nao.ac.jp

Main-belt asteroids have been continuously colliding with one another since they were formed. Their size distribution is primarily determined by the size dependence of asteroid strength against catastrophic impacts. The strength scaling law as a function of body size could depend on collision velocity, but the relationship remains unknown, especially under hypervelocity collisions comparable to 10 km s{sup –1}. We present a wide-field imaging survey at an ecliptic latitude of about 25° for investigating the size distribution of small main-belt asteroids that have highly inclined orbits. The analysis technique allowing for efficient asteroid detections and high-accuracy photometric measurements provides sufficientmore » sample data to estimate the size distribution of sub-kilometer asteroids with inclinations larger than 14°. The best-fit power-law slopes of the cumulative size distribution are 1.25 ± 0.03 in the diameter range of 0.6-1.0 km and 1.84 ± 0.27 in 1.0-3.0 km. We provide a simple size distribution model that takes into consideration the oscillations of the power-law slope due to the transition from the gravity-scaled regime to the strength-scaled regime. We find that the high-inclination population has a shallow slope of the primary components of the size distribution compared to the low-inclination populations. The asteroid population exposed to hypervelocity impacts undergoes collisional processes where large bodies have a higher disruptive strength and longer lifespan relative to tiny bodies than the ecliptic asteroids.« less

Novel hybrid cryo-radial method: an emerging alternative to CT-guided biopsy in suspected lung cancer. A prospective case series and description of technique.

PubMed

Herath, Samantha; Yap, Elaine

2018-02-01

In diagnosing peripheral pulmonary lesions (PPL), radial endobronchial ultrasound (R-EBUS) is emerging as a safer method in comparison to CT-guided biopsy. Despite the better safety profile, the yield of R-EBUS remains lower (73%) than CT-guided biopsy (90%) due to the smaller size of samples. We adopted a hybrid method by adding cryobiopsy via the R-EBUS Guide Sheath (GS) to produce larger, non-crushed samples to improve diagnostic capability and enhance molecular testing. We report six prospective patients who underwent this procedure in our institution. R-EBUS samples were obtained via conventional sampling methods (needle aspiration, forceps biopsy, and cytology brush), followed by a cryobiopsy. An endobronchial blocker was placed near the planned area of biopsy in advance and inflated post-biopsy to minimize the risk of bleeding in all patients. A chest X-ray was performed 1 h post-procedure. All the PPLs were visualized with R-EBUS. The mean diameter of cryobiopsy samples was twice the size of forceps biopsy samples. In four patients, cryobiopsy samples were superior in size and the number of malignant cells per high power filed and was the preferred sample selected for mutation analysis and molecular testing. There was no pneumothorax or significant bleeding to report. Cryobiopsy samples were consistently larger and were the preferred samples for molecular testing, with an increase in the diagnostic yield and reduction in the need for repeat procedures, without hindering the marked safety profile of R-EBUS. Using an endobronchial blocker improves the safety of this procedure.

Radiofrequency energy deposition and radiofrequency power requirements in parallel transmission with increasing distance from the coil to the sample.

PubMed

Deniz, Cem M; Vaidya, Manushka V; Sodickson, Daniel K; Lattanzi, Riccardo

2016-01-01

We investigated global specific absorption rate (SAR) and radiofrequency (RF) power requirements in parallel transmission as the distance between the transmit coils and the sample was increased. We calculated ultimate intrinsic SAR (UISAR), which depends on object geometry and electrical properties but not on coil design, and we used it as the reference to compare the performance of various transmit arrays. We investigated the case of fixing coil size and increasing the number of coils while moving the array away from the sample, as well as the case of fixing coil number and scaling coil dimensions. We also investigated RF power requirements as a function of lift-off, and tracked local SAR distributions associated with global SAR optima. In all cases, the target excitation profile was achieved and global SAR (as well as associated maximum local SAR) decreased with lift-off, approaching UISAR, which was constant for all lift-offs. We observed a lift-off value that optimizes the balance between global SAR and power losses in coil conductors. We showed that, using parallel transmission, global SAR can decrease at ultra high fields for finite arrays with a sufficient number of transmit elements. For parallel transmission, the distance between coils and object can be optimized to reduce SAR and minimize RF power requirements associated with homogeneous excitation. © 2015 Wiley Periodicals, Inc.

Prospective power calculations for the Four Lab study of a multigenerational reproductive/developmental toxicity rodent bioassay using a complex mixture of disinfection by-products in the low-response region.

PubMed

Dingus, Cheryl A; Teuschler, Linda K; Rice, Glenn E; Simmons, Jane Ellen; Narotsky, Michael G

2011-10-01

In complex mixture toxicology, there is growing emphasis on testing environmentally representative doses that improve the relevance of results for health risk assessment, but are typically much lower than those used in traditional toxicology studies. Traditional experimental designs with typical sample sizes may have insufficient statistical power to detect effects caused by environmentally relevant doses. Proper study design, with adequate statistical power, is critical to ensuring that experimental results are useful for environmental health risk assessment. Studies with environmentally realistic complex mixtures have practical constraints on sample concentration factor and sample volume as well as the number of animals that can be accommodated. This article describes methodology for calculation of statistical power for non-independent observations for a multigenerational rodent reproductive/developmental bioassay. The use of the methodology is illustrated using the U.S. EPA's Four Lab study in which rodents were exposed to chlorinated water concentrates containing complex mixtures of drinking water disinfection by-products. Possible experimental designs included two single-block designs and a two-block design. Considering the possible study designs and constraints, a design of two blocks of 100 females with a 40:60 ratio of control:treated animals and a significance level of 0.05 yielded maximum prospective power (~90%) to detect pup weight decreases, while providing the most power to detect increased prenatal loss.

Prospective Power Calculations for the Four Lab Study of A Multigenerational Reproductive/Developmental Toxicity Rodent Bioassay Using A Complex Mixture of Disinfection By-Products in the Low-Response Region

PubMed Central

Dingus, Cheryl A.; Teuschler, Linda K.; Rice, Glenn E.; Simmons, Jane Ellen; Narotsky, Michael G.

2011-01-01

In complex mixture toxicology, there is growing emphasis on testing environmentally representative doses that improve the relevance of results for health risk assessment, but are typically much lower than those used in traditional toxicology studies. Traditional experimental designs with typical sample sizes may have insufficient statistical power to detect effects caused by environmentally relevant doses. Proper study design, with adequate statistical power, is critical to ensuring that experimental results are useful for environmental health risk assessment. Studies with environmentally realistic complex mixtures have practical constraints on sample concentration factor and sample volume as well as the number of animals that can be accommodated. This article describes methodology for calculation of statistical power for non-independent observations for a multigenerational rodent reproductive/developmental bioassay. The use of the methodology is illustrated using the U.S. EPA’s Four Lab study in which rodents were exposed to chlorinated water concentrates containing complex mixtures of drinking water disinfection by-products. Possible experimental designs included two single-block designs and a two-block design. Considering the possible study designs and constraints, a design of two blocks of 100 females with a 40:60 ratio of control:treated animals and a significance level of 0.05 yielded maximum prospective power (~90%) to detect pup weight decreases, while providing the most power to detect increased prenatal loss. PMID:22073030

Increase of porosity by combining semi-carbonization and KOH activation of formaldehyde resins to prepare high surface area carbons for supercapacitor applications

NASA Astrophysics Data System (ADS)

Heimböckel, Ruben; Kraas, Sebastian; Hoffmann, Frank; Fröba, Michael

2018-01-01

A series of porous carbon samples were prepared by combining a semi-carbonization process of acidic polymerized phenol-formaldehyde resins and a following chemical activation with KOH used in different ratios to increase specific surface area, micropore content and pore sizes of the carbons which is favourable for supercapacitor applications. Samples were characterized by nitrogen physisorption, powder X-ray diffraction, Raman spectroscopy and scanning electron microscopy. The results show that the amount of KOH, combined with the semi-carbonization step had a remarkable effect on the specific surface area (up to SBET: 3595 m2 g-1 and SDFT: 2551 m2 g-1), pore volume (0.60-2.62 cm3 g-1) and pore sizes (up to 3.5 nm). The carbons were tested as electrode materials for electrochemical double layer capacitors (EDLC) in a two electrode setup with tetraethylammonium tetrafluoroborate in acetonitrile as electrolyte. The prepared carbon material with the largest surface area, pore volume and pore sizes exhibits a high specific capacitance of 145.1 F g-1 at a current density of 1 A g-1. With a high specific energy of 31 W h kg-1 at a power density of 33028 W kg-1 and a short time relaxation constant of 0.29 s, the carbon showed high power capability as an EDLC electrode material.

Scanning in situ Spectroscopy platform for imaging surgical breast tissue specimens

PubMed Central

Krishnaswamy, Venkataramanan; Laughney, Ashley M.; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.

2013-01-01

A non-contact localized spectroscopic imaging platform has been developed and optimized to scan 1x1cm2 square regions of surgically resected breast tissue specimens with ~150-micron resolution. A color corrected, image-space telecentric scanning design maintained a consistent sampling geometry and uniform spot size across the entire imaging field. Theoretical modeling in ZEMAX allowed estimation of the spot size, which is equal at both the center and extreme positions of the field with ~5% variation across the designed waveband, indicating excellent color correction. The spot sizes at the center and an extreme field position were also measured experimentally using the standard knife-edge technique and were found to be within ~8% of the theoretical predictions. Highly localized sampling offered inherent insensitivity to variations in background absorption allowing direct imaging of local scattering parameters, which was validated using a matrix of varying concentrations of Intralipid and blood in phantoms. Four representative, pathologically distinct lumpectomy tissue specimens were imaged, capturing natural variations in tissue scattering response within a given pathology. Variations as high as 60% were observed in the average reflectance and relative scattering power images, which must be taken into account for robust classification performance. Despite this variation, the preliminary data indicates discernible scatter power contrast between the benign vs malignant groups, but reliable discrimination of pathologies within these groups would require investigation into additional contrast mechanisms. PMID:23389199

Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation.

PubMed

Darda, Kohinoor M; Butler, Emily E; Ramsey, Richard

2018-06-01

Humans show an involuntary tendency to copy other people's actions. Although automatic imitation builds rapport and affiliation between individuals, we do not copy actions indiscriminately. Instead, copying behaviors are guided by a selection mechanism, which inhibits some actions and prioritizes others. To date, the neural underpinnings of the inhibition of automatic imitation and differences between the sexes in imitation control are not well understood. Previous studies involved small sample sizes and low statistical power, which produced mixed findings regarding the involvement of domain-general and domain-specific neural architectures. Here, we used data from Experiment 1 ( N = 28) to perform a power analysis to determine the sample size required for Experiment 2 ( N = 50; 80% power). Using independent functional localizers and an analysis pipeline that bolsters sensitivity, during imitation control we show clear engagement of the multiple-demand network (domain-general), but no sensitivity in the theory-of-mind network (domain-specific). Weaker effects were observed with regard to sex differences, suggesting that there are more similarities than differences between the sexes in terms of the neural systems engaged during imitation control. In summary, neurocognitive models of imitation require revision to reflect that the inhibition of imitation relies to a greater extent on a domain-general selection system rather than a domain-specific system that supports social cognition.

Random-effects linear modeling and sample size tables for two special crossover designs of average bioequivalence studies: the four-period, two-sequence, two-formulation and six-period, three-sequence, three-formulation designs.

PubMed

Diaz, Francisco J; Berg, Michel J; Krebill, Ron; Welty, Timothy; Gidal, Barry E; Alloway, Rita; Privitera, Michael

2013-12-01

Due to concern and debate in the epilepsy medical community and to the current interest of the US Food and Drug Administration (FDA) in revising approaches to the approval of generic drugs, the FDA is currently supporting ongoing bioequivalence studies of antiepileptic drugs, the EQUIGEN studies. During the design of these crossover studies, the researchers could not find commercial or non-commercial statistical software that quickly allowed computation of sample sizes for their designs, particularly software implementing the FDA requirement of using random-effects linear models for the analyses of bioequivalence studies. This article presents tables for sample-size evaluations of average bioequivalence studies based on the two crossover designs used in the EQUIGEN studies: the four-period, two-sequence, two-formulation design, and the six-period, three-sequence, three-formulation design. Sample-size computations assume that random-effects linear models are used in bioequivalence analyses with crossover designs. Random-effects linear models have been traditionally viewed by many pharmacologists and clinical researchers as just mathematical devices to analyze repeated-measures data. In contrast, a modern view of these models attributes an important mathematical role in theoretical formulations in personalized medicine to them, because these models not only have parameters that represent average patients, but also have parameters that represent individual patients. Moreover, the notation and language of random-effects linear models have evolved over the years. Thus, another goal of this article is to provide a presentation of the statistical modeling of data from bioequivalence studies that highlights the modern view of these models, with special emphasis on power analyses and sample-size computations.

