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Many research fields involve count data with zero inflation. A commonly chosen model for analysing a relationship between predictors and a response variable in these scenarios is a zero-inflated generalized linear model (GLM). This model is a mixture of a count-based GLM and a zero-inflation component, with a mixing proportion that determines the amount of excess zeroes. As the use of zero-inflated count models is rising, it is important to be able to conduct a power analysis to properly design studies with such models. In this paper, we propose a flexible method for power analysis with zero-inflated count models using Monte Carlo simulation. We have created the R package ZIPowerAnalysis, which can be used to easily conduct a power analysis for any designed study that will incorporate a zero-inflated count GLM. [ABSTRACT FROM AUTHOR]

Cluster-randomized trials have been widely used to evaluate the treatment effects of interventions on student outcomes. When interventions are implemented by teachers, researchers need to account for the nested structure in schools (i.e., students are nested within teachers nested within schools). Schools usually have a very limited number of teachers in each grade. This limited teacher-level sample size may pose potential challenges for study design, analysis, and reporting. The current study aims to evaluate the impacts of small teacher-level sample sizes on study design, analysis, and reporting in cluster-randomized trials. Through a simulation study, we show that the intraclass correlation coefficients at the teacher and school levels are prone to biased estimates in many cases, especially when the student-level sample size is also small. One design strategy we recommend is to sample a large number of students per teacher as this will alleviate the impacts of small teacher-level sample sizes. We recommend that researchers use the total variance, if available, rather than school-level variance to report standardized treatment effects for a meta-analysis. When the total variance is not available for all studies, researchers should use a school-level variance in two-level models as the base for standardization to reduce biases. [ABSTRACT FROM AUTHOR]

This work studies rollout design problems with a focus of suitable choices of rollout rate under the standard Type I and Type II error probabilities control framework. The main challenge of rollout design is that data is often observed in a lump-sum manner from a spatio-temporal point of view: (1) temporally, only the sum of data in a given sliding time window can be observed; (2) spatially, there are two subgroups for the data at each time step: control and treatment, but one can only observe the total values instead of individual values from each subgroup. We develop rollout tests of lump-sum data under both fixed-sample-size and sequential settings, subject to the constraints on Type I and Type II error probabilities. Numerical studies are conducted to validate our theoretical results. [ABSTRACT FROM AUTHOR]

AbstractWe make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. We also generalize the robust knockoffs inference in Barber, Candès, and Samworth to the time series setting to relax the assumption of known covariate distribution required by model-X knockoffs, since such an assumption is overly stringent for time series data. We establish sufficient conditions under which TSKI achieves the asymptotic false discovery rate (FDR) control. Our technical analysis reveals the effects of serial dependence and unknown covariate distribution on the FDR control. We conduct a power analysis of TSKI using the Lasso coefficient difference knockoff statistic under the generalized linear time series models. The finite-sample performance of TSKI is illustrated with several simulation examples and an economic inflation study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Freedom of Information (FOI) or Access to Information (ATI) legislation regulates the right to make written requests for government records. Previous research either interrogates the effectiveness of FOI/ATI legislation for advancing government transparency or positions the requests as a means for gathering data on government institutions. This article intervenes in this debate by treating FOI/ATI mechanisms as the vantage point from which to examine questions about power in public administration. Adopting a transformative approach, this article explores the potential of using FOI/ATI requests as a liberatory tool that enables the analysis of power dynamics in public administration. For this purpose, the author draws upon her experience with Canada's ATI regime and requests from the Immigration and Refugee Board. This article documents how ATI requests reveal policy tensions within the Board related to case and performance management, as well as patterns of front-line staff resistance to these measures. It goes on to examine complaints about delays made to the ATI watchdog which expose the attribution of ATI resources towards non-ATI responsibilities. This article highlights the contribution that FOI/ATI requests can make to public administration research. [ABSTRACT FROM AUTHOR]

There is increasing concern regarding the influence corporations have over the global food system. The extent of this power is so great that it is described as a defining feature of the current global food regime. In this paper, I explore the case study of regenerative agriculture, a growing transnational sustainable agriculture movement which has received increasing interest from corporate actors. Existing literature has highlighted the risks of corporate co-optation in regenerative agriculture but has failed to explore these patterns in any great depth. To address this gap, I analyse 21 multi-national corporations with regenerative agriculture programs and policies. Combining food regime theory with a three faces of power framework, I document the nature of corporate involvement in regenerative agriculture and how it reflects a broader attempt by agri-food corporations to legitimize their power in the global food system via the corporate environmental food regime. [ABSTRACT FROM AUTHOR]

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model. [ABSTRACT FROM AUTHOR]

The paper deals with the design issue of the computer simulation models of the traction inverter reflecting request on the rapid investigation of the efficiency performance under various operational conditions. According to this, a piece-wised linearized simulation tool PLECS is selected for model development, which considers the relevant loss behaviour of the main power circuit semiconductor components. The topologies, for which the simulation models are created, are topologies, one of which is commonly used across the automotive industry as the heart of the traction drive (VSI – Voltage Source Inverter), and its perspective alternative three-level traction inverter topology T-NPC – T-type Neutral Point Clamped. The simulation models created in the given simulation tool are thus subjected to the analysis of power parameters, which also include the calculated power losses of the entire system of semiconductor devices for both topologies of the traction inverter. Based on the obtained power parameters, the efficiency performance under various operational conditions of both topologies is evaluated. The presented methodology enables rapid investigation of operational performance enabling fast optimization methods. Any design of linearized simulation models in the PLECS simulation tool, whose core consists of semiconductor elements, can be analyzed by the proposed methodology. [ABSTRACT FROM AUTHOR]

