Improving Statistical Power in Cluster Randomized Trials

2015 ◽  
pp. 103-126
Keyword(s):  
Statistical Power ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Cluster Randomized

Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials

2020 ◽  
Vol 45 (4) ◽  
pp. 446-474
Author(s):  
Zuchao Shen ◽  
Benjamin Kelcey
Keyword(s):  
Optimal Design ◽  
Statistical Power ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
R Package ◽  
Design Parameters ◽  
Cluster Randomized Trials ◽  
Cost Structures ◽  
Cluster Randomized ◽  
Sample Allocation

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.

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.

scholarly journals The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0119074 ◽  
Author(s):  
Stephen A. Lauer ◽  
Ken P. Kleinman ◽  
Nicholas G. Reich
Keyword(s):  
Cluster Size ◽  
Statistical Power ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Size Variability ◽  
Cluster Randomized

scholarly journals The use of islands and cluster-randomized trials to investigate vector control interventions: a case study on the Bijagós archipelago, Guinea-Bissau

2020 ◽  
Vol 376 (1818) ◽  
pp. 20190807 ◽  
Author(s):  
Robert T. Jones ◽  
Elizabeth Pretorius ◽  
Thomas H. Ant ◽  
John Bradley ◽  
Anna Last ◽  
...  
Keyword(s):  
Vector Control ◽  
Statistical Power ◽  
Randomized Trials ◽  
Control Strategies ◽  
Cluster Randomized Trials ◽  
Vector Borne Diseases ◽  
Guinea Bissau ◽  
Vector Borne ◽  
Cluster Randomized

Vector-borne diseases threaten the health of populations around the world. While key interventions continue to provide protection from vectors, there remains a need to develop and test new vector control tools. Cluster-randomized trials, in which the intervention or control is randomly allocated to clusters, are commonly selected for such evaluations, but their design must carefully consider cluster size and cluster separation, as well as the movement of people and vectors, to ensure sufficient statistical power and avoid contamination of results. Island settings present an opportunity to conduct these studies. Here, we explore the benefits and challenges of conducting intervention studies on islands and introduce the Bijagós archipelago of Guinea-Bissau as a potential study site for interventions intended to control vector-borne diseases. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases'.

Power for Detecting Treatment by Moderator Effects in Two- and Three-Level Cluster Randomized Trials

2016 ◽  
Vol 41 (6) ◽  
pp. 605-627 ◽  
Author(s):  
Jessaca Spybrook ◽  
Benjamin Kelcey ◽  
Nianbo Dong
Keyword(s):  
Statistical Power ◽  
Randomized Trials ◽  
A Priori ◽  
Individual Characteristics ◽  
Cluster Randomized Trials ◽  
Moderator Effects ◽  
Effect Of Treatment ◽  
Cluster Randomized ◽  
Level Cluster ◽  
The Impact

Recently, there has been an increase in the number of cluster randomized trials (CRTs) to evaluate the impact of educational programs and interventions. These studies are often powered for the main effect of treatment to address the “what works” question. However, program effects may vary by individual characteristics or by context, making it important to also consider power to detect moderator effects. This article presents a framework for calculating statistical power for moderator effects at all levels for two- and three-level CRTs. Annotated R code is included to make the calculations accessible to researchers and increase the regularity in which a priori power analyses for moderator effects in CRTs are conducted.

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