The statistical power of individual-level risk preference estimation

2020 ◽  
Vol 6 (2) ◽  
pp. 168-188
Author(s):  
Brian Albert Monroe
Keyword(s):  
Statistical Power ◽  
Risk Preference ◽  
Individual Level

scholarly journals Power analyses for moderator effects with (non)randomly varying slopes in cluster randomized trials

Methodology ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. 92-110
Author(s):  
Nianbo Dong ◽  
Jessaca Spybrook ◽  
Benjamin Kelcey ◽  
Metin Bulus
Keyword(s):  
Statistical Power ◽  
Randomized Trials ◽  
Future Research ◽  
Size Difference ◽  
Detectable Effect ◽  
Cluster Randomized Trials ◽  
Individual Level ◽  
Moderator Effects ◽  
Cluster Randomized ◽  
Power Analyses

Researchers often apply moderation analyses to examine whether the effects of an intervention differ conditional on individual or cluster moderator variables such as gender, pretest, or school size. This study develops formulas for power analyses to detect moderator effects in two-level cluster randomized trials (CRTs) using hierarchical linear models. We derive the formulas for estimating statistical power, minimum detectable effect size difference and 95% confidence intervals for cluster- and individual-level moderators. Our framework accommodates binary or continuous moderators, designs with or without covariates, and effects of individual-level moderators that vary randomly or nonrandomly across clusters. A small Monte Carlo simulation confirms the accuracy of our formulas. We also compare power between main effect analysis and moderation analysis, discuss the effects of mis-specification of the moderator slope (randomly vs. non-randomly varying), and conclude with directions for future research. We provide software for conducting a power analysis of moderator effects in CRTs.

Robustness of Statistical Power in Group-Randomized Studies of Mediation Under an Optimal Sampling Framework

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 106-118
Author(s):  
Kyle Cox ◽  
Benjamin Kelcey
Keyword(s):  
Statistical Power ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Design Stage ◽  
Optimal Sampling ◽  
True Parameter ◽  
Individual Level ◽  
Randomized Studies ◽  
Sample Allocation ◽  
Parameter Values

Abstract. When planning group-randomized studies probing mediation, effective and efficient sample allocation is governed by several parameters including treatment-mediator and mediator-outcome path coefficients and the mediator and outcome intraclass correlation coefficients. In the design stage, these parameters are typically approximated using information from prior research and these approximations are likely to deviate from the true values eventually realized in the study. This study investigates the robustness of statistical power under an optimal sampling framework to misspecified parameter values in group-randomized designs with group- or individual-level mediators. The results suggest that estimates of statistical power are robust to misspecified parameter values across a variety of conditions and tests. Relative power remained above 90% in most conditions when the incorrect parameter value ranged between 50% and 150% of the true parameter.

scholarly journals A pooling-based approach to mapping genetic variants associated with DNA methylation

2015 ◽  
Author(s):  
Irene Miriam Kaplow ◽  
Julia L MacIsaac ◽  
Sarah M Mah ◽  
Lisa M McEwen ◽  
Michael S Kobor ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genetic Variants ◽  
Statistical Power ◽  
Epigenetic Modification ◽  
Chromatin Accessibility ◽  
Ctcf Binding ◽  
Sequencing Data ◽  
Individual Level ◽  
Novel Approach ◽  
Genome Wide

DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover less than 2% of the genome and cannot account for allele-specific methylation (ASM). Other studies have performed whole-genome bisulfite sequencing on a few individuals, but these lack statistical power to identify variants associated with DNA methylation. We present a novel approach in which bisulfite-treated DNA from many individuals is sequenced together in a single pool, resulting in a truly genome-wide map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified over 2,000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data.

scholarly journals Cryptic individual-level variation in propensity for selfing in a hermaphroditic snail

2021 ◽  
Author(s):  
Anja Felmy ◽  
Alena B Streiff ◽  
Jukka Jokela
Keyword(s):  
Population Density ◽  
Individual Variation ◽  
Statistical Power ◽  
Mating Behaviour ◽  
Empirical Support ◽  
Reproductive Assurance ◽  
Level Variation ◽  
Heritable Variation ◽  
Mate Availability ◽  
Individual Level

For mating-system evolution, individual-level variation is as important as variation among populations. In self-compatible hermaphrodites, individuals may vary in their lifetime propensity for selfing, which consists of a fundamental, likely genetic and an environmental component. According to the reproductive assurance hypothesis explaining partial selfing, a key environmental factor is mate availability, which fluctuates with population density. We quantified individual variation in selfing propensity in a hermaphroditic snail by manipulating mate availability in the laboratory, recording mating behaviour, estimating selfing rates from progeny arrays, and measuring female lifetime fitness. Our results revealed four classes of individuals with different selfing propensities: pure outcrossers, pure selfers, and two types of plastic individuals. These classes only became apparent in the laboratory; the field population is outcrossing. All classes were present both under low and increased mate availability; this large among-individual variation in selfing propensities meant that effects of the pairing treatment on the frequency and extent of selfing were non-significant despite large effect sizes and sufficient statistical power. We believe that selfing propensities may have a genetic component and when selected on cause mean selfing rates to evolve. We propose that heritable variation in selfing propensities offers a reconciliation between the reproductive assurance hypothesis and its weak empirical support: distributions of selfing propensities vary temporally and spatially, thus obscuring the relationship between population density and realised selfing rates.

