Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study

Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework introduces additional design considerations and has the potential to identify designs with more statistical power, even when some parameters are constrained due to immutable practical concerns. The results also suggest that the gains in efficiency introduced through the expanded framework are fairly robust to misspecifications of the expanded cost structure and concomitant design parameters (e.g., intraclass correlation coefficient). The proposed framework is implemented in the R package odr.

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Linkage studies of schizophrenia: A simulation study of statistical power

Genetic Epidemiology ◽

10.1002/gepi.1370090205 ◽

1992 ◽

Vol 9 (2) ◽

pp. 123-139 ◽

Cited By ~ 21

Author(s):

Wei J. Chen ◽

Stephen V. Faraone ◽

Ming T. Tsuang ◽

G. P. Vogler

Keyword(s):

Simulation Study ◽

Statistical Power ◽

Linkage Studies

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

Methodology ◽

10.5964/meth.4003 ◽

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.

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Exploring a model-based analysis of patient derived xenograft studies in oncology drug development

PeerJ ◽

10.7717/peerj.10681 ◽

2021 ◽

Vol 9 ◽

pp. e10681

Author(s):

Jake Dickinson ◽

Marcel de Matas ◽

Paul A. Dickinson ◽

Hitesh B. Mistry

Keyword(s):

Simulation Study ◽

Statistical Power ◽

Tumour Growth ◽

Empirical Approach ◽

Series Data ◽

Ratio Test ◽

Data Set ◽

Model Based ◽

Patient Derived Xenograft ◽

Model Based Analysis

Purpose To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. Methods Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). Results The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. Conclusions The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.

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