The study delves into the operations of a Chinese volunteer tourism organization in Mathare, Kenya, a south-south context that has not been commonly explored in development practice. The first-hand data were collected during two fieldwork trips in 2018 and 2019, employing qualitative research methods such as participant observations and in-depth interviews. By adapting the 'powercube' theoretical framework, the research uncovers the presence of control and manipulation beneath the outward claims of empowerment made by the organization. The analysis reveals that the lack of organizational formality, with power being floated in many situations, has paved the way for both deliberate and unintentional forms of dominance in the realm of voluntary work. As a result, conflicts and disparities have arisen within the community. Recognizing it as a mode of development practice helps us to see new ways that power is articulated and shapes development interventions in African communities. [ABSTRACT FROM AUTHOR]

Because Null Hypothesis Significance Tests (NHST) can be misleading particularly when sample sizes are large, reporting effect sizes is recommended. Effect sizes such as Cohen's d, however, are abstract statistics and not relevant to decision-making. This article introduced an alternative formulation of Cohen's d based on a "minimal important percent difference" (MIPD) between groups that has an intuitive, practical meaning. Cohen's d was calculated based on detecting MIPDs of 5% and 10% differences in means. Power analyses were used to determine the number of responses needed to detect differences in means. Power-probability sample size requirements were examined for an 80% and a 50% chance of accepting a difference of MIPD at p <.05. We proposed a four-step process to show how both effect size and NHST jointly contribute to decision-making. To demonstrate the process, data were obtained for 6 years of annual waterfowl surveys in Illinois (N = 17,265, average response rate = 41%). [ABSTRACT FROM AUTHOR]

Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)k designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)k power calculator Shiny App () that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)k designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes. [ABSTRACT FROM AUTHOR]

State highway agencies (SHAs) use contractor test results in the acceptance and pay rewards of materials and construction. F- and t- tests are commonly used for validating contractor test results. However, these tests are based on sample statistics, which may lead to significant acceptance risks and inconsistent rewards. In this study a Monte Carlo simulation process was developed to systematically quantify the acceptance risks and assess the implications on pay factors (PF). The simulation was performed using typical acceptance quality characteristics (AQCs), such as thickness, for Portland cement concrete (PCC) pavements. The statistical power of the F- and t-test was determined. The analysis indicated that specific combinations of contractor and agency sample sizes and population characteristics have a greater impact on the acceptance risks and may provide inconsistent PF. Thus, the findings of this study can assist both agencies and producers to better assess the acceptance risks and rewards associated with the validation procedures. Therefore, the proposed methodology can be adopted by SHAs to develop statistically valid verification procedures and thus more rational quality assurance (QA). Producers may use such analysis to identify the level of risks and rewards associated with the current production and identify potential improvements in quality. [ABSTRACT FROM AUTHOR]

In this article, we propose new semiparametric and bootstrap tests based on the ratio of two variances under a density ratio model and compare them with existing tests for testing the equality of two variances from two independent populations. We showed that the ratio of two independent variances follows an asymptotic log-normal distribution. Also presented in this article are large sample asymptotic results of the semiparametric estimator of the ratio of the two sample variances. We also present a theoretical comparison of our semiparametric test on the ratio of two variances with the existing non parametric test using Pitman's relative efficiency. Numerical comparisons of the performance of various tests are performed via simulation studies. We also use two numerical examples to illustrate the implementation of new tests. [ABSTRACT FROM AUTHOR]

Researchers with mediation hypotheses must consider which design to use: within-subject or between-subject? In this paper, I argue that three factors should influence design choice: validity, causality, and statistical power. Threats to validity include carry-over effects, participant awareness, measurement, and more. Causality is a core element of mediation, and the assumptions required for causal inference differ between the two designs. Between-subject designs require more restrictive no-confounder assumptions, but within-subject designs require the assumption of no carry-over effects. Statistical power should be higher in within-subject designs, but the degree and conditions of this advantage are unknown for mediation analysis. A Monte Carlo simulation compares designs under a broad range of sample sizes, effect sizes, and correlations among repeated measurements. The results show within-subject designs require about half the sample size of between-subject designs to detect indirect effects of the same size, but this difference can vary with population parameters. I provide an empirical example and R script for conducting power analysis for within-subject mediation analysis. Researchers interested in conducting mediation analysis should not select within-subject designs merely because of higher power, but they should also consider validity and causality in their decision, both of which can favor between-subject designs. [ABSTRACT FROM AUTHOR]

Temperature data, like many other measurements in quantitative fields, are usually modelled using a normal distribution. However, some distributions can offer a better fit while avoiding underestimation of tail event probabilities. To this point, we extend Pearson's notions of skewness and kurtosis to build a powerful family of goodness-of-fit tests based on Rao's score for the exponential power distribution EPD λ (μ , σ) , including tests for normality and Laplacity when λ is set to 1 or 2. We find the asymptotic distribution of our test statistic, which is the sum of the squares of two Z-scores, under the null and under local alternatives. We also develop an innovative regression strategy to obtain Z-scores that are nearly independent and distributed as standard Gaussians, resulting in a χ 2 2 distribution valid for any sample size (up to very high precision for n ≥ 20). The case λ = 1 leads to a powerful test of fit for the Laplace( μ , σ) distribution, whose empirical power is superior to all 39 competitors in the literature, over a wide range of 400 alternatives. Theoretical proofs in this case are particularly challenging and substantial. We applied our tests to three temperature datasets. The new tests are implemented in the R package PoweR. [ABSTRACT FROM AUTHOR]