Low-power laser use in the treatment of alopecia and crural ulcers

NASA Astrophysics Data System (ADS)

Ciuchita, Tavi; Usurelu, Mircea; Antipa, Ciprian; Vlaiculescu, Mihaela; Ionescu, Elena

1998-07-01

The authors tried to verify the efficacy of Low Power Laser (LPL) in scalp alopecia and crural ulcers of different causes. Laser used was (red diode, continuous emission, 8 mW power, wave length 670 nm spot size about 5 mm diameter on some points 1 - 2 minutes per point. We also use as control classical therapy. Before, during and after treatment, histological samples were done for alopecia. For laser groups (alopecia and ulcers) the results were rather superior and in a three or twice time shorter than control group. We conclude that LPL therapy is a very useful complementary method for the treatment of scalp alopecia and crural ulcers.

A Simulation Study on the Performance of the Simple Difference and Covariance-Adjusted Scores in Randomized Experimental Designs.

PubMed

Petscher, Yaacov; Schatschneider, Christopher

2011-01-01

Research by Huck and McLean (1975) demonstrated that the covariance-adjusted score is more powerful than the simple difference score, yet recent reviews indicate researchers are equally likely to use either score type in two-wave randomized experimental designs. A Monte Carlo simulation was conducted to examine the conditions under which the simple difference and covariance-adjusted scores were more or less powerful to detect treatment effects when relaxing certain assumptions made by Huck and McLean (1975). Four factors were manipulated in the design including sample size, normality of the pretest and posttest distributions, the correlation between pretest and posttest, and posttest variance. A 5 × 5 × 4 × 3 mostly crossed design was run with 1,000 replications per condition, resulting in 226,000 unique samples. The gain score was nearly as powerful as the covariance-adjusted score when pretest and posttest variances were equal, and as powerful in fan-spread growth conditions; thus, under certain circumstances the gain score could be used in two-wave randomized experimental designs.

A Simulation Study on the Performance of the Simple Difference and Covariance-Adjusted Scores in Randomized Experimental Designs

PubMed Central

Petscher, Yaacov; Schatschneider, Christopher

2015-01-01

Research by Huck and McLean (1975) demonstrated that the covariance-adjusted score is more powerful than the simple difference score, yet recent reviews indicate researchers are equally likely to use either score type in two-wave randomized experimental designs. A Monte Carlo simulation was conducted to examine the conditions under which the simple difference and covariance-adjusted scores were more or less powerful to detect treatment effects when relaxing certain assumptions made by Huck and McLean (1975). Four factors were manipulated in the design including sample size, normality of the pretest and posttest distributions, the correlation between pretest and posttest, and posttest variance. A 5 × 5 × 4 × 3 mostly crossed design was run with 1,000 replications per condition, resulting in 226,000 unique samples. The gain score was nearly as powerful as the covariance-adjusted score when pretest and posttest variances were equal, and as powerful in fan-spread growth conditions; thus, under certain circumstances the gain score could be used in two-wave randomized experimental designs. PMID:26379310

Dispersion models and sampling of cacao mirid bug Sahlbergella singularis (Hemiptera: Miridae) on Theobroma Cacao in southern Cameroon.

PubMed

Bisseleua, D H B; Vidal, Stefan

2011-02-01

The spatio-temporal distribution of Sahlbergella singularis Haglung, a major pest of cacao trees (Theobroma cacao) (Malvaceae), was studied for 2 yr in traditional cacao forest gardens in the humid forest area of southern Cameroon. The first objective was to analyze the dispersion of this insect on cacao trees. The second objective was to develop sampling plans based on fixed levels of precision for estimating S. singularis populations. The following models were used to analyze the data: Taylor's power law, Iwao's patchiness regression, the Nachman model, and the negative binomial distribution. Our results document that Taylor's power law was a better fit for the data than the Iwao and Nachman models. Taylor's b and Iwao's β were both significantly >1, indicating that S. singularis aggregated on specific trees. This result was further supported by the calculated common k of 1.75444. Iwao's α was significantly <0, indicating that the basic distribution component of S. singularis was the individual insect. Comparison of negative binomial (NBD) and Nachman models indicated that the NBD model was appropriate for studying S. singularis distribution. Optimal sample sizes for fixed precision levels of 0.10, 0.15, and 0.25 were estimated with Taylor's regression coefficients. Required sample sizes increased dramatically with increasing levels of precision. This is the first study on S. singularis dispersion in cacao plantations. Sampling plans, presented here, should be a tool for research on population dynamics and pest management decisions of mirid bugs on cacao. © 2011 Entomological Society of America

Increasing efficiency of preclinical research by group sequential designs

PubMed Central

Piper, Sophie K.; Rex, Andre; Florez-Vargas, Oscar; Karystianis, George; Schneider, Alice; Wellwood, Ian; Siegerink, Bob; Ioannidis, John P. A.; Kimmelman, Jonathan; Dirnagl, Ulrich

2017-01-01

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain. PMID:28282371

Laser system refinements to reduce variability in infarct size in the rat photothrombotic stroke model

PubMed Central

Alaverdashvili, Mariam; Paterson, Phyllis G.; Bradley, Michael P.

2015-01-01

Background The rat photothrombotic stroke model can induce brain infarcts with reasonable biological variability. Nevertheless, we observed unexplained high inter-individual variability despite using a rigorous protocol. Of the three major determinants of infarct volume, photosensitive dye concentration and illumination period were strictly controlled, whereas undetected fluctuation in laser power output was suspected to account for the variability. New method The frequently utilized Diode Pumped Solid State (DPSS) lasers emitting 532 nm (green) light can exhibit fluctuations in output power due to temperature and input power alterations. The polarization properties of the Nd:YAG and Nd:YVO4 crystals commonly used in these lasers are another potential source of fluctuation, since one means of controlling output power uses a polarizer with a variable transmission axis. Thus, the properties of DPSS lasers and the relationship between power output and infarct size were explored. Results DPSS laser beam intensity showed considerable variation. Either a polarizer or a variable neutral density filter allowed adjustment of a polarized laser beam to the desired intensity. When the beam was unpolarized, the experimenter was restricted to using a variable neutral density filter. Comparison with existing method(s) Our refined approach includes continuous monitoring of DPSS laser intensity via beam sampling using a pellicle beamsplitter and photodiode sensor. This guarantees the desired beam intensity at the targeted brain area during stroke induction, with the intensity controlled either through a polarizer or variable neutral density filter. Conclusions Continuous monitoring and control of laser beam intensity is critical for ensuring consistent infarct size. PMID:25840363

Field application of a multi-frequency acoustic instrument to monitor sediment for silt erosion study in Pelton turbine in Himalayan region, India

NASA Astrophysics Data System (ADS)

Rai, A. K.; Kumar, A.; Hies, T.; Nguyen, H. H.

2016-11-01

High sediment load passing through hydropower components erodes the hydraulic components resulting in loss of efficiency, interruptions in power production and downtime for repair/maintenance, especially in Himalayan regions. The size and concentration of sediment play a major role in silt erosion. The traditional process of collecting samples manually to analyse in laboratory cannot suffice the need of monitoring temporal variation in sediment properties. In this study, a multi-frequency acoustic instrument was applied at desilting chamber to monitor sediment size and concentration entering the turbine. The sediment size and concentration entering the turbine were also measured with manual samples collected twice daily. The samples collected manually were analysed in laboratory with a laser diffraction instrument for size and concentration apart from analysis by drying and filtering methods for concentration. A conductivity probe was used to calculate total dissolved solids, which was further used in results from drying method to calculate suspended solid content of the samples. The acoustic instrument was found to provide sediment concentration values similar to drying and filtering methods. However, no good match was found between mean grain size from the acoustic method with the current status of development and laser diffraction method in the first field application presented here. The future versions of the software and significant sensitivity improvements of the ultrasonic transducers are expected to increase the accuracy in the obtained results. As the instrument is able to capture the concentration and in the future most likely more accurate mean grain size of the suspended sediments, its application for monitoring silt erosion in hydropower plant shall be highly useful.

The multiple imputation method: a case study involving secondary data analysis.

PubMed

Walani, Salimah R; Cleland, Charles M

2015-05-01

To illustrate with the example of a secondary data analysis study the use of the multiple imputation method to replace missing data. Most large public datasets have missing data, which need to be handled by researchers conducting secondary data analysis studies. Multiple imputation is a technique widely used to replace missing values while preserving the sample size and sampling variability of the data. The 2004 National Sample Survey of Registered Nurses. The authors created a model to impute missing values using the chained equation method. They used imputation diagnostics procedures and conducted regression analysis of imputed data to determine the differences between the log hourly wages of internationally educated and US-educated registered nurses. The authors used multiple imputation procedures to replace missing values in a large dataset with 29,059 observations. Five multiple imputed datasets were created. Imputation diagnostics using time series and density plots showed that imputation was successful. The authors also present an example of the use of multiple imputed datasets to conduct regression analysis to answer a substantive research question. Multiple imputation is a powerful technique for imputing missing values in large datasets while preserving the sample size and variance of the data. Even though the chained equation method involves complex statistical computations, recent innovations in software and computation have made it possible for researchers to conduct this technique on large datasets. The authors recommend nurse researchers use multiple imputation methods for handling missing data to improve the statistical power and external validity of their studies.

A 12b 200kS/s 0.52mA 0.47mm2 Algorithmic A/D Converter for MEMS Applications

NASA Astrophysics Data System (ADS)

Kim, Young-Ju; Choi, Hee-Cheol; Lee, Seung-Hoon; Cho, Dongil “Dan”

This work describes a 12b 200kS/s 0.52mA 0.47mm2 ADC for sensor applications such as motor control, 3-phase power control, and CMOS image sensors simultaneously requiring ultra-low power and small size. The proposed ADC is based on the conventional algorithmic architecture with a recycling signal path to optimize sampling rate, resolution, chip area, and power consumption. The input SHA with eight input channels employs a folded-cascode amplifier to achieve a required DC gain and a high phase margin. A 3-D fully symmetric layout with critical signal lines shielded reduces the capacitor and device mismatch of the multiplying D/A converter while switched-bias power-reduction circuits minimize the power consumption of analog amplifiers. Current and voltage references are integrated on chip with optional off-chip voltage references for low glitch noise. The down-sampling clock signal selects the sampling rate of 200kS/s and 10kS/s with a further reduced power depending on applications. The prototype ADC in a 0.18μm n-well 1P6M CMOS process demonstrates a maximum measured DNL and INL within 0.40 LSB and 1.97 LSB and shows a maximum SNDR and SFDR of 55dB and 70dB at all sampling frequencies up to 200kS/s, respectively. The ADC occupies an active die area of 0.47mm2 and consumes 0.94mW at 200kS/s and 0.63mW at 10kS/s with a 1.8V supply.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Do adults show a curse of knowledge in false-belief reasoning? A robust estimate of the true effect size.

PubMed

Ryskin, Rachel A; Brown-Schmidt, Sarah

2014-01-01

Seven experiments use large sample sizes to robustly estimate the effect size of a previous finding that adults are more likely to commit egocentric errors in a false-belief task when the egocentric response is plausible in light of their prior knowledge. We estimate the true effect size to be less than half of that reported in the original findings. Even though we found effects in the same direction as the original, they were substantively smaller; the original study would have had less than 33% power to detect an effect of this magnitude. The influence of plausibility on the curse of knowledge in adults appears to be small enough that its impact on real-life perspective-taking may need to be reevaluated.

Microwave enhanced recovery of nickel-copper ore: communition and floatability aspects.

PubMed

Henda, R; Hermas, A; Gedye, R; Islam, M R

2005-01-01

A study describing the effect of microwave radiation, at a frequency of 2450 MHz, on the processes of communication and flotation of a complex sulphide nickel-copper ore is presented. Ore communication has been investigated under standard radiation-free conditions and after ore treatment in a radiated environment as a function of ore size, exposure time to radiation, and microwave power. The findings show that communication is tremendously improved by microwave radiation with values of the relative work index as low as 23% at a microwave power of 1.406 kW and after 10 s of exposure time. Communication is affected by exposure time and microwave power in a nontrivial manner. In terms of ore floatability, the experimental tests have been carried out on a sample of 75 microm in size under different exposure times. The results show that both ore concentrate recoveries and grades of nickel and copper are significantly enhanced after microwave treatment of the ore with relative increases in recovered concentrate, grade of nickel, and grade of copper of 26 wt%, 15 wt%, and 27%, respectively, at a microwave power of 1330 kW and after 30 s of exposure time.

