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ACADEMIC ABSTRACT: In the wake of the replication crisis, social and personality psychologists have increased attention to power analysis and the adequacy of sample sizes. In this article, we analyze current controversies in this area, including choosing effect sizes, why and whether power analyses should be conducted on already-collected data, how to mitigate the negative effects of sample size criteria on specific kinds of research, and which power criterion to use. For novel research questions, we advocate that researchers base sample sizes on effects that are likely to be cost-effective for other people to implement (in applied settings) or to study (in basic research settings), given the limitations of interest-based minimums or field-wide effect sizes. We discuss two alternatives to power analysis, precision analysis and sequential analysis, and end with recommendations for improving the practices of researchers, reviewers, and journal editors in social-personality psychology. PUBLIC ABSTRACT: Recently, social-personality psychology has been criticized for basing some of its conclusions on studies with low numbers of participants. As a result, power analysis, a mathematical way to ensure that a study has enough participants to reliably "detect" a given size of psychological effect, has become popular. This article describes power analysis and discusses some controversies about it, including how researchers should derive assumptions about effect size, and how the requirements of power analysis can be applied without harming research on hard-to-reach and marginalized communities. For novel research questions, we advocate that researchers base sample sizes on effects that are likely to be cost-effective for other people to implement (in applied settings) or to study (in basic research settings). We discuss two alternatives to power analysis, precision analysis and sequential analysis, and end with recommendations for improving the practices of researchers, reviewers, and journal editors in social-personality psychology.

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]

We point out potential drawbacks of some of Leising et al.'s (2022a) proposed ways how personality science can be improved. We argue that it is ill-advised to use only one measure for a concept. Also, we argue that researchers should not refrain from conducting a study when a high level of statistical power is precluded. Then, we go one step further and formulate additional ideas of how to improve research. Specifically, we argue that it is a good thing to use different methods rather than only one when attempting to generalize across these methods. Moreover, we argue for a more theory-driven strategy for specifying factor analytic models, and we emphasize that high-quality research is often interdisciplinary. Finally, we point to a particular risk associated with any formal reward system. [ABSTRACT FROM AUTHOR]

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis. [ABSTRACT FROM AUTHOR]

Landscape-scale monitoring is used to track changes in the status of fauna populations, including identifying when and where change is occurring over large areas. Yet few programs at this scale exist despite calls for long-term monitoring, especially after the 'black summer' fires of 2019/20. Landscape monitoring often sits in the 'too hard and too expensive basket'. To encourage land managers to maintain existing programs or establish new ones, it is important to demonstrate approaches that can cost-effectively monitor fauna and assess the level of investment required to adequately assess trends. We used passive acoustic and ultrasonic monitoring with artificial intelligence to semi-automate the detection of vocalisations from Koalas, Yellow-bellied Gliders, Powerful Owls and Sooty Owls and echolocation calls from two threatened bat species (Eastern Falsistrelle and Little Bent-wing Bat) as part of a pilot study for forest monitoring at a landscape-scale. Analyses that accounted for imperfect detection were used to: (1) generate detection probability and initial occupancy estimates for selected priority fauna species, and (2) undertake power analyses to inform sampling effort, and duration required to detect a commonly applied trend (30% decline over 10 years). Cost-effectiveness of sampling with remote sensors and traditional methods was also examined. Several thousand detections of priority species were recorded, with the number of detections varying by species and season. This translated to differences in detection probability and occupancy for each species. Detection probability was sensitive to the sampling intensity (number of surveys/sensors) deployed at a given site and this varied between seasons, with >95% probability of detection achieved for most species when multiple subplots were sampled for a 2-week period. Power analyses revealed trade-offs between sampling effort (number of sites) and duration (number of visits) required to detect a trend. Only a modest investment was needed to monitor widespread species that are moderately detectable using remote sensors with artificial intelligence and this investment was 2-11 times lower than monitoring using traditional methods for the priority species examined. For very rare species that are not widespread, localised monitoring and/or targeted research may be more suitable. Nevertheless, landscape-scale monitoring for many forest fauna is achievable using cost-effective remote sampling, artificial intelligence and robust analytics that account for imperfect detection. Now, more than ever, we urge support for and implementation of landscape monitoring for biodiversity. [ABSTRACT FROM AUTHOR]