In this article, we propose a flexible weighted test of independence based on Cramér-von Mises statistic of the Bernstein empirical copula process. We investigate the asymptotic properties of the new weighted test. A Monte Carlo study is conducted to measure the power performance of the new test under various dependence structures and also to compare the performance with some other alternative tests. The effects of the weights and the selection of optimal Bernstein polynomial degree are significantly influencing the power of the test according to the particular type of dependence. Finally, the procedure is also applied to a dataset on diabetes for illustrative purpose. [ABSTRACT FROM AUTHOR]

The piecewise latent growth models (PWLGMs) can be used to study changes in the growth trajectory of an outcome due to an event or condition, such as exposure to an intervention. When there are multiple outcomes of interest, a researcher may choose to fit a series of PWLGMs or a single parallel-process PWLGM. A comparison of these models is provided with an illustrative example using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011), and a Monte Carlo simulation study. Conditions manipulated included sample size, location of the transition point, the number of time points, and covariate effect size. The results showed that the power to test parameter estimates with both models depended on the two-way interaction of sample size and covariate effect, and the three-way interaction of sample size by the number of time points by transition point location. Parameter coverage also depended on the three-way interaction of sample size by the number of time points by the transition point location. Recommendations for the use of the PWLGM are provided. [ABSTRACT FROM AUTHOR]

The transistor-based CMOS technology is facing tremendous physical challenges in nano-scale design. The Quantum-dot cellular automata (QCA) have attracted the research focus as aviable alternative to CMOS technology for future designs in the nano-scale. The cell interaction property helps in information propagation in QCA. The three-input majority gate, Inverter gate are the basic building blocks. The fabrication complexity can be optimised with coplanar and clock-based wire crossing techniques. Due to its low power feature, QCA has been experiencing significant power dissipation in its wire crossing and logic-driving cell, which needs special care. Moreover, various challenges to logic synthesis are associated with design automation and integration. Underlying metal wire crossing is another concern in the implementation of a regular clocking circuit. Extensive design proposals in QCA have been studied bypassing regular clocking and ends up with a question of proper fabrication or buildability. This research proposes an emerging technique to develop an efficient, scalable regular clocking scheme incorporating minimal metal wire crossing. The proposed clocking is employed for the cost-effective and power-efficient circuit design in both the sequential and the combinational approach. QCADesigner is used for circuit synthesis, whereas QCAPro and QCADesignerE have been utilised for energy dissipation analysis. [ABSTRACT FROM AUTHOR]

The problems of interval estimation of, and testing a hypothesis on the quantile θ = μ + η σ 1 (for given η) have been considered when independent random samples are available from two normal populations with a common mean μ and possibly unknown and unequal variances. The asymptotic confidence interval (ACI) for the quantile has been derived using the Fisher information matrix. Further, parametric bootstrap approaches such as boot-p, boot-t as well as the generalized p-value method have been adopted to obtain the confidence intervals numerically. For hypothesis testing several tests such as the one based on the Computational Approach Test (CAT), the likelihood ratio test (LRT), a test using an estimator of quantile, and tests based on generalized p-value approach have been proposed. Finally, the sizes (powers) of all the proposed tests have been computed using Monte-Carlo simulation procedure. Also the confidence intervals have been compared through average length (AL), coverage probability (CP), and a new measure called - the probability coverage density (PCD). [ABSTRACT FROM AUTHOR]

Optimal design of multisite randomized trials leverages sampling costs to optimize sampling ratios and ultimately identify more efficient and powerful designs. Past implementations of the optimal design framework have assumed that costs of sampling units are equal across treatment conditions. In this study, we developed a more flexible optimal design framework by introducing additional optimal design parameters that track sampling cost variation across levels and treatment conditions. The proposed framework can frequently suggest more powerful and efficient designs than those identified by conventional frameworks. Additionally, the more flexible framework allows researchers to place constraints for practical considerations. The proposed method has been implemented in the R package odr. [ABSTRACT FROM AUTHOR]

Optimal sampling frameworks attempt to identify the most efficient sampling plans to achieve an adequate statistical power. Although such calculations are theoretical in nature, they are critical to the judicious and wise use of funding because they serve as important starting points that guide practical discussions around sampling tradeoffs and requirements. Conventional optimal sampling frameworks, however, often identify sub-optimal designs because they typically presume the costs of sampling units are equal across treatment conditions. In this study, we develop a more flexible framework that allows costs to differ by treatment conditions and derive the optimal sample size formulas for three-level multisite cluster-randomized trials. We find that the proposed optimal sampling schemes are driven by the differences in costs between treatment conditions, cross-level sampling cost ratios and cross-level variance decomposition ratios. We illustrate the utility of the proposed framework by comparing it to a conventional framework and find that the proposed framework frequently identifies more efficient designs. The proposed optimal sampling framework has been implemented in the R package odr. [ABSTRACT FROM AUTHOR]