Alzheimer Disease Biomarkers as Outcome Measures for Clinical Trials in MCI.

PubMed

Caroli, Anna; Prestia, Annapaola; Wade, Sara; Chen, Kewei; Ayutyanont, Napatkamon; Landau, Susan M; Madison, Cindee M; Haense, Cathleen; Herholz, Karl; Reiman, Eric M; Jagust, William J; Frisoni, Giovanni B

2015-01-01

The aim of this study was to compare the performance and power of the best-established diagnostic biological markers as outcome measures for clinical trials in patients with mild cognitive impairment (MCI). Magnetic resonance imaging, F-18 fluorodeoxyglucose positron emission tomography markers, and Alzheimer's Disease Assessment Scale-cognitive subscale were compared in terms of effect size and statistical power over different follow-up periods in 2 MCI groups, selected from Alzheimer's Disease Neuroimaging Initiative data set based on cerebrospinal fluid (abnormal cerebrospinal fluid Aβ1-42 concentration-ABETA+) or magnetic resonance imaging evidence of Alzheimer disease (positivity to hippocampal atrophy-HIPPO+). Biomarkers progression was modeled through mixed effect models. Scaled slope was chosen as measure of effect size. Biomarkers power was estimated using simulation algorithms. Seventy-four ABETA+ and 51 HIPPO+ MCI patients were included in the study. Imaging biomarkers of neurodegeneration, especially MR measurements, showed highest performance. For all biomarkers and both MCI groups, power increased with increasing follow-up time, irrespective of biomarker assessment frequency. These findings provide information about biomarker enrichment and outcome measurements that could be employed to reduce MCI patient samples and treatment duration in future clinical trials.

Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

PubMed Central

Lakens, Daniël

2013-01-01

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow. PMID:24324449

Low-Power SOI CMOS Transceiver

NASA Technical Reports Server (NTRS)

Fujikawa, Gene (Technical Monitor); Cheruiyot, K.; Cothern, J.; Huang, D.; Singh, S.; Zencir, E.; Dogan, N.

2003-01-01

The work aims at developing a low-power Silicon on Insulator Complementary Metal Oxide Semiconductor (SOI CMOS) Transceiver for deep-space communications. RF Receiver must accomplish the following tasks: (a) Select the desired radio channel and reject other radio signals, (b) Amplify the desired radio signal and translate them back to baseband, and (c) Detect and decode the information with Low BER. In order to minimize cost and achieve high level of integration, receiver architecture should use least number of external filters and passive components. It should also consume least amount of power to minimize battery cost, size, and weight. One of the most stringent requirements for deep-space communication is the low-power operation. Our study identified that two candidate architectures listed in the following meet these requirements: (1) Low-IF receiver, (2) Sub-sampling receiver. The low-IF receiver uses minimum number of external components. Compared to Zero-IF (Direct conversion) architecture, it has less severe offset and flicker noise problems. The Sub-sampling receiver amplifies the RF signal and samples it using track-and-hold Subsampling mixer. These architectures provide low-power solution for the short- range communications missions on Mars. Accomplishments to date include: (1) System-level design and simulation of a Double-Differential PSK receiver, (2) Implementation of Honeywell SOI CMOS process design kit (PDK) in Cadence design tools, (3) Design of test circuits to investigate relationships between layout techniques, geometry, and low-frequency noise in SOI CMOS, (4) Model development and verification of on-chip spiral inductors in SOI CMOS process, (5) Design/implementation of low-power low-noise amplifier (LNA) and mixer for low-IF receiver, and (6) Design/implementation of high-gain LNA for sub-sampling receiver. Our initial results show that substantial improvement in power consumption is achieved using SOI CMOS as compared to standard CMOS process. Potential advantages of SOI CMOS for deep-space communication electronics include: (1) Radiation hardness, (2) Low-power operation, and (3) System-on-Chip (SOC) solutions.

Model error in covariance structure models: Some implications for power and Type I error

PubMed Central

Coffman, Donna L.

2010-01-01

The present study investigated the degree to which violation of the parameter drift assumption affects the Type I error rate for the test of close fit and power analysis procedures proposed by MacCallum, Browne, and Sugawara (1996) for both the test of close fit and the test of exact fit. The parameter drift assumption states that as sample size increases both sampling error and model error (i.e. the degree to which the model is an approximation in the population) decrease. Model error was introduced using a procedure proposed by Cudeck and Browne (1992). The empirical power for both the test of close fit, in which the null hypothesis specifies that the Root Mean Square Error of Approximation (RMSEA) ≤ .05, and the test of exact fit, in which the null hypothesis specifies that RMSEA = 0, is compared with the theoretical power computed using the MacCallum et al. (1996) procedure. The empirical power and theoretical power for both the test of close fit and the test of exact fit are nearly identical under violations of the assumption. The results also indicated that the test of close fit maintains the nominal Type I error rate under violations of the assumption. PMID:21331302

Hiding the Tobacco Power Wall Reduces Cigarette Smoking Risk in Adolescents: Using an Experimental Convenience Store to Assess Tobacco Regulatory Options at Retail Point-of-Sale

PubMed Central

Shadel, William G.; Martino, Steven; Setodji, Claude; Scharf, Deborah; Kusuke, Daniela; Sicker, Angela; Gong, Min

2015-01-01

Objectives This experiment tested whether changing the location or visibility of the tobacco power wall in a life sized replica of a convenience store had any effect on adolescents’ susceptibility to future cigarette smoking. Methods The study was conducted in the RAND StoreLab (RSL), a life sized replica of a convenience store that was developed to experimentally evaluate how changing aspects of tobacco advertising displays in retail point-of-sale environments influences tobacco use risk and behavior. A randomized, between-subjects experimental design with three conditions that varied the location or visibility of the tobacco power wall within the RSL was used. The conditions were: cashier (the tobacco power wall was located in its typical position behind the cash register counter); sidewall (the tobacco power wall was located on a sidewall away from the cash register); or hidden (the tobacco power wall was located behind the cashier but was hidden behind an opaque wall). The sample included 241 adolescents. Results Hiding the tobacco power wall significantly reduced adolescents’ susceptibility to future cigarette smoking compared to leaving it exposed (i.e., the cashier condition; p = .02). Locating the tobacco power wall on a sidewall away from the cashier had no effect on future cigarette smoking susceptibility compared to the cashier condition (p = 0.80). Conclusions Hiding the tobacco power wall at retail point-of-sale locations is a strong regulatory option for reducing the impact of the retail environment on cigarette smoking risk in adolescents. PMID:26598502

Methane Leaks from Natural Gas Systems Follow Extreme Distributions.

PubMed

Brandt, Adam R; Heath, Garvin A; Cooley, Daniel

2016-11-15

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH 4 ) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH 4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.

Methane Leaks from Natural Gas Systems Follow Extreme Distributions

DOE PAGES

Brandt, Adam R.; Heath, Garvin A.; Cooley, Daniel

2016-10-14

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH 4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ~15,000 measurements from 18 prior studies, we show that all available natural gas leakage datasets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of themore » total leakage volume. While prior studies used lognormal model distributions, we show that lognormal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH 4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of datasets to increase sample size is not recommended due to apparent deviation between sampled populations. Finally, understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.« less

Methane Leaks from Natural Gas Systems Follow Extreme Distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brandt, Adam R.; Heath, Garvin A.; Cooley, Daniel

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH 4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ~15,000 measurements from 18 prior studies, we show that all available natural gas leakage datasets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of themore » total leakage volume. While prior studies used lognormal model distributions, we show that lognormal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH 4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of datasets to increase sample size is not recommended due to apparent deviation between sampled populations. Finally, understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.« less

Impact of Sample Size and Variability on the Power and Type I Error Rates of Equivalence Tests: A Simulation Study

ERIC Educational Resources Information Center

Rusticus, Shayna A.; Lovato, Chris Y.

2014-01-01

The question of equivalence between two or more groups is frequently of interest to many applied researchers. Equivalence testing is a statistical method designed to provide evidence that groups are comparable by demonstrating that the mean differences found between groups are small enough that they are considered practically unimportant. Few…

Intraclass Correlation Coefficients for Obesity Indicators and Energy Balance-Related Behaviors among New York City Public Elementary Schools

ERIC Educational Resources Information Center

Gray, Heewon Lee; Burgermaster, Marissa; Tipton, Elizabeth; Contento, Isobel R.; Koch, Pamela A.; Di Noia, Jennifer

2016-01-01

Objective: Sample size and statistical power calculation should consider clustering effects when schools are the unit of randomization in intervention studies. The objective of the current study was to investigate how student outcomes are clustered within schools in an obesity prevention trial. Method: Baseline data from the Food, Health &…

Optimal Design in Three-Level Block Randomized Designs with Two Levels of Nesting: An ANOVA Framework with Random Effects

ERIC Educational Resources Information Center

Konstantopoulos, Spyros

2013-01-01

Large-scale experiments that involve nested structures may assign treatment conditions either to subgroups such as classrooms or to individuals such as students within subgroups. Key aspects of the design of such experiments include knowledge of the variance structure in higher levels and the sample sizes necessary to reach sufficient power to…

Estimates of Intraclass Correlation Coefficients from Longitudinal Group-Randomized Trials of Adolescent HIV/STI/Pregnancy Prevention Programs

ERIC Educational Resources Information Center

Glassman, Jill R.; Potter, Susan C.; Baumler, Elizabeth R.; Coyle, Karin K.

2015-01-01

Introduction: Group-randomized trials (GRTs) are one of the most rigorous methods for evaluating the effectiveness of group-based health risk prevention programs. Efficiently designing GRTs with a sample size that is sufficient for meeting the trial's power and precision goals while not wasting resources exceeding them requires estimates of the…

A Time-Domain CMOS Oscillator-Based Thermostat with Digital Set-Point Programming

PubMed Central

Chen, Chun-Chi; Lin, Shih-Hao

2013-01-01

This paper presents a time-domain CMOS oscillator-based thermostat with digital set-point programming [without a digital-to-analog converter (DAC) or external resistor] to achieve on-chip thermal management of modern VLSI systems. A time-domain delay-line-based thermostat with multiplexers (MUXs) was used to substantially reduce the power consumption and chip size, and can benefit from the performance enhancement due to the scaling down of fabrication processes. For further cost reduction and accuracy enhancement, this paper proposes a thermostat using two oscillators that are suitable for time-domain curvature compensation instead of longer linear delay lines. The final time comparison was achieved using a time comparator with a built-in custom hysteresis to generate the corresponding temperature alarm and control. The chip size of the circuit was reduced to 0.12 mm2 in a 0.35-μm TSMC CMOS process. The thermostat operates from 0 to 90 °C, and achieved a fine resolution better than 0.05 °C and an improved inaccuracy of ± 0.6 °C after two-point calibration for eight packaged chips. The power consumption was 30 μW at a sample rate of 10 samples/s. PMID:23385403

A simple encoding method for Sigma-Delta ADC based biopotential acquisition systems.

PubMed

Guerrero, Federico N; Spinelli, Enrique M

2017-10-01

Sigma Delta analogue-to-digital converters allow acquiring the full dynamic range of biomedical signals at the electrodes, resulting in less complex hardware and increased measurement robustness. However, the increased data size per sample (typically 24 bits) demands the transmission of extremely large volumes of data across the isolation barrier, thus increasing power consumption on the patient side. This problem is accentuated when a large number of channels is used as in current 128-256 electrodes biopotential acquisition systems, that usually opt for an optic fibre link to the computer. An analogous problem occurs for simpler low-power acquisition platforms that transmit data through a wireless link to a computing platform. In this paper, a low-complexity encoding method is presented to decrease sample data size without losses, while preserving the full DC-coupled signal. The method achieved a 2.3 average compression ratio evaluated over an ECG and EMG signal bank acquired with equipment based on Sigma-Delta converters. It demands a very low processing load: a C language implementation is presented that resulted in an 110 clock cycles average execution on an 8-bit microcontroller.

Optimisation of Microwave-Assisted Extraction of Pomegranate (Punica granatum L.) Seed Oil and Evaluation 
of Its Physicochemical and Bioactive Properties.