Regulatory genetic toxicology testing is essential for identifying potentially mutagenic hazards. Duplex Sequencing (DS) is an error‐corrected next‐generation sequencing technology that provides substantial advantages for mutation analysis over conventional mutagenicity assays including: improved accuracy of mutation detection, ability to measure changes in mutation spectrum, and applicability across diverse biological models. To apply DS for regulatory toxicology testing, power analyses are required to determine suitable sample sizes and study designs. In this study, we explored study designs to achieve sufficient power for various effect sizes in chemical mutagenicity assessment. We collected data from MutaMouse bone marrow and liver samples that were analyzed by DS using TwinStrand's Mouse Mutagenesis Panel. Average duplex reads achieved in two separates studies on liver and bone marrow were 8.4 × 108 (± 7.4 × 107) and 9.5 × 108 (± 1.0 × 108), respectively. Baseline mean mutation frequencies (MF) were 4.6 × 10−8 (± 6.7 × 10−9) and 4.6 × 10−8 (± 1.1 × 10−8), with estimated standard deviations for the animal‐to‐animal random effect of 0.15 and 0.20, for liver and bone marrow, respectively. We conducted simulation analyses based on these empirically derived parameters. We found that a sample size of four animals per group is sufficient to obtain over 80% power to detect a two‐fold change in MF relative to baseline. In addition, we estimated the minimal total number of informative duplex bases sequenced with different sample sizes required to retain power for various effect sizes. Our work provides foundational data for establishing suitable study designs for mutagenicity testing using DS. [ABSTRACT FROM AUTHOR]

The cost-effectiveness of different attractants during camera trapping surveys has been seldom evaluated. To contribute in filling this knowledge gap we (1) compare the effectiveness of a suite of attractants in detecting widely distributed mammals in Europe and (2) evaluate the cost-effectiveness of these attractants, by calculating the costs associated to reach a specific monitoring objective. We conducted a large-scale field experiment across four study areas in central and northern Italy, encompassing a variety of environments, from lowland forest to alpine beech forest. We focused on comparing the following low cost and readily available attractants: sardines, peanut butter, a commercial lure and we used a camera with no attractant as control, collecting data on a suite of small to large mammals. We found that for seven of our 13 target species detectability varied with the type of attractant used. Specifically, sardines proved to be the most effective attractant for canids and the porcupine, peanut butter was most effective for mustelids but was avoided by the roe deer, whereas the commercial lure was the most effective with red deer. Through a power analysis combined with a cost function analysis we were able to show striking differences in the cost-effectiveness of the different methods, sometimes in the order of magnitude of tens of thousands of euros, which strongly emphasizes the critical importance played by the choice of whether to use an attractant or not and the type of attractant to be used. [ABSTRACT FROM AUTHOR]

Uncertainty, complexity, and dynamic changes present challenges for conservation and natural resource management. Evidence-based approaches grounded in reliable information and rigorous analysis can enhance the navigation of the uncertainties and trade-offs inherent in conservation problems. This study highlights the importance of collaborative efforts and evidence-based decision-making, specifically implementing the Resilience-Experimentalist school of adaptive management (RE-ARM), which emphasizes stakeholder involvement, shared understanding, and experimentation. Our goal was to develop an adaptive management framework to reduce the uncertainty around the use of prescribed fire to manage the habitat for eastern black rails (Laterallus jamaicensis jamaicensis) and mottled ducks (Anas fulvigula) in saltmarshes of the Gulf of Mexico. Supported by discussions at a series of workshops, we used a value of information analysis to select a fire management hypothesis to test, developed an influence diagram to represent the system under fire management, used the influence diagram to develop a Bayesian decision network (BDN), and conducted a power analysis to guide management experiments and monitoring. Value of information analysis identified fire return interval as the critical uncertainty. Our BDN provided valuable insight into how managers believe prescribed fire influences vegetation characteristics and how vegetation influences both eastern black rail occupancy and mottled duck abundance. The results of the power analysis indicated that a standard occupancy modeling framework was more useful to compare 2- and 5-year fire return intervals for black rails than two alternative designs (removal and conditional). Our BDN can be used to predict the probability of achieving the desirable vegetative response to increase the occupancy probability of black rails and abundance of mottled ducks, and monitoring data can be used to update the BDN (learn) and improve best management practices for prescribed burns (adapt). Linking the value of information, BDNs, and power analysis enhances our understanding of the system, improves management decision-making, and builds trust among scientists, interested parties, and decision-makers. This approach lays the groundwork for knowledge co-production and adaptive management. [ABSTRACT FROM AUTHOR]