In this paper, we obtain an analytic and explicit estimator of the scale parameter using pivotal method and propose two new goodness of fit tests for the scale family based on general progressively Type-II censored samples. The properties about the test statistics are studied. Furthermore, we apply our methods to some specific life distributions for illustration. Moreover, we obtain critical values by simulations and compare the power of the two tests against various alternatives under many censoring schemes. We also obtain the empirical significance levels thorough Monte Carlo simulation. We do the comparison of the proposed estimator with the existing estimators given in the literature by means of a power study. The influence of the parameter in the alternative distribution to power is explored. The simulation results are presented using data visualization. We conclude that the estimator we derive and tests we propose perform well in many cases. Finally, a numerical example is presented. [ABSTRACT FROM AUTHOR]

Brown spot, caused by Septoria glycines Hemmi, is a highly prevalent foliar disease of soybean (Glycine max (L.) Merr.). Despite its wide distribution, the development of Septoria brown spot and its relationship with yield reduction remain poorly characterized. In this study, we conducted replicated multi-location inoculated field trials to characterize the disease development and evaluated the relationship between Septoria brown spot and soybean yield. Multiple components of disease and yield were rated weekly to characterize disease development. Fungicide treatments had a significant effect on ratings of vertical progress of the disease and chlorotic area at the end of the season. There were also significant differences between the fungicide treatments for the Area Under the Disease Progress Curve (AUDPC) of all the disease components, including necrotic area and defoliation rate. Soybean yield was negatively correlated with the vertical progress of the disease (r = −0.36). The vertical progress was the best linear predictor of yield with an R2 = 0.08 for the end of the season rating and an R2 = 0.2 for the AUDPC. A variance component analysis of the data showed that location was the most critical factor, illustrating the large effect of local environmental conditions on the disease. There was no statistically significant effect of the fungicide treatments on yield. Power analyses indicated that at least eight locations are needed to detect an effect of 269 kg ha−1. Our results provide useful information in the characterization of the disease development and for estimations of yield damage. [ABSTRACT FROM AUTHOR]

The Extended Bootstrap (EB) assessment approach was developed for the examination of relationships of Type I error, power, sample size (n), and effect size (ES) for statistical tests of ecological data. The EB approach was applied to univariate and multivariate statistical analyses of a large data set collected from an ongoing, multiple stressor bioassessment study of watersheds in the Central Valley, San Francisco, and Central Coast areas of California. Benthic metrics were created that either increased or decreased monotonically with stress (toxicants or metrics indicative of habitat quality). Type I errors were stable for all statistical tests that were evaluated. The relationships between n and ES displayed patterns of "diminishing returns" for all statistical tests: i.e. an increasingly larger n was required to detect decreasingly smaller ES. Nonetheless, the n's collected across the watersheds and within a selected watershed were sufficient to detect even small correlations between representative benthic metrics and potential stressors with high power. The power and robustness of a novel method using EB and previously described statistical techniques designed to address multicollinearity were shown to approach those of simpler univariate regressions. Potential applications of the EB approach for experimental design, data assessment and interpretation, and hypothesis testing are discussed. [ABSTRACT FROM AUTHOR]

This article is a primer for a decision-theoretic approach to hypothesis testing for students and teachers of basic statistics. Using three examples at an introductory level, this article demonstrates how decision-theoretic hypothesis testing can be taught to the students of basic statistics. It also demonstrates that students and researchers can make more sensible and unambiguous decisions under uncertainty by employing this particular approach. The examples are illustrated using R and its package "OptSig." [ABSTRACT FROM AUTHOR]

In a 2-step monotone missing dataset drawn from a multivariate normal population, T2-type test statistic (similar to Hotelling's T2 test statistic) and likelihood ratio (LR) are often used for the test for a mean vector. In complete data, Hotelling's T2 test and LR test are equivalent, however T2-type test and LR test are not equivalent in the 2-step monotone missing dataset. Then we interest which statistic is reasonable with relation to power. In this paper, we derive asymptotic power function of both statistics under a local alternative and obtain an explicit form for difference in asymptotic power function. Furthermore, under several parameter settings, we compare LR and T2-type test numerically by using difference in empirical power and in asymptotic power function. Summarizing obtained results, we recommend applying LR test for testing a mean vector. [ABSTRACT FROM AUTHOR]

The impacts of invasive house mice Mus musculus have received increasing attention on islands where mice are the only invasive rodent species. On Gough Island, the impact of mice on seabirds has increased over the past decade, but the current population status of the Critically Endangered Gough Finch Rowettia goughensis is uncertain. Based on nest monitoring at high elevation sites in 2009 (n = 37) and 2018 (n = 45), we found mean nest survival of 55% in both years and a fecundity of 1.31 ± 0.69 fledglings per pair in 2018. Density estimates from territory mapping in 2009 and 2018 were similar to past estimates and indicated little change in upland habitat. Density estimates from line transect distance sampling surveys around the island from 2018 to 2020 revealed higher densities in moorland (55.4 birds/km2) and wet heath (29.5 birds/km2) habitats, compared to coastal tussock (10.9 birds/km2) and lowland fern bush habitat (2.6 birds/km2). Extrapolating these habitat-specific densities across the island indicates a global population of Gough Finches in 2020 of 1917 individuals (95% CI: 1550–2500). Future population surveys using the same design could detect population changes of 40% or more. Our population estimate provides an important baseline for future monitoring following the planned eradication of house mice from Gough Island in 2021. However, we highlight that greater monitoring effort may be needed to increase the power to detect smaller population changes after an invasive species eradication. [ABSTRACT FROM AUTHOR]