PubMed

Çavdar, Hasene Keskin; Yanık, Derya Koçak; Gök, Uğur; Göğüş, Fahrettin

2017-03-01

Pomegranate seed oil was extracted in a closed-vessel high-pressure microwave system. The characteristics of the obtained oil, such as fatty acid composition, free fatty acidity, total phenolic content, antioxidant activity and colour, were compared to those of the oil obtained by cold solvent extraction. Response surface methodology was applied to optimise extraction conditions: power (176-300 W), time (5-20 min), particle size ( d =0.125-0.800 mm) and solvent to sample ratio (2:1, 6:1 and 10:1, by mass). The predicted highest extraction yield (35.19%) was obtained using microwave power of 220 W, particle size in the range of d =0.125-0.450 mm and solvent-to-sample ratio of 10:1 (by mass) in 5 min extraction time. Microwave-assisted solvent extraction (MASE) resulted in higher extraction yield than that of Soxhlet (34.70% in 8 h) or cold (17.50% in 8 h) extraction. The dominant fatty acid of pomegranate seed oil was punicic acid (86%) irrespective of the extraction method. Oil obtained by MASE had better physicochemical properties, total phenolic content and antioxidant activity than the oil obtained by cold solvent extraction.

Determination of respirable-sized crystalline silica in different ambient environments in the United Kingdom with a mobile high flow rate sampler utilising porous foams to achieve the required particle size selection

NASA Astrophysics Data System (ADS)

Stacey, Peter; Thorpe, Andrew; Roberts, Paul; Butler, Owen

2018-06-01

Inhalation of respirable crystalline silica (RCS) can cause diseases including silicosis and cancer. Levels of RCS close to an emission source are measured but little is known about the wider ambient exposure from industry emissions or natural sources. The aim of this work is to report the RCS concentrations obtained from a variety of ambient environments using a new mobile respirable (PM4) sampler. A mobile battery powered high flow rate (52 L min-1) sampler was developed and evaluated for particulate aerosol sampling employing foams to select the respirable particle size fraction. Sampling was conducted in the United Kingdom at site boundaries surrounding seven urban construction and demolition and five sand quarry sites. These are compared with data from twelve urban aerosol samples and from repeat measurements from a base line study at a single rural site. The 50% particle size penetration (d50) through the foam was 4.3 μm. Over 85% of predict bias values were with ±10% of the respirable convention, which is based on a log normal curve. Results for RCS from all construction and quarry activities are generally low with a 95 th percentile of 11 μg m-3. Eighty percent of results were less than the health benchmark value of 3 μg m-3 used in some states in America for ambient concentrations. The power cutting of brick and the largest demolition activities gave the highest construction levels. Measured urban background RCS levels were typically below 0.3 μg m-3 and the median RCS level, at a rural background location, was 0.02 μg m-3. These reported ambient RCS concentrations may provide useful baseline values to assess the wider impact of fugitive, RCS containing, dust emissions into the wider environment.

Assessment and Implication of Prognostic Imbalance in Randomized Controlled Trials with a Binary Outcome – A Simulation Study

PubMed Central

Chu, Rong; Walter, Stephen D.; Guyatt, Gordon; Devereaux, P. J.; Walsh, Michael; Thorlund, Kristian; Thabane, Lehana

2012-01-01

Background Chance imbalance in baseline prognosis of a randomized controlled trial can lead to over or underestimation of treatment effects, particularly in trials with small sample sizes. Our study aimed to (1) evaluate the probability of imbalance in a binary prognostic factor (PF) between two treatment arms, (2) investigate the impact of prognostic imbalance on the estimation of a treatment effect, and (3) examine the effect of sample size (n) in relation to the first two objectives. Methods We simulated data from parallel-group trials evaluating a binary outcome by varying the risk of the outcome, effect of the treatment, power and prevalence of the PF, and n. Logistic regression models with and without adjustment for the PF were compared in terms of bias, standard error, coverage of confidence interval and statistical power. Results For a PF with a prevalence of 0.5, the probability of a difference in the frequency of the PF≥5% reaches 0.42 with 125/arm. Ignoring a strong PF (relative risk = 5) leads to underestimating the strength of a moderate treatment effect, and the underestimate is independent of n when n is >50/arm. Adjusting for such PF increases statistical power. If the PF is weak (RR = 2), adjustment makes little difference in statistical inference. Conditional on a 5% imbalance of a powerful PF, adjustment reduces the likelihood of large bias. If an absolute measure of imbalance ≥5% is deemed important, including 1000 patients/arm provides sufficient protection against such an imbalance. Two thousand patients/arm may provide an adequate control against large random deviations in treatment effect estimation in the presence of a powerful PF. Conclusions The probability of prognostic imbalance in small trials can be substantial. Covariate adjustment improves estimation accuracy and statistical power, and hence should be performed when strong PFs are observed. PMID:22629322

Sample size and power estimation for studies with health related quality of life outcomes: a comparison of four methods using the SF-36.

PubMed

Walters, Stephen J

2004-05-25

We describe and compare four different methods for estimating sample size and power, when the primary outcome of the study is a Health Related Quality of Life (HRQoL) measure. These methods are: 1. assuming a Normal distribution and comparing two means; 2. using a non-parametric method; 3. Whitehead's method based on the proportional odds model; 4. the bootstrap. We illustrate the various methods, using data from the SF-36. For simplicity this paper deals with studies designed to compare the effectiveness (or superiority) of a new treatment compared to a standard treatment at a single point in time. The results show that if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at the boundaries is high (scoring 0 or 100), then we would recommend using Whitehead's method (Method 3). Alternatively, if the HRQoL outcome has a large number of distinct values and the proportion at the boundaries is low, then we would recommend using Method 1. If a pilot or historical dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation (Method 4) based on this data will provide a more accurate and reliable sample size estimate than conventional methods (Methods 1, 2, or 3). In the absence of a reliable pilot set, bootstrapping is not appropriate and conventional methods of sample size estimation or simulation will need to be used. Fortunately, with the increasing use of HRQoL outcomes in research, historical datasets are becoming more readily available. Strictly speaking, our results and conclusions only apply to the SF-36 outcome measure. Further empirical work is required to see whether these results hold true for other HRQoL outcomes. However, the SF-36 has many features in common with other HRQoL outcomes: multi-dimensional, ordinal or discrete response categories with upper and lower bounds, and skewed distributions, so therefore, we believe these results and conclusions using the SF-36 will be appropriate for other HRQoL measures.

Effects of sources of variability on sample sizes required for RCTs, applied to trials of lipid-altering therapies on carotid artery intima-media thickness.

PubMed

Gould, A Lawrence; Koglin, Joerg; Bain, Raymond P; Pinto, Cathy-Anne; Mitchel, Yale B; Pasternak, Richard C; Sapre, Aditi

2009-08-01

Studies measuring progression of carotid artery intima-media thickness (cIMT) have been used to estimate the effect of lipid-modifying therapies cardiovascular event risk. The likelihood that future cIMT clinical trials will detect a true treatment effect is estimated by leveraging results from prior studies. The present analyses assess the impact of between- and within-study variability based on currently published data from prior clinical studies on the likelihood that ongoing or future cIMT trials will detect the true treatment effect of lipid-modifying therapies. Published data from six contemporary cIMT studies (ASAP, ARBITER 2, RADIANCE 1, RADIANCE 2, ENHANCE, and METEOR) including data from a total of 3563 patients were examined. Bayesian and frequentist methods were used to assess the impact of between study variability on the likelihood of detecting true treatment effects on 1-year cIMT progression/regression and to provide a sample size estimate that would specifically compensate for the effect of between-study variability. In addition to the well-described within-study variability, there is considerable between-study variability associated with the measurement of annualized change in cIMT. Accounting for the additional between-study variability decreases the power for existing study designs. In order to account for the added between-study variability, it is likely that future cIMT studies would require a large increase in sample size in order to provide substantial probability (> or =90%) to have 90% power of detecting a true treatment effect.Limitation Analyses are based on study level data. Future meta-analyses incorporating patient-level data would be useful for confirmation. Due to substantial within- and between-study variability in the measure of 1-year change of cIMT, as well as uncertainty about progression rates in contemporary populations, future study designs evaluating the effect of new lipid-modifying therapies on atherosclerotic disease progression are likely to be challenged by large sample sizes in order to demonstrate a true treatment effect.

Size effects on electrical properties of chemically grown zinc oxide nanoparticles

NASA Astrophysics Data System (ADS)

Rathod, K. N.; Joshi, Zalak; Dhruv, Davit; Gadani, Keval; Boricha, Hetal; Joshi, A. D.; Solanki, P. S.; Shah, N. A.

2018-03-01

In the present article, we study ZnO nanoparticles grown by cost effective sol–gel technique for various electrical properties. Structural studies performed by x-ray diffraction (XRD) revealed hexagonal unit cell phase with no observed impurities. Transmission electron microscopy (TEM) and particle size analyzer showed increased average particle size due to agglomeration effect with higher sintering. Dielectric constant (ε‧) decreases with increase in frequency because of the disability of dipoles to follow higher electric field. With higher sintering, dielectric constant reduced owing to the important role of increased formation of oxygen vacancy defects. Universal dielectric response (UDR) was verified by straight line fitting of log (fε‧) versus log (f) plots. All samples exhibit UDR behavior and with higher sintering more contribution from crystal cores. Impedance studies suggest an important role of boundary density while Cole–Cole (Z″ versus Z‧) plots have been studied for the relaxation behavior of the samples. Average normalized change (ANC) in impedance has been studied for all the samples wherein boundaries play an important role. Frequency dependent electrical conductivity has been understood on the basis of Jonscher’s universal power law. Jonscher’s law fits suggest that conduction of charge carrier is possible in the context of correlated barrier hopping (CBH) mechanism for lower temperature sintered sample while for higher temperature sintered ZnO samples, Maxwell–Wagner (M–W) relaxation process has been determined.

Comparative study of the microstructural and magnetic properties of fly ashes obtained from different thermal power plants in West Bengal, India.

PubMed

Bhattacharjee, Ashis; Mandal, Haradhan; Roy, Madhusudan; Kusz, Joachim; Hofmeister, Wolfgang

2013-10-01

This paper deals with the physical nature of the fly ashes obtained from two thermal power plants, situated in West Bengal, India. The fly ash samples are characterized by using comprehensive techniques with an emphasis on their ultrafine nature. The particle sizes of the samples are estimated using scanning electron microcopy (SEM) and found to lie within 0.18-5.90 μm. For morphology and compositional analysis, we also use SEM coupled with energy dispersive X-ray spectrometry. From X-ray study of the fly ashes the nature of conglomeration is seen to be crystalline, and the major components are mullite (Al6Si2O13) and quartz (SiO2). The magnetic measurement of the fly ash samples was carried out by SQUID magnetometer. (57)Fe Mössbauer spectra are obtained using a conventional constant-acceleration spectrometer with a (57)Co/Rh Mössbauer source. The hyperfine parameters obtained, in general, support the findings as made from XRD analysis and provide a quantitative measure of different iron ions present in the samples. The paper presents experimental data on the physical aspects of the fly ash samples of the thermal power plants which comprise coarse, fine, and ultrafine magnetic particulate materials and attempts to provide an exhaustive analysis.

The three youngest Plinian eruptions of Mt Pelée, Martinique (P1, P2 and P3): Constraining the eruptive conditions from field and experimental studies.

NASA Astrophysics Data System (ADS)

Kueppers, Ulrich; Uhlig, Joan; Carazzo, Guillaume; Kaminski, Edouard; Perugini, Diego; Tait, Steve; Clouard, Valérie

2015-04-01

Mt Pelée on Martinique, French Lesser Indies, is infamous for the last big Pelean (i.e., dome forming) eruption in 1902 AD that destroyed agricultural land and the city of Saint Pierre by pyroclastic density currents. Beside such mostly valley-confined deposits, the geological record shows thick fall deposits of at least three Plinian eruptions during the past 2000 years. In an attempt to describe and understand systematic eruptive behaviours as well as the associated variability of eruptive scenarios of Plinian eruptions in Martinique, we have investigated approx. 50 outcrops belonging to the P1 (1315 AD), P2 (345 AD) and P3 (4 AD) eruptions (Traineau et al., JVGR 1989) and collected bulk samples as well as >100 mm pumiceous clasts. All samples are andesitic, contain plagioclase and pyroxene in a glassy matrix and range in porosity between 55 and 69 vol.% with individual bubbles rarely larger than 1 mm. Our approach was two-fold: 1) Loose bulk samples have been subject to dry mechanical sieving in order to quantively describe the grain-size distribution and the fractal dimension. 2) From large clasts, 60*25 mm cylinders have been drilled for fragmentation experiments following the sudden decompression of gas in the sample's pore space. The used experimental set-up allowed for precisely controllable and repeatable conditions (5, 10 and 15 MPa, 25 °C) and the complete sampling of the generated pyroclasts. These experimentally generated clasts were analysed for their grain-size distribution and fractal dimension. For both natural samples and experimental populations, we find we find that the grain-size distribution follows a power-law, with an exponent between 2,5 and 3,7. Deciphering eruption conditions from deposits alone is challenging because of the complex interplay of dynamic volcanic processes and transport-related sorting. We use the quantified values of fractal dimension for a comparison of the power law exponents among the three eruptions and the laboratory results. This will contribute to an increased interpretability of well-preserved deposits and a critical evaluation of the limits.