Background: The use of corporate power to undermine public health policy processes is increasingly well understood; however, relatively little scholarship examines how advocates can leverage power to promote the successful adoption of public health policies. The objective of this paper is to explore how advocates leveraged three forms of power – structural, instrumental and discursive – to promote the passage of the Promotion of Healthy Eating Law (Ley 27,642) in Argentina, one of the most comprehensive policies to introduce mandatory front-of-package (FOP) warning labels and regulate the marketing and sales of ultra-processed foods (UPFs) adopted to date. Methods: We conducted seventeen semi-structured interviews with advocates from different sectors, including civil society, international agencies, and government. Both data collection and analysis were guided by Milsom's conceptual framework for analyzing power in public health policymaking, and the data was analyzed using hybrid deductive and inductive thematic analysis. Results: Advocates harnessed structural power through the leveraging of revolving doors, informal alliances, and formal coalitions, enabling them to convene discussion spaces with decision-makers, make strategic use of limited resources, and cultivate the diverse expertise (e.g., research, nutrition science, advocacy, law, political science, activism and communications) needed to support the law through different phases of the policy process. Advocates wielded instrumental power by amassing an armada of localized evidence to promote robust policy design, building technical literacy amongst themselves and decision-makers, and exposing conflicts of interest to harness public pressure. Advocates exercised discursive power by adopting a rights-based discourse, including of children and adolescents and of consumers to transparent information, which enabled advocates to foster a favorable perception of the law amongst both decision-makers and the public. Key contextual enablers include a political window of opportunity, the COVID-19 pandemic, and the ability to learn from the regional precedent of similar policies. Conclusions: Public health policymaking, particularly when encroaching upon corporate interests, is characterized by stark imbalances of power that hinder policy decisions. The strategies identified in the case of Argentina provide important insights as to how advocates might harness and exercise structural, instrumental, and discursive power to counter corporate influence and promote the successful adoption of comprehensive UPF regulation. [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]

Distance covariance is a popular dependence measure for two random vectors |$X$| and |$Y$| of possibly different dimensions and types. Recent years have witnessed concentrated efforts in the literature to understand the distributional properties of the sample distance covariance in a high-dimensional setting, with an exclusive emphasis on the null case that |$X$| and |$Y$| are independent. This paper derives the first non-null central limit theorem for the sample distance covariance, and the more general sample (Hilbert–Schmidt) kernel distance covariance in high dimensions, in the distributional class of |$(X,Y)$| with a separable covariance structure. The new non-null central limit theorem yields an asymptotically exact first-order power formula for the widely used generalized kernel distance correlation test of independence between |$X$| and |$Y$|⁠. The power formula in particular unveils an interesting universality phenomenon: the power of the generalized kernel distance correlation test is completely determined by |$n\cdot \operatorname{dCor}^{2}(X,Y)/\sqrt{2}$| in the high-dimensional limit, regardless of a wide range of choices of the kernels and bandwidth parameters. Furthermore, this separation rate is also shown to be optimal in a minimax sense. The key step in the proof of the non-null central limit theorem is a precise expansion of the mean and variance of the sample distance covariance in high dimensions, which shows, among other things, that the non-null Gaussian approximation of the sample distance covariance involves a rather subtle interplay between the dimension-to-sample ratio and the dependence between |$X$| and |$Y$|⁠. [ABSTRACT FROM AUTHOR]

Abstract Background The use of corporate power to undermine public health policy processes is increasingly well understood; however, relatively little scholarship examines how advocates can leverage power to promote the successful adoption of public health policies. The objective of this paper is to explore how advocates leveraged three forms of power – structural, instrumental and discursive – to promote the passage of the Promotion of Healthy Eating Law (Ley 27,642) in Argentina, one of the most comprehensive policies to introduce mandatory front-of-package (FOP) warning labels and regulate the marketing and sales of ultra-processed foods (UPFs) adopted to date. Methods We conducted seventeen semi-structured interviews with advocates from different sectors, including civil society, international agencies, and government. Both data collection and analysis were guided by Milsom’s conceptual framework for analyzing power in public health policymaking, and the data was analyzed using hybrid deductive and inductive thematic analysis. Results Advocates harnessed structural power through the leveraging of revolving doors, informal alliances, and formal coalitions, enabling them to convene discussion spaces with decision-makers, make strategic use of limited resources, and cultivate the diverse expertise (e.g., research, nutrition science, advocacy, law, political science, activism and communications) needed to support the law through different phases of the policy process. Advocates wielded instrumental power by amassing an armada of localized evidence to promote robust policy design, building technical literacy amongst themselves and decision-makers, and exposing conflicts of interest to harness public pressure. Advocates exercised discursive power by adopting a rights-based discourse, including of children and adolescents and of consumers to transparent information, which enabled advocates to foster a favorable perception of the law amongst both decision-makers and the public. Key contextual enablers include a political window of opportunity, the COVID-19 pandemic, and the ability to learn from the regional precedent of similar policies. Conclusions Public health policymaking, particularly when encroaching upon corporate interests, is characterized by stark imbalances of power that hinder policy decisions. The strategies identified in the case of Argentina provide important insights as to how advocates might harness and exercise structural, instrumental, and discursive power to counter corporate influence and promote the successful adoption of comprehensive UPF regulation.