Accurate power calculations are essential in small studies containing expensive experimental units or high-stakes exposures. Herein, power of the Wilcoxon Mann–Whitney rank-sum test of a continuous outcome is formulated using a Monte Carlo approach and defining P (X < Y) ≡ p as a measure of effect size, where X and Y denote random observations from two distributions hypothesized to be equal under the null. Effect size p fosters productive communications because researchers understand p = 0.5 is analogous to a fair coin toss, and p near 0 or 1 represents a large effect. This approach is feasible even without background data. Simulations were conducted comparing the empirical power approach to existing approaches by Rosner & Glynn, Shieh and colleagues, Noether, and O'Brien-Castelloe. Approximations by Noether and O'Brien-Castelloe are shown to be inaccurate for small sample sizes. The Rosner & Glynn and Shieh, Jan & Randles approaches performed well in many small sample scenarios, though both are restricted to location-shift alternatives and neither approach is theoretically justified for small samples. The empirical method is recommended and available in the R package wmwpow. [ABSTRACT FROM AUTHOR]

When planning mediation studies, researchers are often interested in the sample size needed to achieve adequate power for testing mediation. Power depends on population effect sizes, which are unknown in practice. In conventional power analysis, effect size estimates, however, are often used as population values, which could result in underpowered studies. Uncertainty in effect size estimates has been considered in other sample size planning contexts (e.g., t-test, ANOVA), but has not been handled properly for planning mediation studies. In the current study, we proposed an easy-to-use sample size planning method for testing mediation with uncertainty in effect size estimates considered. We conducted simulation studies to demonstrate the impact of uncertainty in effect size estimates on power of testing mediation, and to provide sample size suggestions under different levels of uncertainty. Empirical examples were provided to illustrate the application of our method. R functions and a web application were developed to facilitate implementation. [ABSTRACT FROM AUTHOR]

The effect of reference treatment over placebo, known as M1, is essential in the development of non-inferiority margin. We proposed a M1 adjustment approach to reduce the selection bias for collected data of historical trials. A quantitative illustration of selection bias of historical data is also defined. Simulation study shows that the proposed approaches would significantly reduce the bias when the proportion of positive studies in historical data is noticeably larger than the power of studies include in historical data. When historical data are constituted by only positive studies, the performance of the proposed method is also appreciable. However, when the proportion of positive studies is close to the power of studies included or the number of studies included is too small, the performance of the proposed approach may not be reliable. A real-data application is also presented. The proposed bias-adjustment approach is a reasonable method to reduce the over-estimate of effect size in the specification of non-inferiority margin. It could also be applied in most non-inferiority margin specification methods or be cooperate used with other bias-adjustment approaches. [ABSTRACT FROM AUTHOR]

Joint analysis of multiple phenotypes can increase statistical power in genetic association studies. Principal component analysis, as a popular dimension reduction method, especially when the number of phenotypes is high dimensional, has been proposed to analyze multiple correlated phenotypes. It has been empirically observed that the first PC, which summarizes the largest amount of variance, can be less powerful than higher-order PCs and other commonly used methods in detecting genetic association signals. In this article, we investigate the properties of PCA-based multiple phenotype analysis from a geometric perspective by introducing a novel concept called principal angle. A particular PC is powerful if its principal angle is and is powerless if its principal angle is. Without prior knowledge about the true principal angle, each PC can be powerless. We propose linear, nonlinear, and data-adaptive omnibus tests by combining PCs. We demonstrate that the Wald test is a special quadratic PC-based test. We show that the omnibus PC test is robust and powerful in a wide range of scenarios. We study the properties of the proposed methods using power analysis and eigen-analysis. The subtle differences and close connections between these combined PC methods are illustrated graphically in terms of their rejection boundaries. Our proposed tests have convex acceptance regions and hence are admissible. The p-values for the proposed tests can be efficiently calculated analytically and the proposed tests have been implemented in a publicly available R package MPAT. We conduct simulation studies in both low- and high-dimensional settings with various signal vectors and correlation structures. We apply the proposed tests to the joint analysis of metabolic syndrome-related phenotypes with datasets collected from four international consortia to demonstrate the effectiveness of the proposed combined PC testing procedures. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

The average environmental and occupational physiologist may find statistics are difficult to interpret and use since their formal training in statistics is limited. Unfortunately, poor statistical practices can generate erroneous or at least misleading results and distorts the evidence in the scientific literature. These problems are exacerbated when statistics are used as thoughtless ritual that is performed after the data are collected. The situation is worsened when statistics are then treated as strict judgements about the data (i.e., significant versus non-significant) without a thought given to how these statistics were calculated or their practical meaning. We propose that researchers should consider statistics at every step of the research process whether that be the designing of experiments, collecting data, analysing the data or disseminating the results. When statistics are considered as an integral part of the research process, from start to finish, several problematic practices can be mitigated. Further, proper practices in disseminating the results of a study can greatly improve the quality of the literature. Within this review, we have included a number of reminders and statistical questions researchers should answer throughout the scientific process. Rather than treat statistics as a strict rule following procedure we hope that readers will use this review to stimulate a discussion around their current practices and attempt to improve them. The code to reproduce all analyses and figures within the manuscript can be found at . [ABSTRACT FROM AUTHOR]