Comparison of rheumatoid arthritis clinical trial outcome measures: a simulation study.

PubMed

Anderson, Jennifer J; Bolognese, James A; Felson, David T

2003-11-01

Isolated studies have suggested that continuous measures of response may be better than predefined, dichotomous definitions (e.g., the American College of Rheumatology 20% improvement criteria [ACR20]) for discriminating between rheumatoid arthritis (RA) treatments. Our goal was to determine the statistical power of predefined dichotomous outcome measures (termed "a priori"), compared with that of continuous measures derived from trial data in which there was no predefined response threshold (termed "data driven"), and to evaluate the sensitivity to change of these measures in the context of different treatments and early versus later-stage disease. In order to generalize beyond results from a single trial, we performed simulation studies. We obtained summary data from trials comparing disease-modifying antirheumatic drugs (DMARDs) and from comparative coxib-placebo trials to test the power of 2 a priori outcomes, the ACR20 and improvement of the Disease Activity Score (DDAS), as well as 2 data-driven outcomes. We studied patients with early RA and those with later-stage RA (duration of <4 years and 4-9 years, respectively). We performed simulation studies, using the interrelationship of ACR core set measures in the trials to generate multiple trial data sets consistent with the original data. The data-driven outcomes had greater power than did the a priori measures. The DMARD comparison was more powerful in early disease than in later-stage disease (the sample sizes needed to achieve 80% power for the most powerful test were 64 for early disease versus 100 for later disease), but the coxib-versus-placebo comparison was less powerful in early disease than in later disease (the sample sizes needed to achieve 80% power were 200 and 100, respectively). When the effects of treatment on core set items were small and/or inconsistent, power was reduced, particularly for a less broadly based outcome (e.g., DDAS) compared with the ACR20. The simulation studies demonstrate that data-driven outcome definitions can provide better sensitivity to change than does the ACR20 or DDAS. Using such methods would improve power, but at the expense of trial standardization. The studies also show how patient population and treatment characteristics affect the power of specific outcome measures in RA clinical trials, and provide quantification of those effects.

15N in tree rings as a bio-indicator of changing nitrogen cycling in tropical forests: an evaluation at three sites using two sampling methods

PubMed Central

van der Sleen, Peter; Vlam, Mart; Groenendijk, Peter; Anten, Niels P. R.; Bongers, Frans; Bunyavejchewin, Sarayudh; Hietz, Peter; Pons, Thijs L.; Zuidema, Pieter A.

2015-01-01

Anthropogenic nitrogen deposition is currently causing a more than twofold increase of reactive nitrogen input over large areas in the tropics. Elevated 15N abundance (δ15N) in the growth rings of some tropical trees has been hypothesized to reflect an increased leaching of 15N-depleted nitrate from the soil, following anthropogenic nitrogen deposition over the last decades. To find further evidence for altered nitrogen cycling in tropical forests, we measured long-term δ15N values in trees from Bolivia, Cameroon, and Thailand. We used two different sampling methods. In the first, wood samples were taken in a conventional way: from the pith to the bark across the stem of 28 large trees (the “radial” method). In the second, δ15N values were compared across a fixed diameter (the “fixed-diameter” method). We sampled 400 trees that differed widely in size, but measured δ15N in the stem around the same diameter (20 cm dbh) in all trees. As a result, the growth rings formed around this diameter differed in age and allowed a comparison of δ15N values over time with an explicit control for potential size-effects on δ15N values. We found a significant increase of tree-ring δ15N across the stem radius of large trees from Bolivia and Cameroon, but no change in tree-ring δ15N values over time was found in any of the study sites when controlling for tree size. This suggests that radial trends of δ15N values within trees reflect tree ontogeny (size development). However, for the trees from Cameroon and Thailand, a low statistical power in the fixed-diameter method prevents to conclude this with high certainty. For the trees from Bolivia, statistical power in the fixed-diameter method was high, showing that the temporal trend in tree-ring δ15N values in the radial method is primarily caused by tree ontogeny and unlikely by a change in nitrogen cycling. We therefore stress to account for tree size before tree-ring δ15N values can be properly interpreted. PMID:25914707

(15)N in tree rings as a bio-indicator of changing nitrogen cycling in tropical forests: an evaluation at three sites using two sampling methods.

PubMed

van der Sleen, Peter; Vlam, Mart; Groenendijk, Peter; Anten, Niels P R; Bongers, Frans; Bunyavejchewin, Sarayudh; Hietz, Peter; Pons, Thijs L; Zuidema, Pieter A

2015-01-01

Anthropogenic nitrogen deposition is currently causing a more than twofold increase of reactive nitrogen input over large areas in the tropics. Elevated (15)N abundance (δ(15)N) in the growth rings of some tropical trees has been hypothesized to reflect an increased leaching of (15)N-depleted nitrate from the soil, following anthropogenic nitrogen deposition over the last decades. To find further evidence for altered nitrogen cycling in tropical forests, we measured long-term δ(15)N values in trees from Bolivia, Cameroon, and Thailand. We used two different sampling methods. In the first, wood samples were taken in a conventional way: from the pith to the bark across the stem of 28 large trees (the "radial" method). In the second, δ(15)N values were compared across a fixed diameter (the "fixed-diameter" method). We sampled 400 trees that differed widely in size, but measured δ(15)N in the stem around the same diameter (20 cm dbh) in all trees. As a result, the growth rings formed around this diameter differed in age and allowed a comparison of δ(15)N values over time with an explicit control for potential size-effects on δ(15)N values. We found a significant increase of tree-ring δ(15)N across the stem radius of large trees from Bolivia and Cameroon, but no change in tree-ring δ(15)N values over time was found in any of the study sites when controlling for tree size. This suggests that radial trends of δ(15)N values within trees reflect tree ontogeny (size development). However, for the trees from Cameroon and Thailand, a low statistical power in the fixed-diameter method prevents to conclude this with high certainty. For the trees from Bolivia, statistical power in the fixed-diameter method was high, showing that the temporal trend in tree-ring δ(15)N values in the radial method is primarily caused by tree ontogeny and unlikely by a change in nitrogen cycling. We therefore stress to account for tree size before tree-ring δ(15)N values can be properly interpreted.

Studies on Microstructure and Thermoelectric Properties of p-Type Bi-Sb-Te Based Alloys by Gas Atomization and Hot Extrusion Processes

NASA Astrophysics Data System (ADS)

Park, Ki-Chan; Madavali, Babu; Kim, Eun-Bin; Koo, Kyung-Wan; Hong, Soon-Jik

2017-05-01

p-Type Bi2Te3 + 75% Sb2Te3 based thermoelectric materials were fabricated via gas atomization and the hot extrusion process. The gas atomized powder showed a clean surface with a spherical shape, and expanded in a wide particle size distribution (average particle size 50 μm). The phase of the fabricated extruded and R-extruded bars was identified using x-ray diffraction. The relative densities of both the extruded and R-extruded samples were measured by Archimedes principle with ˜98% relative density. The R-extruded bar exhibited finer grain microstructure than that of single extrusion process, which was attributed to a recrystallization mechanism during the fabrication. The R-extruded sample showed improved Vickers hardness compared to the extruded sample due to its fine grain microstructure. The electrical conductivity improved for the extruded sample whereas the Seebeck coefficient decreases due to its high carrier concentration. The peak power factor, ˜4.26 × 10-3 w/mK2 was obtained for the single extrusion sample, which is higher than the R-extrusion sample owing to its high electrical properties.

Study of water-oil emulsion combustion in large pilot power plants for fine particle matter emission reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allouis, C.; Beretta, F.; L'Insalata, A.

2007-04-15

The combustion of heavy fuel oil for power generation is a great source of carbonaceous and inorganic particle emissions, even though the combustion technologies and their efficiency are improving. The information about the size distribution function of the particles originated by trace metals present into the fuels is not adequate. In this paper, we focused our attention the influence of emulsion oil-water on the larger distribution mode of both the carbonaceous and metallic particles. Isokinetic sampling was performed at the exhausts of flames of a low-sulphur content heavy oil and its emulsion with water produced in two large pilot plants.more » The samples were size-segregated by mean of an 8-stages Andersen impactor. Further investigation performed on the samples using electronic microscopy (SEM) coupled with X-ray analysis (EDX) evidenced the presence of solid spherical particles, plerosphere, with typical dimensions ranging between 200 nm and 2-3 {mu}m, whose atomic composition contains a large amount of the trace metals present in the parent oils (Fe, V, Ni, etc.). EDX analyses revealed that the metal concentration increases as the plerosphere dimension decreases. We also observed that the use of emulsion slightly reduce the emission of fine particles (D{sub 50} < 8 {mu}m) in the large scale plant. (author)« less

Small sample mediation testing: misplaced confidence in bootstrapped confidence intervals.

PubMed

Koopman, Joel; Howe, Michael; Hollenbeck, John R; Sin, Hock-Peng

2015-01-01

Bootstrapping is an analytical tool commonly used in psychology to test the statistical significance of the indirect effect in mediation models. Bootstrapping proponents have particularly advocated for its use for samples of 20-80 cases. This advocacy has been heeded, especially in the Journal of Applied Psychology, as researchers are increasingly utilizing bootstrapping to test mediation with samples in this range. We discuss reasons to be concerned with this escalation, and in a simulation study focused specifically on this range of sample sizes, we demonstrate not only that bootstrapping has insufficient statistical power to provide a rigorous hypothesis test in most conditions but also that bootstrapping has a tendency to exhibit an inflated Type I error rate. We then extend our simulations to investigate an alternative empirical resampling method as well as a Bayesian approach and demonstrate that they exhibit comparable statistical power to bootstrapping in small samples without the associated inflated Type I error. Implications for researchers testing mediation hypotheses in small samples are presented. For researchers wishing to use these methods in their own research, we have provided R syntax in the online supplemental materials. (c) 2015 APA, all rights reserved.

Computational Process Modeling for Additive Manufacturing

NASA Technical Reports Server (NTRS)

Bagg, Stacey; Zhang, Wei

2014-01-01

Computational Process and Material Modeling of Powder Bed additive manufacturing of IN 718. Optimize material build parameters with reduced time and cost through modeling. Increase understanding of build properties. Increase reliability of builds. Decrease time to adoption of process for critical hardware. Potential to decrease post-build heat treatments. Conduct single-track and coupon builds at various build parameters. Record build parameter information and QM Meltpool data. Refine Applied Optimization powder bed AM process model using data. Report thermal modeling results. Conduct metallography of build samples. Calibrate STK models using metallography findings. Run STK models using AO thermal profiles and report STK modeling results. Validate modeling with additional build. Photodiode Intensity measurements highly linear with power input. Melt Pool Intensity highly correlated to Melt Pool Size. Melt Pool size and intensity increase with power. Applied Optimization will use data to develop powder bed additive manufacturing process model.

Constructs of power and equity and their association with contraceptive use among men and women in rural Ethiopia and Kenya.

PubMed

Stephenson, Rob; Bartel, Doris; Rubardt, Marcie

2012-01-01

Using samples of reproductive aged men and women from rural Ethiopia and Kenya, this study examines the associations between two scales measuring balances of power and equitable attitudes within relationships and modern contraceptive use. The scales are developed from the Sexual and Reproductive Power Scale (SRPS) and Gender Equitable Male (GEM) scale, which were originally developed to measure relationship power (SRPS) among women and gender equitable attitudes (GEM) among men. With the exception of Ethiopian women, a higher score on the balance of power scale was associated with significantly higher odds of reporting modern contraceptive use. For men and women in both countries, a higher score on the equitable attitudes scale was associated with significantly higher odds of reporting modern contraceptive use. However, only the highest categories of the scales are associated with contraceptive use, suggesting a threshold effect in the relationships between power, equity and contraceptive use. The results presented here demonstrate how elements of the GEM and SRPS scales can be used to create scales measuring balances of power and equitable attitudes within relationships that are associated with self-reporting of modern contraceptive use in two resource-poor settings. However, further work with larger sample sizes is needed to confirm these findings, and to examine the extent to which these scales can be applied to other social and cultural contexts.

Statistical power as a function of Cronbach alpha of instrument questionnaire items.