Ordinal data such as Likert items, ratings or generic ordered variables are widespread in psychology. These variables are usually analysed using metric models (e.g., standard linear regression) with important drawbacks in terms of statistical inference (reduced power and increased type‐1 error) and prediction. One possible reason for not using ordinal regression models could be difficulty in understanding parameters or conducting a power analysis. The tutorial aims to present ordinal regression models using a simulation‐based approach. Firstly, we introduced the general model highlighting crucial components and assumptions. Then, we explained how to interpret parameters for a logit and probit model. Then we proposed two ways for simulating data as a function of predictors showing a 2 × 2 interaction with categorical predictors and the interaction between a numeric and categorical predictor. Finally, we showed an example of power analysis using simulations that can be easily extended to complex models with multiple predictors. The tutorial is supported by a collection of custom R functions developed to simulate and understand ordinal regression models. The code to reproduce the proposed simulation, the custom R functions and additional examples of ordinal regression models can be found on the online Open Science Framework repository (https://osf.io/93h5j). [ABSTRACT FROM AUTHOR]

Well-chosen covariates boost the design sensitivity of individually and cluster-randomized trials. We provide guidance on covariate selection generating an extensive compilation of single- and multilevel design parameters on student achievement. Embedded in psychometric heuristics, we analyzed (a) covariate types of varying bandwidth-fidelity, namely domain-identical (IP), cross-domain (CP), and fluid intelligence (Gf) pretests, as well as sociodemographic characteristics (SC); (b) covariate combinations quantifying incremental validities of CP, Gf, and/or SC beyond IP; and (c) covariate time lags of 1–7 years, testing validity degradation in IP, CP, and Gf. Estimates from six German samples (1868 ≤ N ≤ 10,543) covering various outcome domains across grades 1–12 were meta-analyzed and included in precision simulations. Results varied widely by grade level, domain, and hierarchical level. In general, IP outperformed CP, which slightly outperformed Gf and SC. Benefits from coupling IP with CP, Gf, and/or SC were small. IP appeared most affected by temporal validity decay. Findings are applied in illustrative scenarios of study planning and enriched by comprehensive Online Supplemental Material (OSM) accessible via the Open Science Framework (OSF; ). [ABSTRACT FROM AUTHOR]

The recently proposed fixed-X knockoff is a powerful variable selection procedure that controls the false discovery rate (FDR) in any finite-sample setting, yet its theoretical insights are difficult to show beyond Gaussian linear models. In this paper, we make the first attempt to extend the fixed-X knockoff to partially linear models by using generalized knockoff features, and propose a new stability generalized knockoff (Stab-GKnock) procedure by incorporating selection probability as feature importance score. We provide FDR control and power guarantee under some regularity conditions. In addition, we propose a two-stage method under high dimensionality by introducing a new joint feature screening procedure, with guaranteed sure screening property. Extensive simulation studies are conducted to evaluate the finite-sample performance of the proposed method. A real data example is also provided for illustration. [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.

A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling. [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]

Background: The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers. Methods: We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented. Results: We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented. Conclusion: This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials. [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]

Cluster-randomized trials (CRTs) often allocate intact clusters of participants to treatment or control conditions and are increasingly used to evaluate healthcare delivery interventions. While previous studies have developed sample size methods for testing confirmatory hypotheses of treatment effect heterogeneity in CRTs (i.e., targeting the difference between subgroup-specific treatment effects), sample size methods for testing the subgroup-specific treatment effects themselves have not received adequate attention—despite a rising interest in health equity considerations in CRTs. In this article, we develop formal methods for sample size and power analyses for testing subgroup-specific treatment effects in parallel-arm CRTs with a continuous outcome and a binary subgroup variable. We point out that the variances of the subgroup-specific treatment effect estimators and their covariance are given by weighted averages of the variance of the overall average treatment effect estimator and the variance of the heterogeneous treatment effect estimator. This analytical insight facilitates an explicit characterization of the requirements for both the omnibus test and the intersection–union test to achieve the desired level of power. Generalizations to allow for subgroup-specific variance structures are also discussed. We report on a simulation study to validate the proposed sample size methods and demonstrate that the empirical power corresponds well with the predicted power for both tests. The design and setting of the Umea Dementia and Exercise (UMDEX) CRT in older adults are used to illustrate our sample size methods. [ABSTRACT FROM AUTHOR]