In clinical research, power analysis is often performed for sample size calculation. The purpose is to achieve a desired power of correctly detecting a clinically meaningful difference at a pre-specified level of significance if such a difference truly exists. However, in some situations such as (i) clinical trials with extremely low incidence rates and (ii) for rare disease drug development clinical trials, power analysis for sample size calculation may not be feasible because (i) it may require a huge sample size for detecting a relatively small difference and (ii) eligible patients may not be available for a small target patient population. In these cases, other procedures for sample size determination with certain statistical assurance are needed. In this article, an innovative method based on a probability monitoring procedure is proposed for sample size determination. The concept is to select an appropriate sample size for controlling the probability of crossing safety and/or efficacy boundaries. For rare disease clinical development, an adaptive probability monitoring procedure may be applied if a multiple-stage adaptive trial design is used. [ABSTRACT FROM AUTHOR]

The stepped-wedge design for pragmatic clinical trials has received increased attention in health service-related research seeking to evaluate the effect of interventions. Compared with the parallel design, the stepped-wedge design is preferred when there is prior knowledge supporting the effectiveness and harmlessness of the intervention, and/or when practical or financial constraints exist such that the intervention can only be implemented sequentially on a fraction of clusters. In some health service studies, the study period may consist of two parts: an active implementation followed by a sustainability phase, where the intervention effect is possibly reduced. There is a gap in current literature of the stepped-wedge design for cluster randomization trials for dealing with this specific scenario. We aim to provide an analytical formula for power analysis under this situation to aid the stepped-wedge design of an ongoing PREVENT trial. [ABSTRACT FROM AUTHOR]

The reporting of accurate and appropriate conclusions is an essential aspect of scientific research, and failure in this endeavor can threaten the progress of cumulative knowledge. This is highlighted by the current reproducibility crisis, and this crisis disproportionately affects fields that use behavioral research methods, as in much lighting research. A sample of general and topic-specific lighting research papers was reviewed for information about sample sizes and statistical reporting. This highlighted that lighting research is generally underpowered and, given median sample sizes, is unlikely to be able to reveal small effects. Lighting research most commonly uses parametric statistical tests, but assessment of test assumptions is rarely carried out. This risks the inappropriate use of statistical tests, potentially leading to type I and type II errors. Lighting research papers also rarely report measures of effect size, and this can hamper cumulative science and power analyses required to determine appropriate sample sizes for future research studies. Addressing the issues raised in this article related to sample sizes, statistical test assumptions, and reporting of effect sizes can improve the evidential value of lighting research. [ABSTRACT FROM AUTHOR]

In Peru, payment for ecosystem services is an increasingly popular mechanism to secure the transfer of water from rural to urban areas. This article analyzes the process of setting up such a scheme in the watersheds of Lima. The concept of hydrosocial territories and a power analysis are used to scrutinize how urban-based imaginaries and top-down approaches result in a disregard of local knowledge, rationalities, history of urban–rural relations and land ownership structures in the highlands. This could result in unintended outcomes of the scheme and in subordinating upstream communities to the city's needs. [ABSTRACT FROM AUTHOR]

Engineers use reliability experiments to determine the factors that drive product reliability, build robust products, and predict reliability under use conditions. This article uses recent testing of a howitzer to illustrate the challenges in designing reliability experiments for complex, repairable systems. We review research in complex system reliability models, failure-time experiments, and experimental design principles. We highlight the need for reliability experiments that account for various intended uses and environments. We leverage lessons learned from current research and propose methods for designing an experiment for a complex, repairable system. [ABSTRACT FROM AUTHOR]

Although the research methodology literature includes empirical benchmarks for effect sizes and intraclass correlations to help researchers determine adequate sample sizes through power analysis, it does not include similar benchmarks that would assist proper planning for attrition. To help fill this void, this paper describes how researchers can incorporate student attrition in power analyses and provides empirical benchmarks for the amount of attrition one might expect when conducting a school-based study that follows students over multiple years. The paper incorporates parameters for student attrition in common minimum detectable effect size calculations, presents attrition benchmarks based on student mobility rates in nationally representative longitudinal surveys, and presents benchmarks based on a synthesis of published evaluation studies. The paper includes a demonstration of how researchers can use the attrition benchmarks to take student attrition into account in a power analysis. [ABSTRACT FROM AUTHOR]

Soil sampling may be used as a decision-making tool for late-vegetative stage nitrogen (N) fertilizer applications in corn (Zea mays L.). Recommended sampling strategies following banded fertilizer applications commonly suggest taking cores from both on the fertilizer band (B) and off the band (O-B), however we hypothesized that soil nitrate concentrations (NO3−ppm) in the O-B were not influenced by N application rate. Analyzing samples from six experiments, we found there was a strong relationship between NO3−ppm and applied N rate in the B, but not the O-B position. Power analysis revealed that finding significant differences in applied N rates was only likely when sampling on the B and the difference in N rate was greater than 110 kg N ha−1. This demonstrates that soil N sampling is not sensitive to small differences in applied N, and that O-B soil cores may only dilute the ability to detect these differences. Abbreviations: B, on N fertilizer band; O-B, halfway between the corn row and the N fertilizer band; NO3−ppm, log-transformed nitrate-N concentration (ppm); NH4+ppm; ammonium-N concentration (ppm); D1, 0-30 cm depth; D2, 30-60 cm depth; C-220, contrast of N rates differing by 220 kg N ha−1; C-110, contrast of N rates differing by 110 kg N ha−1; C-55H, contrast of N rates differing by 55 kg N ha−1 at high N rates; C-55L, contrast of N rates differing by 55 kg N ha−1 at low N rates; A:N, ratio of non-transformed ammonium-N to nitrate-N concentrations; 0N, unfertilized treatment; CV, coefficient of variation; SE, standard error. [ABSTRACT FROM AUTHOR]