PubMed

Heo, Moonseong; Kim, Namhee; Faith, Myles S

2015-10-14

In countless number of clinical trials, measurements of outcomes rely on instrument questionnaire items which however often suffer measurement error problems which in turn affect statistical power of study designs. The Cronbach alpha or coefficient alpha, here denoted by C(α), can be used as a measure of internal consistency of parallel instrument items that are developed to measure a target unidimensional outcome construct. Scale score for the target construct is often represented by the sum of the item scores. However, power functions based on C(α) have been lacking for various study designs. We formulate a statistical model for parallel items to derive power functions as a function of C(α) under several study designs. To this end, we assume fixed true score variance assumption as opposed to usual fixed total variance assumption. That assumption is critical and practically relevant to show that smaller measurement errors are inversely associated with higher inter-item correlations, and thus that greater C(α) is associated with greater statistical power. We compare the derived theoretical statistical power with empirical power obtained through Monte Carlo simulations for the following comparisons: one-sample comparison of pre- and post-treatment mean differences, two-sample comparison of pre-post mean differences between groups, and two-sample comparison of mean differences between groups. It is shown that C(α) is the same as a test-retest correlation of the scale scores of parallel items, which enables testing significance of C(α). Closed-form power functions and samples size determination formulas are derived in terms of C(α), for all of the aforementioned comparisons. Power functions are shown to be an increasing function of C(α), regardless of comparison of interest. The derived power functions are well validated by simulation studies that show that the magnitudes of theoretical power are virtually identical to those of the empirical power. Regardless of research designs or settings, in order to increase statistical power, development and use of instruments with greater C(α), or equivalently with greater inter-item correlations, is crucial for trials that intend to use questionnaire items for measuring research outcomes. Further development of the power functions for binary or ordinal item scores and under more general item correlation strutures reflecting more real world situations would be a valuable future study.

Monitoring disease progression with plasma creatinine in amyotrophic lateral sclerosis clinical trials

PubMed Central

van Eijk, Ruben P A; Eijkemans, Marinus J C; Ferguson, Toby A; Nikolakopoulos, Stavros; Veldink, Jan H; van den Berg, Leonard H

2018-01-01

Objectives Plasma creatinine is a predictor of survival in amyotrophic lateral sclerosis (ALS). It remains, however, to be established whether it can monitor disease progression and serve as surrogate endpoint in clinical trials. Methods We used clinical trial data from three cohorts of clinical trial participants in the LITRA, EMPOWER and PROACT studies. Longitudinal associations between functional decline, muscle strength and survival with plasma creatinine were assessed. Results were translated to trial design in terms of sample size and power. Results A total of 13 564 measurements were obtained for 1241 patients. The variability between patients in rate of decline was lower in plasma creatinine than in ALS functional rating scale–Revised (ALSFRS-R; p<0.001). The average rate of decline was faster in the ALSFRS-R, with less between-patient variability at baseline (p<0.001). Plasma creatinine had strong longitudinal correlations with the ALSFRS-R (0.43 (0.39–0.46), p<0.001), muscle strength (0.55 (0.51–0.58), p<0.001) and overall mortality (HR 0.88 (0.86–0.91, p<0.001)). Using plasma creatinine as outcome could reduce the sample size in trials by 21.5% at 18 months. For trials up to 10 months, the ALSFRS-R required a lower sample size. Conclusions Plasma creatinine is an inexpensive and easily accessible biomarker that exhibits less variability between patients with ALS over time and is predictive for the patient’s functional status, muscle strength and mortality risk. Plasma creatinine may, therefore, increase the power to detect treatment effects and could be incorporated in future ALS clinical trials as potential surrogate outcome. PMID:29084868

Impact of non-uniform correlation structure on sample size and power in multiple-period cluster randomised trials.

PubMed

Kasza, J; Hemming, K; Hooper, R; Matthews, Jns; Forbes, A B

2017-01-01

Stepped wedge and cluster randomised crossover trials are examples of cluster randomised designs conducted over multiple time periods that are being used with increasing frequency in health research. Recent systematic reviews of both of these designs indicate that the within-cluster correlation is typically taken account of in the analysis of data using a random intercept mixed model, implying a constant correlation between any two individuals in the same cluster no matter how far apart in time they are measured: within-period and between-period intra-cluster correlations are assumed to be identical. Recently proposed extensions allow the within- and between-period intra-cluster correlations to differ, although these methods require that all between-period intra-cluster correlations are identical, which may not be appropriate in all situations. Motivated by a proposed intensive care cluster randomised trial, we propose an alternative correlation structure for repeated cross-sectional multiple-period cluster randomised trials in which the between-period intra-cluster correlation is allowed to decay depending on the distance between measurements. We present results for the variance of treatment effect estimators for varying amounts of decay, investigating the consequences of the variation in decay on sample size planning for stepped wedge, cluster crossover and multiple-period parallel-arm cluster randomised trials. We also investigate the impact of assuming constant between-period intra-cluster correlations instead of decaying between-period intra-cluster correlations. Our results indicate that in certain design configurations, including the one corresponding to the proposed trial, a correlation decay can have an important impact on variances of treatment effect estimators, and hence on sample size and power. An R Shiny app allows readers to interactively explore the impact of correlation decay.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Novel hybrid cryo‐radial method: an emerging alternative to CT‐guided biopsy in suspected lung cancer. A prospective case series and description of technique

PubMed Central

Yap, Elaine

2017-01-01

In diagnosing peripheral pulmonary lesions (PPL), radial endobronchial ultrasound (R‐EBUS) is emerging as a safer method in comparison to CT‐guided biopsy. Despite the better safety profile, the yield of R‐EBUS remains lower (73%) than CT‐guided biopsy (90%) due to the smaller size of samples. We adopted a hybrid method by adding cryobiopsy via the R‐EBUS Guide Sheath (GS) to produce larger, non‐crushed samples to improve diagnostic capability and enhance molecular testing. We report six prospective patients who underwent this procedure in our institution. R‐EBUS samples were obtained via conventional sampling methods (needle aspiration, forceps biopsy, and cytology brush), followed by a cryobiopsy. An endobronchial blocker was placed near the planned area of biopsy in advance and inflated post‐biopsy to minimize the risk of bleeding in all patients. A chest X‐ray was performed 1 h post‐procedure. All the PPLs were visualized with R‐EBUS. The mean diameter of cryobiopsy samples was twice the size of forceps biopsy samples. In four patients, cryobiopsy samples were superior in size and the number of malignant cells per high power filed and was the preferred sample selected for mutation analysis and molecular testing. There was no pneumothorax or significant bleeding to report. Cryobiopsy samples were consistently larger and were the preferred samples for molecular testing, with an increase in the diagnostic yield and reduction in the need for repeat procedures, without hindering the marked safety profile of R‐EBUS. Using an endobronchial blocker improves the safety of this procedure. PMID:29321931

Optimizing trial design in pharmacogenetics research: comparing a fixed parallel group, group sequential, and adaptive selection design on sample size requirements.

PubMed

Boessen, Ruud; van der Baan, Frederieke; Groenwold, Rolf; Egberts, Antoine; Klungel, Olaf; Grobbee, Diederick; Knol, Mirjam; Roes, Kit

2013-01-01

Two-stage clinical trial designs may be efficient in pharmacogenetics research when there is some but inconclusive evidence of effect modification by a genomic marker. Two-stage designs allow to stop early for efficacy or futility and can offer the additional opportunity to enrich the study population to a specific patient subgroup after an interim analysis. This study compared sample size requirements for fixed parallel group, group sequential, and adaptive selection designs with equal overall power and control of the family-wise type I error rate. The designs were evaluated across scenarios that defined the effect sizes in the marker positive and marker negative subgroups and the prevalence of marker positive patients in the overall study population. Effect sizes were chosen to reflect realistic planning scenarios, where at least some effect is present in the marker negative subgroup. In addition, scenarios were considered in which the assumed 'true' subgroup effects (i.e., the postulated effects) differed from those hypothesized at the planning stage. As expected, both two-stage designs generally required fewer patients than a fixed parallel group design, and the advantage increased as the difference between subgroups increased. The adaptive selection design added little further reduction in sample size, as compared with the group sequential design, when the postulated effect sizes were equal to those hypothesized at the planning stage. However, when the postulated effects deviated strongly in favor of enrichment, the comparative advantage of the adaptive selection design increased, which precisely reflects the adaptive nature of the design. Copyright © 2013 John Wiley & Sons, Ltd.

Evaluating the power to detect temporal trends in fishery-independent time surveys: A case study based on gill nets set in the Ohio waters of Lake Erie for walleyes

USGS Publications Warehouse

Wagner, Tyler; Vandergoot, Christopher S.; Tyson, Jeff

2011-01-01

Fishery-independent (FI) surveys provide critical information used for the sustainable management and conservation of fish populations. Because fisheries management often requires the effects of management actions to be evaluated and detected within a relatively short time frame, it is important that research be directed toward FI survey evaluation, especially with respect to the ability to detect temporal trends. Using annual FI gill-net survey data for Lake Erie walleyes Sander vitreus collected from 1978 to 2006 as a case study, our goals were to (1) highlight the usefulness of hierarchical models for estimating spatial and temporal sources of variation in catch per effort (CPE); (2) demonstrate how the resulting variance estimates can be used to examine the statistical power to detect temporal trends in CPE in relation to sample size, duration of sampling, and decisions regarding what data are most appropriate for analysis; and (3) discuss recommendations for evaluating FI surveys and analyzing the resulting data to support fisheries management. This case study illustrated that the statistical power to detect temporal trends was low over relatively short sampling periods (e.g., 5–10 years) unless the annual decline in CPE reached 10–20%. For example, if 50 sites were sampled each year, a 10% annual decline in CPE would not be detected with more than 0.80 power until 15 years of sampling, and a 5% annual decline would not be detected with more than 0.8 power for approximately 22 years. Because the evaluation of FI surveys is essential for ensuring that trends in fish populations can be detected over management-relevant time periods, we suggest using a meta-analysis–type approach across systems to quantify sources of spatial and temporal variation. This approach can be used to evaluate and identify sampling designs that increase the ability of managers to make inferences about trends in fish stocks.

Evaluating the power to detect temporal trends in fishery independent surveys: A case study based on Gillnets Set in the Ohio waters of Lake Erie for walleye

USGS Publications Warehouse

Wagner, Tyler; Vandergoot, Christopher S.; Tyson, Jeff

2009-01-01

Fishery-independent (FI) surveys provide critical information used for the sustainable management and conservation of fish populations. Because fisheries management often requires the effects of management actions to be evaluated and detected within a relatively short time frame, it is important that research be directed toward FI survey evaluation, especially with respect to the ability to detect temporal trends. Using annual FI gill-net survey data for Lake Erie walleyes Sander vitreus collected from 1978 to 2006 as a case study, our goals were to (1) highlight the usefulness of hierarchical models for estimating spatial and temporal sources of variation in catch per effort (CPE); (2) demonstrate how the resulting variance estimates can be used to examine the statistical power to detect temporal trends in CPE in relation to sample size, duration of sampling, and decisions regarding what data are most appropriate for analysis; and (3) discuss recommendations for evaluating FI surveys and analyzing the resulting data to support fisheries management. This case study illustrated that the statistical power to detect temporal trends was low over relatively short sampling periods (e.g., 5–10 years) unless the annual decline in CPE reached 10–20%. For example, if 50 sites were sampled each year, a 10% annual decline in CPE would not be detected with more than 0.80 power until 15 years of sampling, and a 5% annual decline would not be detected with more than 0.8 power for approximately 22 years. Because the evaluation of FI surveys is essential for ensuring that trends in fish populations can be detected over management-relevant time periods, we suggest using a meta-analysis–type approach across systems to quantify sources of spatial and temporal variation. This approach can be used to evaluate and identify sampling designs that increase the ability of managers to make inferences about trends in fish stocks.

NASA GRC and MSFC Space-Plasma Arc Testing Procedures

NASA Technical Reports Server (NTRS)

Ferguson, Dale C.; Vayner, Boris V.; Galofaro, Joel T,; Hillard, G. Barry; Vaughn, Jason; Schneider, Todd

2005-01-01

Tests of arcing and current collection in simulated space plasma conditions have been performed at the NASA Glenn Research Center (GRC) in Cleveland, Ohio, for over 30 years and at the Marshall Space Flight Center (MSFC) in Huntsville, Alabama, for almost as long. During this period, proper test conditions for accurate and meaningful space simulation have been worked out, comparisons with actual space performance in spaceflight tests and with real operational satellites have been made, and NASA has achieved our own internal standards for test protocols. It is the purpose of this paper to communicate the test conditions, test procedures, and types of analysis used at NASA GRC and MSFC to the space environmental testing community at large, to help with international space-plasma arcing-testing standardization. To be discussed are: 1.Neutral pressures, neutral gases, and vacuum chamber sizes. 2. Electron and ion densities, plasma uniformity, sample sizes, and Debuy lengths. 3. Biasing samples versus self-generated voltages. Floating samples versus grounded. 4. Power supplies and current limits. Isolation of samples from power supplies during arcs. 5. Arc circuits. Capacitance during biased arc-threshold tests. Capacitance during sustained arcing and damage tests. Arc detection. Prevention sustained discharges during testing. 6. Real array or structure samples versus idealized samples. 7. Validity of LEO tests for GEO samples. 8. Extracting arc threshold information from arc rate versus voltage tests. 9. Snapover and current collection at positive sample bias. Glows at positive bias. Kapon (R) pyrolisis. 10. Trigger arc thresholds. Sustained arc thresholds. Paschen discharge during sustained arcing. 11. Testing for Paschen discharge threshold. Testing for dielectric breakdown thresholds. Testing for tether arcing. 12. Testing in very dense plasmas (ie thruster plumes). 13. Arc mitigation strategies. Charging mitigation strategies. Models. 14. Analysis of test results. Finally, the necessity of testing will be emphasized, not to the exclusion of modeling, but as part of a complete strategy for determining when and if arcs will occur, and preventing them from occurring in space.