scholarly journals metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

2015 ◽  
Author(s):  
Anna Cichonska ◽  
Juho Rousu ◽  
Pekka Marttinen ◽  
Antti J Kangas ◽  
Pasi Soininen ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Statistical Power ◽  
Association Studies ◽  
Meta Analysis ◽  
Original Data ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Individual Level

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analysing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.

scholarly journals Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tamar Sofer ◽  
Xiuwen Zheng ◽  
Cecelia A. Laurie ◽  
Stephanie M. Gogarten ◽  
Jennifer A. Brody ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Statistical Power ◽  
Epidemiological Studies ◽  
Phenotypic Variance ◽  
Whole Genome ◽  
Association Analyses ◽  
Individual Level ◽  
Level Data ◽  
The Impact

AbstractIn modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term ‘variance stratification’. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.

scholarly journals Individual-level response adaptive crossover trial design for epilepsy: structure and simulation

2020 ◽  
Author(s):  
Wesley T. Kerr ◽  
Xingruo Zhang ◽  
John M. Stern
Keyword(s):  
Seizure Frequency ◽  
Statistical Power ◽  
Crossover Design ◽  
Controlled Clinical Trial ◽  
Duration Of Treatment ◽  
Individual Level ◽  
Group Assignment ◽  
Likelihood Approach ◽  
Adaptive Crossover ◽  
Static Group

Trials of antiseizure medications involve static group assignments for treatments with pre-specified durations. We propose a response-adaptive crossover design using basic statistical assumptions regarding both seizure count and duration of treatment to determine when a participant can change group assignment. We modelled seizure frequency as a Poisson process and estimated the likelihood that seizure frequency had decreased by 50% compares to baseline using both a Bayesian and maximum likelihood approach. We simulated trials to estimate the influence of this design on statistical power and observation duration with each treatment. For patients with 9 baseline seizures in 4 weeks who had no change in seizure frequency, the simulation identified non-response in a median of 16 days. The response-adaptive crossover design resulted in a modest increase in statistical power to identify an effective treatment while maximizing the time in a group producing a response. Only 8% of participants remained in the placebo group for all 90 days of the simulated trials. These example theoretical results can provide quantitative guidance regarding objective criteria to determine non-response in real-time during a controlled clinical trial without revealing the assigned treatment. Implementing a response-adaptive crossover design may both improve statistical power while minimizing participant risk.

scholarly journals PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zijie Zhao ◽  
Yanyao Yi ◽  
Jie Song ◽  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
...  
Keyword(s):  
Statistical Power ◽  
Human Genetics ◽  
Association Studies ◽  
Fine Tuning ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Polygenic Risk ◽  
Individual Level ◽  
Genetics Research

AbstractPolygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.

Control and Well-being Across Cultures: The Moderation of Individualism on the Relationship between Primary & Secondary Control and Affective & Cognitive SWB

2021 ◽  
Author(s):  
Xingyu LIU ◽  
René Mõttus
Keyword(s):  
Perceived Control ◽  
Statistical Power ◽  
Well Being ◽  
Primary Control ◽  
Subjective Well Being ◽  
Secondary Control ◽  
Individual Level ◽  
Country Level ◽  
The Individual ◽  
Collectivist Cultures

Perceived control is essential to our subjective well-being (SWB). However, people from individualist and collectivist cultures may prefer different types of perceived control. We examined cultural variations in agency-oriented primary control and adjustment-oriented secondary control, their relationship to affective and cognitive SWB, and the moderation of Individualism of this relationship. We used an IPIP-based personality questionnaire that sampled 40, 000 participants from over across 42 countries. Using multilevel analyses, we found that the correlations between the two types of perceived control and the two aspects of SWB were different at the country level versus the individual level: primary control was more strongly related to cognitive SWB than affective SWB at the individual level, but the pattern was flipped at the country level. Contrary to previous results, we found stronger associations between individualism and secondary control and between collectivism and primary control; and primary control better predicted general SWB in collectivist cultures while secondary control better predicted general SWB in individualist cultures. We explored methodological (e.g., high influence items), philosophical (e.g., Hobbesian social contract), and sociopolitical (e.g., globalization) explanations of our results. Given the high generalizability and the high statistical power of our results that contradict previous findings, our study potentially calls for more nuanced and more generalizable revision of the current understanding of cultural differences in perceived control and its relation to SWB.

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