The “What If” analysis is applicablein research and heuristic situations that utilize statistical significance testing. One utility for the “What If” is in a pedagogical perspective; the “What If” analysis provides professors an interactive tool that visually represents examples of what statistical significance testing entails and the variables that affect the commonly misinterpreted pCALCULATEDvalue. In order to develop a strong understanding of what affects the pCALCULATEDvalue, the students tangibly manipulate data within the Excel sheet to create a visualize representation that explicitly demonstrates how variables affect the pCALCULATEDvalue. The second utility is primarily applicable to researchers. “What If” analysis contributes to research in two ways: (1) a “What If” analysis can be run a priori to estimate the sample size a researcher may wish to use for his study; and (2) a “What If” analysis can be run a posteriori to aid in the interpretation of results. If used, the “What If” analysis provides researchers with another utility that enables them to conduct high-quality research and disseminate their results in an accurate manner. [ABSTRACT FROM AUTHOR]

For many imperiled fishes in the Laurentian Great Lakes basin, detection protocols and population monitoring programs are lacking. In this study, we used a repeat-sampling approach to compare the effectiveness of two seines (bag and beach) to detect and characterize the abundance of lake-dwelling populations of Eastern Sand Darter (Ammocrypta pellucida); a threatened species in Canada. Compared to the bag seine, the larger beach seine collected a greater number of Eastern Sand Darter and detected the species at more sampling sites. Model-averaged estimates of detection probability were also greater for the beach seine (p= 0.72) than bag seine (p= 0.48). A decline in catch over repeated seine hauls occurred at less than a third of the sample units. Mean capture probabilities were 0.41 in units sampled by beach seine, and 0.37 in units sampled by bag seine, when depletion occurred. Sizes of Eastern Sand Darter collected by each seine were significantly different, with fewer small (<40 mm total length) individuals found in bag seine hauls. Power analyses indicate that data collected with either seine are expected to detect changes in local distribution of 50% or greater. Power to detect future changes in Eastern Sand Darter abundance of 50% or greater is predicted to be higher for the beach seine, and to increase with the number of seine hauls at a site. Catch differences between seines are interpreted to reflect the greater area sampled by the larger beach seine. [ABSTRACT FROM AUTHOR]

The latent Markov (LM) model is a popular method for identifying distinct unobserved states and transitions between these states over time in longitudinally observed responses. The bootstrap likelihood-ratio (BLR) test yields the most rigorous test for determining the number of latent states, yet little is known about power analysis for this test. Power could be computed as the proportion of the bootstrap p values (PBP) for which the null hypothesis is rejected. This requires performing the full bootstrap procedure for a large number of samples generated from the model under the alternative hypothesis, which is computationally infeasible in most situations. This article presents a computationally feasible shortcut method for power computation for the BLR test. The shortcut method involves the following simple steps: (1) obtaining the parameters of the model under the null hypothesis, (2) constructing the empirical distributions of the likelihood ratio under the null and alternative hypotheses via Monte Carlo simulations, and (3) using these empirical distributions to compute the power. We evaluate the performance of the shortcut method by comparing it to the PBP method and, moreover, show how the shortcut method can be used for sample-size determination. [ABSTRACT FROM PUBLISHER]

In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use observed effect sizes and prior distributions to obtain the posterior distribution of the effect size and model parameters. Then, we simulate effect sizes from the obtained posterior distribution. For each simulated effect size, we obtain a power value. With an estimated power distribution for a given sample size, we can estimate the probability of reaching a power level or higher and the expected power. With a range of planned sample sizes, we can generate a power assurance curve. Both the conventional frequentist and our Bayesian procedures were applied to conduct prospective power analyses for two meta-analysis examples (testing standardized mean differences in example 1 and Pearson's correlations in example 2). The advantages of our proposed procedure are demonstrated and discussed. [ABSTRACT FROM PUBLISHER]

Two methods for the power analysis of standing jumps are proposed and compared in this article. The first method is based on a simple analytical formulation which requires as input the coordinates of the center of gravity in three specified instants of the jump. The second method is based on a multibody model that simulates the jumps processing the data obtained by a three-dimensional (3D) motion capture system and the dynamometric measurements obtained by the force platforms. The multibody model is developed with OpenSim, an open-source software which provides tools for the kinematic and dynamic analyses of 3D human body models. The study is focused on two of the typical tests used to evaluate the muscular activity of lower limbs, which are the counter movement jump and the standing long jump. The comparison between the results obtained by the two methods confirms that the proposed analytical formulation is correct and represents a simple tool suitable for a preliminary analysis of total mechanical work and the mean power exerted in standing jumps. [ABSTRACT FROM AUTHOR]