NASA GRC and MSFC Space-Plasma Arc Testing Procedures

NASA Technical Reports Server (NTRS)

Ferguson, Dale C.a; Vayner, Boris V.; Galofaro, Joel T.; Hillard, G. Barry; Vaughn, Jason; Schneider, Todd

2005-01-01

Tests of arcing and current collection in simulated space plasma conditions have been performed at the NASA Glenn Research Center (GRC) in Cleveland, Ohio, for over 30 years and at the Marshall Space flight Center (MSFC) for almost as long. During this period, proper test conditions for accurate and meaningful space simulation have been worked out, comparisons with actual space performance in spaceflight tests and with real operational satellites have been made, and NASA has achieved our own internal standards for test protocols. It is the purpose of this paper to communicate the test conditions, test procedures, and types of analysis used at NASA GRC and MSFC to the space environmental testing community at large, to help with international space-plasma arcing testing standardization. To be discussed are: 1. Neutral pressures, neutral gases, and vacuum chamber sizes. 2. Electron and ion densities, plasma uniformity, sample sizes, and Debye lengths. 3. Biasing samples versus self-generated voltages. Floating samples versus grounded. 4. Power supplies and current limits. Isolation of samples from power supplies during arcs. Arc circuits. Capacitance during biased arc-threshold tests. Capacitance during sustained arcing and damage tests. Arc detection. Preventing sustained discharges during testing. 5. Real array or structure samples versus idealized samples. 6. Validity of LEO tests for GEO samples. 7. Extracting arc threshold information from arc rate versus voltage tests. 8 . Snapover and current collection at positive sample bias. Glows at positive bias. Kapton pyrolization. 9. Trigger arc thresholds. Sustained arc thresholds. Paschen discharge during sustained arcing. 10. Testing for Paschen discharge thresholds. Testing for dielectric breakdown thresholds. Testing for tether arcing. 11. Testing in very dense plasmas (ie thruster plumes). 12. Arc mitigation strategies. Charging mitigation strategies. Models. 13. Analysis of test results. Finally, the necessity of testing will be emphasized, not to the exclusion of modeling, but as part of a complete strategy for determining when and if arcs will occur, and preventing them from occurring in space.

Merging National Forest and National Forest Health Inventories to Obtain an Integrated Forest Resource Inventory – Experiences from Bavaria, Slovenia and Sweden

PubMed Central

Kovač, Marko; Bauer, Arthur; Ståhl, Göran

2014-01-01

Backgrounds, Material and Methods To meet the demands of sustainable forest management and international commitments, European nations have designed a variety of forest-monitoring systems for specific needs. While the majority of countries are committed to independent, single-purpose inventorying, a minority of countries have merged their single-purpose forest inventory systems into integrated forest resource inventories. The statistical efficiencies of the Bavarian, Slovene and Swedish integrated forest resource inventory designs are investigated with the various statistical parameters of the variables of growing stock volume, shares of damaged trees, and deadwood volume. The parameters are derived by using the estimators for the given inventory designs. The required sample sizes are derived via the general formula for non-stratified independent samples and via statistical power analyses. The cost effectiveness of the designs is compared via two simple cost effectiveness ratios. Results In terms of precision, the most illustrative parameters of the variables are relative standard errors; their values range between 1% and 3% if the variables’ variations are low (s%<80%) and are higher in the case of higher variations. A comparison of the actual and required sample sizes shows that the actual sample sizes were deliberately set high to provide precise estimates for the majority of variables and strata. In turn, the successive inventories are statistically efficient, because they allow detecting the mean changes of variables with powers higher than 90%; the highest precision is attained for the changes of growing stock volume and the lowest for the changes of the shares of damaged trees. Two indicators of cost effectiveness also show that the time input spent for measuring one variable decreases with the complexity of inventories. Conclusion There is an increasing need for credible information on forest resources to be used for decision making and national and international policy making. Such information can be cost-efficiently provided through integrated forest resource inventories. PMID:24941120

A study on geometry effect of transmission coil for micro size magnetic induction coil

NASA Astrophysics Data System (ADS)

Lee, Kyung Hwa; Jun, Byoung Ok; Kim, Seunguk; Lee, Gwang Jun; Ryu, Mingyu; Choi, Ji-Woong; Jang, Jae Eun

2016-05-01

The effects of transmission (Tx) coil structure have been studied for micro-size magnetic induction coil. The size of the receiving (Rx) coil should be shrunk to the micrometer level for the various new applications such as micro-robot and wireless body implanted devices. In case of the macro-scale magnetic induction coil, the power transmission efficiency is generally considered to be higher as the inductance of the transmission coil became larger; however, the large size difference between macro-size Tx coil and micro-size Rx coil can decrease the power transmission efficiency due to the difference of resonance frequency. Here, we study a correlation of the power transmission with the size and distance between the macro-size Tx and micro-size Rx coils using magnetic induction technique. The maximum power efficiency was 0.28/0.23/0.13/0.12% at the distance of 0.3/1/3/5 cm between Rx and Tx coil. In addition, more efficient wireless power transferring method is suggested with a floating coil for the body implantable devices. The voltage output increased up to 5.4 mV than the original one Tx coil system. The results demonstrated the foundational wireless power transferring system with enhanced power efficiency.

GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment

PubMed Central

Rietveld, Cornelius A.; Medland, Sarah E.; Derringer, Jaime; Yang, Jian; Esko, Tõnu; Martin, Nicolas W.; Westra, Harm-Jan; Shakhbazov, Konstantin; Abdellaoui, Abdel; Agrawal, Arpana; Albrecht, Eva; Alizadeh, Behrooz Z.; Amin, Najaf; Barnard, John; Baumeister, Sebastian E.; Benke, Kelly S.; Bielak, Lawrence F.; Boatman, Jeffrey A.; Boyle, Patricia A.; Davies, Gail; de Leeuw, Christiaan; Eklund, Niina; Evans, Daniel S.; Ferhmann, Rudolf; Fischer, Krista; Gieger, Christian; Gjessing, Håkon K.; Hägg, Sara; Harris, Jennifer R.; Hayward, Caroline; Holzapfel, Christina; Ibrahim-Verbaas, Carla A.; Ingelsson, Erik; Jacobsson, Bo; Joshi, Peter K.; Jugessur, Astanand; Kaakinen, Marika; Kanoni, Stavroula; Karjalainen, Juha; Kolcic, Ivana; Kristiansson, Kati; Kutalik, Zoltán; Lahti, Jari; Lee, Sang H.; Lin, Peng; Lind, Penelope A.; Liu, Yongmei; Lohman, Kurt; Loitfelder, Marisa; McMahon, George; Vidal, Pedro Marques; Meirelles, Osorio; Milani, Lili; Myhre, Ronny; Nuotio, Marja-Liisa; Oldmeadow, Christopher J.; Petrovic, Katja E.; Peyrot, Wouter J.; Polašek, Ozren; Quaye, Lydia; Reinmaa, Eva; Rice, John P.; Rizzi, Thais S.; Schmidt, Helena; Schmidt, Reinhold; Smith, Albert V.; Smith, Jennifer A.; Tanaka, Toshiko; Terracciano, Antonio; van der Loos, Matthijs J.H.M.; Vitart, Veronique; Völzke, Henry; Wellmann, Jürgen; Yu, Lei; Zhao, Wei; Allik, Jüri; Attia, John R.; Bandinelli, Stefania; Bastardot, François; Beauchamp, Jonathan; Bennett, David A.; Berger, Klaus; Bierut, Laura J.; Boomsma, Dorret I.; Bültmann, Ute; Campbell, Harry; Chabris, Christopher F.; Cherkas, Lynn; Chung, Mina K.; Cucca, Francesco; de Andrade, Mariza; De Jager, Philip L.; De Neve, Jan-Emmanuel; Deary, Ian J.; Dedoussis, George V.; Deloukas, Panos; Dimitriou, Maria; Eiriksdottir, Gudny; Elderson, Martin F.; Eriksson, Johan G.; Evans, David M.; Faul, Jessica D.; Ferrucci, Luigi; Garcia, Melissa E.; Grönberg, Henrik; Gudnason, Vilmundur; Hall, Per; Harris, Juliette M.; Harris, Tamara B.; Hastie, Nicholas D.; Heath, Andrew C.; Hernandez, Dena G.; Hoffmann, Wolfgang; Hofman, Adriaan; Holle, Rolf; Holliday, Elizabeth G.; Hottenga, Jouke-Jan; Iacono, William G.; Illig, Thomas; Järvelin, Marjo-Riitta; Kähönen, Mika; Kaprio, Jaakko; Kirkpatrick, Robert M.; Kowgier, Matthew; Latvala, Antti; Launer, Lenore J.; Lawlor, Debbie A.; Lehtimäki, Terho; Li, Jingmei; Lichtenstein, Paul; Lichtner, Peter; Liewald, David C.; Madden, Pamela A.; Magnusson, Patrik K. E.; Mäkinen, Tomi E.; Masala, Marco; McGue, Matt; Metspalu, Andres; Mielck, Andreas; Miller, Michael B.; Montgomery, Grant W.; Mukherjee, Sutapa; Nyholt, Dale R.; Oostra, Ben A.; Palmer, Lyle J.; Palotie, Aarno; Penninx, Brenda; Perola, Markus; Peyser, Patricia A.; Preisig, Martin; Räikkönen, Katri; Raitakari, Olli T.; Realo, Anu; Ring, Susan M.; Ripatti, Samuli; Rivadeneira, Fernando; Rudan, Igor; Rustichini, Aldo; Salomaa, Veikko; Sarin, Antti-Pekka; Schlessinger, David; Scott, Rodney J.; Snieder, Harold; Pourcain, Beate St; Starr, John M.; Sul, Jae Hoon; Surakka, Ida; Svento, Rauli; Teumer, Alexander; Tiemeier, Henning; Rooij, Frank JAan; Van Wagoner, David R.; Vartiainen, Erkki; Viikari, Jorma; Vollenweider, Peter; Vonk, Judith M.; Waeber, Gérard; Weir, David R.; Wichmann, H.-Erich; Widen, Elisabeth; Willemsen, Gonneke; Wilson, James F.; Wright, Alan F.; Conley, Dalton; Davey-Smith, George; Franke, Lude; Groenen, Patrick J. F.; Hofman, Albert; Johannesson, Magnus; Kardia, Sharon L.R.; Krueger, Robert F.; Laibson, David; Martin, Nicholas G.; Meyer, Michelle N.; Posthuma, Danielle; Thurik, A. Roy; Timpson, Nicholas J.; Uitterlinden, André G.; van Duijn, Cornelia M.; Visscher, Peter M.; Benjamin, Daniel J.; Cesarini, David; Koellinger, Philipp D.

2013-01-01

A genome-wide association study of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent SNPs are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈ 2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics. PMID:23722424

The impact of the Sarbanes Oxley Act on auditing fees: An empirical study of the oil and gas industry

NASA Astrophysics Data System (ADS)

Ezelle, Ralph Wayne, Jr.

2011-12-01

This study examines auditing of energy firms prior and post Sarbanes Oxley Act of 2002. The research explores factors impacting the asset adjusted audit fee of oil and gas companies and specifically examines the effect of the Sarbanes Oxley Act. This research analyzes multiple year audit fees of the firms engaged in the oil and gas industry. Pooled samples were created to improve statistical power with sample sizes sufficient to test for medium and large effect size. The Sarbanes Oxley Act significantly increases a firm's asset adjusted audit fees. Additional findings are that part of the variance in audit fees was attributable to the market value of the enterprise, the number of subsidiaries, the receivables and inventory, debt ratio, non-profitability, and receipt of a going concern report.