Quantifying the cost‐effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost‐effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single‐season occupancy‐detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1–200) and repeat surveys (2–40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24‐h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25‐ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24‐h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time. [ABSTRACT FROM AUTHOR]

Population genetics is a valuable tool for assessing the impact of human‐altered landscapes on genetic connectivity in various species. However, when applied to insects, challenges arise due to potentially large effective population sizes (Ne), high dispersal capacities and the recency of anthropogenic impacts.This study assessed the population genetic structure of two pollinators across a human‐altered landscape in Luxembourg. Samples from the ashy mining bee (Andrena cineraria, N = 201) and the greater bee fly (Bombylius major, N = 637) were genotyped at 25 microsatellite loci, including a genotyping‐by‐amplicon‐sequencing approach for A. cineraria.Despite high statistical power of FST > 0.002 in B. major and FST > 0.0025 in A. cineraria, no deviations from genetic homogeneity were detected. For both species, there was no evidence for isolation‐by‐distance or genetic clustering. Genetic homogeneity was most likely the result of high levels of gene flow that compensate for the effects of genetic drift.Estimates of Ne ranged between several thousand to tens of thousands, although precision was low. Simulations highlighted that genetic lag times can substantially affect our ability to detect recent (<50 generations) population differentiation when Ne is very large. Lag times were shorter with data from 25,000 simulated di‐allelic loci, but only when sample sizes remained high.Insect genetic studies should consider lag times due to large Ne and ensure sample size and markers offer adequate power to reject the null hypothesis of no landscape effect on genetic connectivity. [ABSTRACT FROM AUTHOR]

The decision to treat children with benign epilepsy with centrotemporal spikes (BECTS) using antiseizure medications (ASM) is controversial. Our goal is to compare the effect of ASM treatment on the alteration of electroencephalographic (EEG) functional connectivity and power across four frequency bands in children with BECTS. Children with BECTS with two-year follow-up were retrospectively divided into ASM versus non-ASM groups. The network properties of the EEGs as based on network-based statistic and graph theory were evaluated by the following indices: global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands (delta, theta, alpha, and beta). EEG power including absolute power (AP) and relative power (RP) was analyzed in four frequency bands. In children with BECTS with ASM treatment, there was no significant change in EEG connectivity across all bands before and after two years of ASM. In children with BECTS without ASM treatment, there was a significant increase of global efficiency, clustering coefficient, and nodal strength but not the betweenness centrality in the delta band after two years of follow-up. A decrease in AP in the delta and theta bands and a decrease in RP in the theta band were found in the ASM group after two years of treatment. Our results suggest that ASM may play a role in modulating the development of increasing overall brain connectivity and in downregulating overt synaptic activity, but not intrinsic focal connectivity, in the early years of BECTS. The changes in the EEG power indicate that ASM significantly normalized slow-wave band power. [ABSTRACT FROM AUTHOR]

Estimating sample size and statistical power is an essential part of a good epidemiological study design. Closed-form formulas exist for simple hypothesis tests but not for advanced statistical methods designed for exposure mixture studies. Estimating power with Monte Carlo simulations is flexible and applicable to these methods. However, it is not straightforward to code a simulation for non-experienced programmers and is often hard for a researcher to manually specify multivariate associations among exposure mixtures to set up a simulation. To simplify this process, we present the R package mpower for power analysis of observational studies of environmental exposure mixtures involving recently developed mixtures analysis methods. The components within mpower are also versatile enough to accommodate any mixtures methods that will be developed in future. The package allows users to simulate realistic exposure data and mixed-typed covariates based on public dataset such as the National Health and Nutrition Examination Survey or other existing dataset from prior studies. Users can generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This paper presents tutorials and examples of power analysis using mpower. [ABSTRACT FROM AUTHOR]

Identifying significant variables for the high-dimensional logistic regression model is a fundamental problem in modern statistics and machine learning. This paper introduces a stability knockoffs (StabKoff) selection procedure by merging stability selection and knockoffs to conduct controlled variable selection for logistic regression. Under some regularity conditions, we show that the proposed method achieves FDR control under the finite-sample setting, and the power also asymptotically approaches one as the sample size tends to infinity. In addition, we further develop an intersection strategy that allows better separation of knockoff statistics between significant and unimportant variables, which in some cases leads to an increase in power. The simulation studies demonstrate that the proposed method possesses satisfactory finite-sample performance compared with existing methods in terms of both FDR and power. We also apply the proposed method to a real data set on opioid use disorder treatment. [ABSTRACT FROM AUTHOR]