Thematic analysis is frequently used to analyse qualitative data in psychology, healthcare, social research and beyond. An important stage in planning a study is determining how large a sample size may be required, however current guidelines for thematic analysis are varied, ranging from around 2 to over 400 and it is unclear how to choose a value from the space in between. Some guidance can also not be applied prospectively. This paper introduces a tool to help users think about what would be a useful sample size for their particular context when investigating patterns across participants. The calculation depends on (a) the expected population theme prevalence of the least prevalent theme, derived either from prior knowledge or based on the prevalence of the rarest themes considered worth uncovering, e.g. 1 in 10, 1 in 100; (b) the number of desired instances of the theme; and (c) the power of the study. An adequately powered study will have a high likelihood of finding sufficient themes of the desired prevalence. This calculation can then be used alongside other considerations. We illustrate how to use the method to calculate sample size before starting a study and achieved power given a sample size, providing tables of answers and code for use in the free software, R. Sample sizes are comparable to those found in the literature, for example to have 80% power to detect two instances of a theme with 10% prevalence, 29 participants are required. Increasing power, increasing the number of instances or decreasing prevalence increases the sample size needed. We do not propose this as a ritualistic requirement for study design, but rather as a pragmatic supporting tool to help plan studies using thematic analysis. [ABSTRACT FROM AUTHOR]

About a half century ago, in 1965, Warner proposed the randomized response method as a survey technique to reduce potential bias due to nonresponse and social desirability when asking questions about sensitive behaviors and beliefs. This method asks respondents to use a randomization device, such as a coin flip, whose outcome is unobserved by the interviewer. By introducing random noise, the method conceals individual responses and protects respondent privacy. While numerous methodological advances have been made, we find surprisingly few applications of this promising survey technique. In this article, we address this gap by (1) reviewing standard designs available to applied researchers, (2) developing various multivariate regression techniques for substantive analyses, (3) proposing power analyses to help improve research designs, (4) presenting new robust designs that are based on less stringent assumptions than those of the standard designs, and (5) making all described methods available through open-source software. We illustrate some of these methods with an original survey about militant groups in Nigeria. [ABSTRACT FROM PUBLISHER]

Latent curve models (LCMs) have been used extensively to analyze longitudinal data. However, little is known about the power of LCMs to detect nonlinear trends when they are present in the data. This simulation study was designed to investigate the Type I error rates, rates of nonconvergence, and the power of LCMs to detect piecewise linear growth and mean differences in the slopes of the 2 joined longitudinal processes represented by the piecewise model. The impact of 7 design factors was examined: number of time points, growth magnitude (slope mean), interindividual variability, sample size, position of the turning point, and the correlation of the intercept and the second slope as well between the 2 slopes. The results show that previous results based on linear LCMs cannot be fully generalized to a nonlinear model defined by 2 linear slopes. Interestingly, design factors specific to the piecewise context (position of the turning point and correlation between the 2 growth factors) had some effects on the results, but these effects remained minimal and much lower than the effects of other design factors. Similarly, observed rates of inadmissible solutions are comparable to those previously reported for linear LCMs. The major finding of this study is that a moderate sample size (N= 200) is needed to detect piecewise linear trajectories, but that much larger samples (N= 1,500) are required to achieve adequate statistical power to detect slope mean difference of small magnitude. [ABSTRACT FROM AUTHOR]

Medical and health policy decision-makers require improved design and analysis methods for comparative effectiveness research (CER) trials. In CER trials, there may be limited information to guide initial design choices. In general settings, adaptive designs (ADs) have effectively overcome limits on initial information. However, CER trials have fundamental differences from standard clinical trials including population heterogeneity and a vaguer concept of a “minimum clinically meaningful difference”. The objective of this article is to explore the use of a particular form of ADs for comparing treatments within the CER trial context. To achieve this, the authors review the current state of clinical CER. They also identify areas of CER as particularly strong candidates for application of novel AD and illustrate the potential usefulness of the designs and methods for two group comparisons. The authors found that ADs can stabilize power. Furthermore, the designs ensure adequate power for true effects are at least at clinically significant pre-planned effect size, or when variability is larger than expected. The designs allow for sample size savings when the true effect is larger or when variability is smaller than planned. The authors conclude that ADs in CER have great potential to allow trials to successfully and efficiently make important comparisons. [ABSTRACT FROM AUTHOR]

The main idea behind the proposed class of tests is rooted on an extension of the technique used in the derivation of the Mann–Whitney–Wilcoxon test. Just like the case of two-sample rank-based tests, the new class consists of tests defined through score functions. When properly selected, these score functions lead to consistent and often more powerful tests compared with classical goodness-of-fit tests. Theoretical results are supported by an extensive simulation study. [ABSTRACT FROM AUTHOR]

Kelley and Lai (2011) recently proposed the use of accuracy in parameter estimation (AIPE) for sample size planning in structural equation modeling. The sample size that reaches the desired width for the confidence interval of root mean square error of approximation (RMSEA) is suggested. This study proposes a graphical extension with the AIPE approach, abbreviated as GAIPE, on RMSEA to facilitate sample size planning in structural equation modeling. GAIPE simultaneously displays the expected width of a confidence interval of RMSEA, the necessary sample size to reach the desired width, and the RMSEA values covered in the confidence interval. Power analysis for hypothesis tests related to RMSEA can also be integrated into the GAIPE framework to allow for a concurrent consideration of accuracy in estimation and statistical power to plan sample sizes. A package written in R has been developed to implement GAIPE. Examples and instructions for using theGAIPEpackage are presented to help readers make use of this flexible framework. With the capacity of incorporating information on accuracy in RMSEA estimation, values of RMSEA, and power for hypothesis testing on RMSEA in a single graphical representation, the GAIPE extension offers an informative and practical approach for sample size planning in structural equation modeling. [ABSTRACT FROM PUBLISHER]