Statistical considerations in monitoring birds over large areas

USGS Publications Warehouse

Johnson, D.H.

2000-01-01

The proper design of a monitoring effort depends primarily on the objectives desired, constrained by the resources available to conduct the work. Typically, managers have numerous objectives, such as determining abundance of the species, detecting changes in population size, evaluating responses to management activities, and assessing habitat associations. A design that is optimal for one objective will likely not be optimal for others. Careful consideration of the importance of the competing objectives may lead to a design that adequately addresses the priority concerns, although it may not be optimal for any individual objective. Poor design or inadequate sample sizes may result in such weak conclusions that the effort is wasted. Statistical expertise can be used at several stages, such as estimating power of certain hypothesis tests, but is perhaps most useful in fundamental considerations of describing objectives and designing sampling plans.

Powerful Identification of Cis-regulatory SNPs in Human Primary Monocytes Using Allele-Specific Gene Expression

PubMed Central

Almlöf, Jonas Carlsson; Lundmark, Per; Lundmark, Anders; Ge, Bing; Maouche, Seraya; Göring, Harald H. H.; Liljedahl, Ulrika; Enström, Camilla; Brocheton, Jessy; Proust, Carole; Godefroy, Tiphaine; Sambrook, Jennifer G.; Jolley, Jennifer; Crisp-Hihn, Abigail; Foad, Nicola; Lloyd-Jones, Heather; Stephens, Jonathan; Gwilliam, Rhian; Rice, Catherine M.; Hengstenberg, Christian; Samani, Nilesh J.; Erdmann, Jeanette; Schunkert, Heribert; Pastinen, Tomi; Deloukas, Panos; Goodall, Alison H.; Ouwehand, Willem H.; Cambien, François; Syvänen, Ann-Christine

2012-01-01

A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers. PMID:23300628

Developmental validation of the PowerPlex(®) ESI 16 and PowerPlex(®) ESI 17 Systems: STR multiplexes for the new European standard.

PubMed

Tucker, Valerie C; Hopwood, Andrew J; Sprecher, Cynthia J; McLaren, Robert S; Rabbach, Dawn R; Ensenberger, Martin G; Thompson, Jonelle M; Storts, Douglas R

2011-11-01

In response to the ENFSI and EDNAP groups' call for new STR multiplexes for Europe, Promega(®) developed a suite of four new DNA profiling kits. This paper describes the developmental validation study performed on the PowerPlex(®) ESI 16 (European Standard Investigator 16) and the PowerPlex(®) ESI 17 Systems. The PowerPlex(®) ESI 16 System combines the 11 loci compatible with the UK National DNA Database(®), contained within the AmpFlSTR(®) SGM Plus(®) PCR Amplification Kit, with five additional loci: D2S441, D10S1248, D22S1045, D1S1656 and D12S391. The multiplex was designed to reduce the amplicon size of the loci found in the AmpFlSTR(®) SGM Plus(®) kit. This design facilitates increased robustness and amplification success for the loci used in the national DNA databases created in many countries, when analyzing degraded DNA samples. The PowerPlex(®) ESI 17 System amplifies the same loci as the PowerPlex(®) ESI 16 System, but with the addition of a primer pair for the SE33 locus. Tests were designed to address the developmental validation guidelines issued by the Scientific Working Group on DNA Analysis Methods (SWGDAM), and those of the DNA Advisory Board (DAB). Samples processed include DNA mixtures, PCR reactions spiked with inhibitors, a sensitivity series, and 306 United Kingdom donor samples to determine concordance with data generated with the AmpFlSTR(®) SGM Plus(®) kit. Allele frequencies from 242 white Caucasian samples collected in the United Kingdom are also presented. The PowerPlex(®) ESI 16 and ESI 17 Systems are robust and sensitive tools, suitable for the analysis of forensic DNA samples. Full profiles were routinely observed with 62.5pg of a fully heterozygous single source DNA template. This high level of sensitivity was found to impact on mixture analyses, where 54-86% of unique minor contributor alleles were routinely observed in a 1:19 mixture ratio. Improved sensitivity combined with the robustness afforded by smaller amplicons has substantially improved the quantity of data obtained from degraded samples, and the improved chemistry confers exceptional tolerance to high levels of laboratory prepared inhibitors. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

A robust measure of HIV-1 population turnover within chronically infected individuals.

PubMed

Achaz, G; Palmer, S; Kearney, M; Maldarelli, F; Mellors, J W; Coffin, J M; Wakeley, J

2004-10-01

A simple nonparameteric test for population structure was applied to temporally spaced samples of HIV-1 sequences from the gag-pol region within two chronically infected individuals. The results show that temporal structure can be detected for samples separated by about 22 months or more. The performance of the method, which was originally proposed to detect geographic structure, was tested for temporally spaced samples using neutral coalescent simulations. Simulations showed that the method is robust to variation in samples sizes and mutation rates, to the presence/absence of recombination, and that the power to detect temporal structure is high. By comparing levels of temporal structure in simulations to the levels observed in real data, we estimate the effective intra-individual population size of HIV-1 to be between 10(3) and 10(4) viruses, which is in agreement with some previous estimates. Using this estimate and a simple measure of sequence diversity, we estimate an effective neutral mutation rate of about 5 x 10(-6) per site per generation in the gag-pol region. The definition and interpretation of estimates of such "effective" population parameters are discussed.

Ultrasound enhanced glucose release from corn in ethanol plants.

PubMed

Khanal, Samir Kumar; Montalbo, Melissa; van Leeuwen, J; Srinivasan, Gowrishankar; Grewell, David

2007-12-01

This work evaluated the use of high power ultrasonic energy to treat corn slurry in dry corn milling ethanol plants to enhance liquefaction and saccharification for ethanol production. Corn slurry samples obtained before and after jet cooking were subjected to ultrasonic pretreatment for 20 and 40 s at amplitudes of vibration ranging from 180 to 299 microm(pp) (peak to peak amplitude in microm). The resulting samples were then exposed to enzymes (alpha-amylase and glucoamylase) to convert cornstarch into glucose. A comparison of scanning electron micrographs of raw and sonicated samples showed the development of micropores and the disruption of cell walls in corn mash. The corn particle size declined nearly 20-fold following ultrasonic treatment at high power settings. The glucose release rate from sonicated samples increased as much as threefold compared to the control group. The efficiency of ultrasound exceeded 100% in terms of energy gain from the sugar released over the ultrasonic energy supplied. Enzymatic activity was enhanced when the corn slurry was sonicated with simultaneous addition of enzymes. This finding suggests that the ultrasonic energy did not degrade or denature the enzymes during the pretreatment.

Replicability and Robustness of GWAS for Behavioral Traits

PubMed Central

Rietveld, Cornelius A.; Conley, Dalton; Eriksson, Nicholas; Esko, Tõnu; Medland, Sarah E.; Vinkhuyzen, Anna A.E.; Yang, Jian; Boardman, Jason D.; Chabris, Christopher F.; Dawes, Christopher T.; Domingue, Benjamin W.; Hinds, David A.; Johannesson, Magnus; Kiefer, Amy K.; Laibson, David; Magnusson, Patrik K. E.; Mountain, Joanna L.; Oskarsson, Sven; Rostapshova, Olga; Teumer, Alexander; Tung, Joyce Y.; Visscher, Peter M.; Benjamin, Daniel J.; Cesarini, David; Koellinger, Philipp D.

2015-01-01

A recent genome-wide association study (GWAS) of educational attainment identified three single-nucleotide polymorphisms (SNPs) that, despite their small effect sizes (each R2 ≈ 0.02%), reached genome-wide significance (p < 5×10−8) in a large discovery sample and replicated in an independent sample (p < 0.05). The study also reported associations between educational attainment and indices of SNPs called “polygenic scores.” We evaluate the robustness of these findings. Study 1 finds that all three SNPs replicate in another large (N = 34,428) independent sample. We also find that the scores remain predictive (R2 ≈ 2%) with stringent controls for stratification (Study 2) and in new within-family analyses (Study 3). Our results show that large and therefore well-powered GWASs can identify replicable genetic associations with behavioral traits. The small effect sizes of individual SNPs are likely to be a major contributing explanation for the striking contrast between our results and the disappointing replication record of most candidate gene studies. PMID:25287667

The effect of ultrasonic irradiation on the structure, morphology and photocatalytic performance of ZnO nanoparticles by sol-gel method.

PubMed

Mahdavi, Reza; Ashraf Talesh, S Siamak

2017-11-01

In this research, the effect of ultrasonic irradiation power (0, 75, 150 and 200W) and time (0, 5, 15 and 20min) on the structure, morphology and photocatalytic activity of zinc oxide nanoparticles synthesized by sol-gel method was investigated. Crystallographic structures and the morphologies of the resultant powders were determined by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The XRD patterns showed that ZnO samples were crystallized in their pure phase. The purity of samples was increased by increasing the ultrasonic irradiation power and time. Not only did ultrasonic irradiation unify both the structure and the morphology, but also it reduced the size and prohibited particles from aggregation. The optical behavior of the samples was studied by UV-vis spectroscopy. Photocatalytic activity of particles was measured by degradation of methyl orange under radiation of ultraviolet light. Ultrasound nanoparticles represented higher degradation compared to non-ultrasound ones. Copyright © 2017 Elsevier B.V. All rights reserved.

Implications of Atmospheric Test Fallout Data for Nuclear Winter.

NASA Astrophysics Data System (ADS)

Baker, George Harold, III

1987-09-01

Atmospheric test fallout data have been used to determine admissable dust particle size distributions for nuclear winter studies. The research was originally motivated by extreme differences noted in the magnitude and longevity of dust effects predicted by particle size distributions routinely used in fallout predictions versus those used for nuclear winter studies. Three different sets of historical data have been analyzed: (1) Stratospheric burden of Strontium -90 and Tungsten-185, 1954-1967 (92 contributing events); (2) Continental U.S. Strontium-90 fallout through 1958 (75 contributing events); (3) Local Fallout from selected Nevada tests (16 events). The contribution of dust to possible long term climate effects following a nuclear exchange depends strongly on the particle size distribution. The distribution affects both the atmospheric residence time and optical depth. One dimensional models of stratospheric/tropospheric fallout removal were developed and used to identify optimum particle distributions. Results indicate that particle distributions which properly predict bulk stratospheric activity transfer tend to be somewhat smaller than number size distributions used in initial nuclear winter studies. In addition, both ^{90}Sr and ^ {185}W fallout behavior is better predicted by the lognormal distribution function than the prevalent power law hybrid function. It is shown that the power law behavior of particle samples may well be an aberration of gravitational cloud stratification. Results support the possible existence of two independent particle size distributions in clouds generated by surface or near surface bursts. One distribution governs late time stratospheric fallout, the other governs early time fallout. A bimodal lognormal distribution is proposed to describe the cloud particle population. The distribution predicts higher initial sunlight attenuation and lower late time attenuation than the power law hybrid function used in initial nuclear winter studies.

Influence of sputtering power on structural and magnetic properties of as-deposited, annealed and ERTA Co2FeSi films: A comparative study

NASA Astrophysics Data System (ADS)

Saravanan, L.; Raja, M. Manivel; Prabhu, D.; Therese, H. A.

2018-02-01

We report the effect of sputtering power (200 W - 350 W) on the structural, topographical and magnetic properties of Co2FeSi (CFS) films deposited at ambient temperatures as compared to the films which were either annealed at 300 °C or were subjected to Electron beam Rapid Thermal Annealed (ERTA) treatment. The structural and morphological analyses reveal changes in their crystalline phases and particle sizes. All the as-deposited and annealed CFS films showed A2 phase crystal structure. Whereas the CFS film sputtered at 350 W followed by ERTA displayed the fully ordered L21 structure. The particles are spherical in shape and their sizes increased gradually with increase in the sputtering power of the as-deposited and annealed CFS films. However, ERTA CFS films had spherical as well as columnar (elongated) shaped grains and their grain sizes increased nonlinearly with sputtering power. M-H studies on as-deposited, annealed and ERTA CFS films show ferromagnetic responses. The comparatively stronger ferromagnetic response was observed for the ERTA samples with low saturation field which depends on the enrichment of fine crystallites in these films. This indicates that, apart from higher sputtering powers used for deposition of CFS films, ERTA process plays a significant role in the enhancement of their magnetic responses. 350 W ERTA film has the considerable saturation magnetization (∼816 emu/cc), coercivity (∼527 Oe) and a good squareness values at 100 K than at 300 K, which could originate from the spin wave excitation effect. Further, the optimized parameters to achieve a CFS film with good structural and magnetic properties are discussed from the perspective of spintronics.