Context: Ecological surveillance monitoring typically targets multiple taxonomic groups by using standardised sampling across large spatial scales. Although surveillance monitoring confers advantages over hypothesis-driven monitoring in its broad taxonomic and spatial scope, the approach has been criticised for its disconnect from ecological management and failure to provide insights on the drivers of ecological change Aims: To assess the adequacy of a plot-based general fauna-monitoring program for sampling reptiles as indicators of ecosystem health in a semi-arid upland region of the Northern Territory, Australia. Methods: We surveyed reptiles at 90 sites, stratified between major landform and vegetation types, and using standard fauna-sampling methods, across the 2568 km2 Tjoritja National Park in the MacDonnell Ranges. We compiled a full inventory of the reptile fauna of the study area and identified species with potential utility as ecological indicators. We then used single-season occupancy models and power analyses to evaluate the adequacy of sampling for detecting potential future changes in occupancy. Key results: We detected 57 of the 68 reptile species known from the protected area, 17 of which are potentially useful indicators of ecological health, mostly related to fire management. There was insufficient power to detect moderate (50%) future changes in reptile occupancy for all but the single most detected species. For the two ecological indicator species with sufficient detections for occupancy modelling, a positive association with a keystone structure (dense spinifex grass) was confirmed. However, increasing detection probability or the number of surveys would result in only minor improvements in power to detect occupancy change in these species. Conclusions: Although reptiles are potentially useful indicators of ecological health, particularly in relation to fire regimes, the number of sites required to detect future changes in reptile occupancy by using standardised plot-based monitoring in this protected area is prohibitively high. Implications: Our results suggest that once ecological associations are understood, monitoring ecological health remotely by using techniques such as fire-scar mapping to track proportions of long-unburnt vegetation should be considered over labour-intensive surveillance monitoring for reptiles. Targeted monitoring of threatened and other reptile species of conservation or cultural concern may also be warranted. Ecological surveillance monitoring targets multiple taxonomic groups using standardised sampling across large spatial scales. We assessed the adequacy of a general fauna-monitoring program for sampling reptiles as indicators of ecosystem health in the semi-arid Northern Territory and found insufficient power to detect potential future changes in reptile occupancy. Once ecological associations are understood, monitoring ecological health remotely using techniques such fire-scar mapping to track proportions of long-unburnt vegetation, should be considered over labour-intensive surveillance monitoring for reptiles. Photograph by Peter J. McDonald. [ABSTRACT FROM AUTHOR]

Uncertainty, complexity, and dynamic changes present challenges for conservation and natural resource management. Evidence-based approaches grounded in reliable information and rigorous analysis can enhance the navigation of the uncertainties and trade-offs inherent in conservation problems. This study highlights the importance of collaborative efforts and evidence-based decision-making, specifically implementing the Resilience-Experimentalist school of adaptive management (RE-ARM), which emphasizes stakeholder involvement, shared understanding, and experimentation. Our goal was to develop an adaptive management framework to reduce the uncertainty around the use of prescribed fire to manage the habitat for eastern black rails (Laterallus jamaicensis jamaicensis) and mottled ducks (Anas fulvigula) in saltmarshes of the Gulf of Mexico. Supported by discussions at a series of workshops, we used a value of information analysis to select a fire management hypothesis to test, developed an influence diagram to represent the system under fire management, used the influence diagram to develop a Bayesian decision network (BDN), and conducted a power analysis to guide management experiments and monitoring. Value of information analysis identified fire return interval as the critical uncertainty. Our BDN provided valuable insight into how managers believe prescribed fire influences vegetation characteristics and how vegetation influences both eastern black rail occupancy and mottled duck abundance. The results of the power analysis indicated that a standard occupancy modeling framework was more useful to compare 2- and 5-year fire return intervals for black rails than two alternative designs (removal and conditional). Our BDN can be used to predict the probability of achieving the desirable vegetative response to increase the occupancy probability of black rails and abundance of mottled ducks, and monitoring data can be used to update the BDN (learn) and improve best management practices for prescribed burns (adapt). Linking the value of information, BDNs, and power analysis enhances our understanding of the system, improves management decision-making, and builds trust among scientists, interested parties, and decision-makers. This approach lays the groundwork for knowledge co-production and adaptive management.

