Power considerations for generalized estimating equations analyses of four‐level cluster randomized trials

@article{Wang2021PowerCF,
  title={Power considerations for generalized estimating equations analyses of four‐level cluster randomized trials},
  author={Xueqi Wang and Elizabeth L. Turner and John S. Preisser and Fan Li},
  journal={Biometrical Journal},
  year={2021},
  volume={64},
  pages={663 - 680}
}
In this article, we develop methods for sample size and power calculations in four‐level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs involving four levels are becoming popular in healthcare research, where the effects are measured, for example, from evaluations (level 1) within participants (level 2) in divisions (level 3) that are nested in clusters (level 4). In such multilevel CRTs, we… 

Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome

It is found that the Mancl and DeRouen (2001) type bias-corrected sandwich variance estimator tends to provide the closest to nominal coverage for both propensity score weighting and multivariable regression estimators, extending the existing recommendations for unadjusted analysis of CRTs to accommodate covariate adjustment in small CRTs with a rare binary outcome.

Required sample size to detect mediation in 3-level implementation studies

Background Statistical tests of mediation are important for advancing implementation science; however, little research has examined the sample sizes needed to detect mediation in 3-level designs

Design and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model

A primary focus of current methods for cluster randomized trials (CRTs) has been for continuous, binary, and count outcomes, with relatively less attention given to right‐censored, time‐to‐event

Implementation strategy in collaboration with people with lived experience of mental illness to reduce stigma among primary care providers in Nepal (RESHAPE): protocol for a type 3 hybrid implementation effectiveness cluster randomized controlled trial

A type 3 hybrid implementation-effectiveness cluster randomized controlled trial will evaluate the implementation-as-usual training for PCPs compared to an alternative implementation strategy to train PCPs, entitled Reducing Stigma among Healthcare Providers (RESHAPE).

%CRTFASTGEEPWR: a SAS macro for power of the generalized estimating equations of multi-period cluster randomized trials with application to stepped wedge designs

Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) are commonly used to

Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials

Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small‐sample corrections for valid inference. A recent systematic review

References

SHOWING 1-10 OF 40 REFERENCES

Sample Size Considerations for GEE Analyses of Three‐Level Cluster Randomized Trials

A sample size formula is derived that accounts for two levels of clustering: that of subjects within clusters and that of evaluations within subjects, which reveals that sample size is inflated, relative to a design with completely independent evaluations.

Statistical Power and Sample Size Requirements for Three Level Hierarchical Cluster Randomized Trials

This study derived a closed form power function and formulae for sample size determination required to detect an intervention effect on outcomes at the subject's level from a mixed-effects linear regression model for three level data.

Sample size calculation in three-level cluster randomized trials using generalized estimating equation models

GEE models are utilized to test the treatment effect in a two-group comparison for continuous, binary, or count data in three-level CRTs and a percentage increase in the number of practices is proposed due to efficiency loss from unequal provider and/or practice sizes.

Sample size determination for GEE analyses of stepped wedge cluster randomized trials

It is demonstrated that analytical power agrees well with simulated power for as few as eight clusters, when data is analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-Corrected sandwich variance estimator.

Power and sample size requirements for GEE analyses of cluster randomized crossover trials

This paper considers the two‐treatment two‐period crossover design, and develops sample size procedures for continuous and binary outcomes corresponding to a population‐averaged model estimated by generalized estimating equations, accounting for both within‐period and interperiod correlations.

Sample size determination for clustered count data

This work considers the problem of sample size determination for count data and provides simple expressions for calculating the number of clusters when comparing event rates of two groups in cross-sectional studies.

Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

This work discusses two marginal modeling approaches for the analysis of CRTs with truncated counts and develops two corresponding closed-form sample size formulas to facilitate the design of such trials and explores the implication of right truncation on power.

Optimal designs in three‐level cluster randomized trials with a binary outcome

This paper proposes optimal designs in three-level CRTs with a binary outcome, assuming a nested exchangeable correlation structure in generalized estimating equation models, and provides the variance of estimators of three commonly used measures: risk difference, risk ratio, and odds ratio.

Sample size calculation for stepped-wedge cluster-randomized trials with more than two levels of clustering

The original sample size methodology derived for stepped-wedge trials based on a random intercepts model is extended, to accommodate more than two levels of clustering, and a simple variance inflation factor is obtained that can be used to calculate power and sample size for continuous and by approximation for binary and rate outcomes.

Multilevel analysis of group-randomized trials with binary outcomes.

Analytical methods used for the analysis of binary outcomes from cluster trials, including intra-class correlation (ICC), are reviewed to illustrate these concepts and analytical methods using a school-based GRT.