Measuring fluctuations of the clock phase was identified as a source of leakage in early electromagnetic side-channel investigations. Despite this, only recently was measuring the clock phase (or jitter) of digital signals (not electromagnetic signals) from a target used as a source of exploitable leakage. As the phase of a clock output will be related to signal propagation delay through the target, and this propagation delay is related to voltage, this means that most digital devices perform an unintended phase modulation (PM) of their internal voltage onto clock outputs. This paper first demonstrates an unprofiled CPA attack against a Cortex-M microcontroller using the phase of a clock output, observing the signal on both optically isolated and capacitively isolated paths. The unprofiled attack takes only 2–4x more traces than an attack using a classic shunt-resistor measurement. It is then demonstrated how the JTAG bypass mode can be used to force a clock through a digital device. This forced clock signal can then be used as a highly effective oscilloscope that is located on the target device. As the attack does not require modifications to the device (such as capacitor removal or heat spreader removal) it is difficult to detect using existing countermeasures. The example attack over JTAG uses an unprofiled CPA attack, requiring only about 5x more traces than an ideal shunt-resistor based measurement. In addition, a version of this attack using a fault correlation analysis attack is also demonstrated. Countermeasures are discussed, and a simple resampling countermeasure is tested. All tools both offensive and defensive presented in the paper have been released under open-source licenses.

Security verification of masked software implementations of cryptographic algorithms must account for microarchitectural side-effects of CPUs. Leakage contracts were proposed to provide a formal separation between hardware and software verification, ensuring interoperability and end-to-end security for independently verified components. However, previously proposed leakage contracts did not consider a class of ephemeral hardware effects called glitches, which leaves a considerable gap between security models and the capabilities of real-world attackers. We address this issue by extending the model for leakage contracts to account for glitches and transitions. We further present the first end-to-end verification tool for transient leakage contracts. Our hardware and software verification rely on the same contract as a single source of truth, facilitating fully machine-checked verification from the hardware gate level to the software. By allowing contracts to be written in the C programming language we make power contracts more accessible and intuitive for system-level engineers. To showcase the efficacy of our approach, we apply it to the RISC-V Ibex core. We show that it is possible to write a power contract for Ibex without any modifications to the hardware design. Using this contract, we prove end-to-end security between masked software and gate-level hardware.

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.

Several options exist for conducting inference on indirect effects in mediation analysis. Although methods that use bootstrapping are the preferred inferential approach for testing mediation, they are time-consuming when the test must be performed many times for a power analysis. Alternatives that are more computationally efficient are not as robust, meaning accuracy of the inferences from these methods is more affected by nonnormal and heteroskedastic data. Previous research has shown that different sample sizes are needed to achieve the same amount of statistical power for different inferential approaches with data that meet all the statistical assumptions of linear regression. By contrast, we explore how similar power estimates are at the same sample size, including when assumptions are violated. We compare the power estimates from six inferential methods for between-subjects mediation using a Monte Carlo simulation study. We varied the path coefficients, inferential methods for the indirect effect, and degree to which assumptions are met. We found that when the assumptions of linear regression are met, three inferential methods consistently perform similarly: the joint significance test, the Monte Carlo confidence interval, and the percentile bootstrap confidence interval. When the assumptions were violated, the nonbootstrapping methods tended to have vastly different power estimates compared with the bootstrapping methods. On the basis of these results, we recommend using the more computationally efficient joint significance test for power analysis only when no assumption violations are hypothesized a priori. We also recommend the joint significance test to pick an optimal starting sample size value for power analysis using the percentile bootstrap confidence interval when assumption violations are suspected.

Cronbach’s alpha, widely used for measuring reliability, often operates within studies with sample information, suffering insufficient sample sizes to have sufficient statistical power or precise estimation. To address this challenge and incorporate considerations of both confidence intervals and cost-effectiveness into statistical inferences, our study introduces a novel framework. This framework aims to determine the optimal configuration of measurements and subjects for Cronbach’s alpha by integrating hypothesis testing and confidence intervals. We have developed two R Shiny apps capable of considering up to nine probabilities, which encompass width, validity, and/or rejection events. These apps facilitate obtaining the required number of measurements/subjects, either by minimizing overall cost for a desired probability or by maximizing probability for a predefined cost.

The KRAS oncogene is present in up to 25% of solid tumors and for decades had been undruggable. Sotorasib was the first-in-class KRAS inhibitor to reach the US and European market, and its pharmacological inhibition is restricted to the KRAS p.G12C mutation. Sotorasib showed activity (tumor shrinkage) in patients with non-small cell lung cancer harboring this specific mutation, and efficacy was tested in the CodeBreaK 200, open-label, phase 3 trial (NCT04303780). The results were presented in the ESMO 2022 meeting. CodeBreaK 200 found an improvement in the primary endpoint of progression-free survival (PFS), but overall survival, a key secondary endpoint, was not improved. However, critical questions about the trial's design may limit inferences regarding the reported results. The control arm treatment was inferior to the best standard of care. A late protocol modification (which lowered the sample size and allowed a problematic crossover) prohibited the trial from making a determination regarding overall survival. Imbalance in censoring rates, with potential informative censoring, makes PFS estimates unreliable. Quality-of-life data were also limited. Ultimately, CodeBreaK 200 does not clarify how this therapy should be used in practice, and while we maintain cautious enthusiasm for this and other Ras inhibitors, we await more informative trials.

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]

Ensuring a credible literature base is essential for all research fields. One element of this relates to the replicability of published work, which is the probability that the results of an original study would replicate in an independent investigation. A critical feature of replicable research is that the sample size of a study is sufficient to minimize statistical error and detect effects that exist in reality. A recent study (Buckley, Hyland, et al., 2023) estimated that the replicability of all quantitative technology education research is approximately 55% with this estimate showing an increasing trend in recent years. Given this estimate, it would be useful to invest efforts to improve replicability and thus credibility in the literature base in this way. Power analyses can be conducted when planning a quantitative study to support the determination of sample size requirements to detect population effects, however their existence in technology education research is rare. As the conduction of power analyses is a growing phenomenon in social scientific research more broadly, it is likely that one reason for their limited use by quantitative technology education researchers is a lack of resources within the field. As such, this article offers a primer for technology education researchers for conducting power analysis for common research designs within the field. [ABSTRACT FROM AUTHOR]

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications. [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]

When designing a study for causal mediation analysis, it is crucial to conduct a power analysis to determine the sample size required to detect the causal mediation effects with sufficient power. However, the development of power analysis methods for causal mediation analysis has lagged far behind. To fill the knowledge gap, I proposed a simulation-based method and an easy-to-use web application (https://xuqin.shinyapps.io/CausalMediationPowerAnalysis/) for power and sample size calculations for regression-based causal mediation analysis. By repeatedly drawing samples of a specific size from a population predefined with hypothesized models and parameter values, the method calculates the power to detect a causal mediation effect based on the proportion of the replications with a significant test result. The Monte Carlo confidence interval method is used for testing so that the sampling distributions of causal effect estimates are allowed to be asymmetric, and the power analysis runs faster than if the bootstrapping method is adopted. This also guarantees that the proposed power analysis tool is compatible with the widely used R package for causal mediation analysis, mediation, which is built upon the same estimation and inference method. In addition, users can determine the sample size required for achieving sufficient power based on power values calculated from a range of sample sizes. The method is applicable to a randomized or nonrandomized treatment, a mediator, and an outcome that can be either binary or continuous. I also provided sample size suggestions under various scenarios and a detailed guideline of app implementation to facilitate study designs. [ABSTRACT FROM AUTHOR]

Cronbach's alpha, widely used for measuring reliability, often operates within studies with sample information, suffering insufficient sample sizes to have sufficient statistical power or precise estimation. To address this challenge and incorporate considerations of both confidence intervals and cost-effectiveness into statistical inferences, our study introduces a novel framework. This framework aims to determine the optimal configuration of measurements and subjects for Cronbach's alpha by integrating hypothesis testing and confidence intervals. We have developed two R Shiny apps capable of considering up to nine probabilities, which encompass width, validity, and/or rejection events. These apps facilitate obtaining the required number of measurements/subjects, either by minimizing overall cost for a desired probability or by maximizing probability for a predefined cost. [ABSTRACT FROM AUTHOR]

Abstract Quantifying the cost‐effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost‐effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single‐season occupancy‐detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1–200) and repeat surveys (2–40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24‐h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25‐ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24‐h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time.

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications.

To understand the role of power in health policy processes in low- and middle-income country (LMIC) contexts, it is necessary to engage with global and local power structures and their historical contexts. In this commentary, we outline three dimensions that shape a dominant power in health policy processes — the biomedical power. We propose that understanding the linkages between medical power and colonialism; the close connection of public health, medicine and elite networks; and the intersectionalities that shape the powers of medical professionals can offer the means to examine the biomedical hegemony in health policy processes. Additionally we suggest that a more nuanced understanding of the interaction of local powers with global funding can offer some entry points to achieving more equitable and interdisciplinary health policy processes in LMICs.