Design and analysis considerations for cohort stepped wedge cluster randomized trials with a decay correlation structure

Self Cite

Cluster randomized trials (CRTs) refer to experiments with randomization carried out at the cluster or the group level. While numerous statistical methods have been developed for the design and analysis of CRTs, most of the existing methods focused on testing the overall treatment effect across the population characteristics, with few discussions on the differential treatment effect among subpopulations. In addition, the sample size and power requirements for detecting differential treatment effect in CRTs remain unclear, but are helpful for studies planned with such an objective. In this article, we develop a new sample size formula for detecting treatment effect heterogeneity in two-level CRTs for continuous outcomes, continuous or binary covariates measured at cluster or individual level. We also investigate the roles of two intraclass correlation coefficients (ICCs): the adjusted ICC for the outcome of interest and the marginal ICC for the covariate of interest. We further derive a closed-form design effect formula to facilitate the application of the proposed method, and provide extensions to accommodate multiple covariates. Extensive simulations are carried out to validate the proposed formula in finite samples. We find that the empirical power agrees well with the prediction across a range of parameter constellations, when data are analyzed by a linear mixed effects model with a treatment-by-covariate interaction. Finally, we use data from the HF-ACTION study to illustrate the proposed sample size procedure for detecting heterogeneous treatment effects.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials

Yang

Li²,

Starks

et al. 2020

Self Cite

“…Autoregressive errors at the participant level can be obtained by setting

D_{ϵ} (t, s) = τ_{D}^{| t - s |}

, and the discrete‐time decay model of Kasza et al is returned if

D_{C P} (t, s) = π_{D}^{| t - s |}

for constants τ D and π D . Li presented a similar model for closed‐cohort longitudinal cluster randomized trials with autoregressive participant‐level errors and decaying between‐period correlations.…”

Section: Sample Size Formulas For Open Cohort Longitudinal Cluster Ramentioning

confidence: 99%

Sample size and power calculations for open cohort longitudinal cluster randomized trials

Kasza

Hooper

Copas

et al. 2020

When calculating sample size or power for stepped wedge or other types of longitudinal cluster randomized trials, it is critical that the planned sampling structure be accurately specified. One common assumption is that participants will provide measurements in each trial period, that is, a closed cohort, and another is that each participant provides only one measurement during the course of the trial. However some studies have an "open cohort" sampling structure, where participants may provide measurements in variable numbers of periods. To date, sample size calculations for longitudinal cluster randomized trials have not accommodated open cohorts. Feldman and McKinlay (1994) provided some guidance, stating that the participant-level autocorrelation could be varied to account for the degree of overlap in different periods of the study, but did not indicate precisely how to do so. We present sample size and power formulas that allow for open cohorts and discuss the impact of the degree of "openness" on sample size and power. We consider designs where the number of participants in each cluster will be maintained throughout the trial, but individual participants may provide differing numbers of measurements. Our results are a unification of closed cohort and repeated cross-sectional sample results of Hooper et al (2016), and indicate precisely how participant autocorrelation of Feldman and McKinlay should be varied to account for an open cohort sampling structure. We discuss different types of open cohort sampling schemes and how open cohort sampling structure impacts on power in the presence of decaying within-cluster correlations and autoregressive participant-level errors. K E Y W O R D Scluster crossover trial, intra cluster correlation, mixed effects models, stepped wedge INTRODUCTIONCluster randomized trials are randomized trials in which clusters of participants, rather than the participants themselves, are randomized to particular treatments. 1 Longitudinal cluster randomized trials extend standard cluster randomized trials in time: clusters are now randomized to a sequence of treatments, and may switch between intervention and control conditions over the course of the trial. 2,3 Particular examples of such trials include cluster randomized cross-over trials, 4 stepped wedge trials, 5 or even parallel cluster trial designs, in which measurements are taken at several time pointsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

“…However, this article assumed a study design and statistical model that are not necessarily applicable to the general landscape of cluster SWTs. Additionally, Li et al 12 and Li 20 found both the Kauermann and Carroll 21 and Fay and Graubard 19 bias corrections to perform favorably using differing degrees of freedom. However, their settings assume correctly specified correlation structures which, to our knowledge, are not readily available in most statistical software, and they assume common cluster sizes.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, small‐sample corrections and df adjustments have yet to be adequately considered in the cluster SWT literature. For a summary comparing the different aspects of the simulation studies of Scott et al, 7 Li et al, 12 Li, 20 and this article, please see Table 1.…”

Section: Introductionmentioning

confidence: 99%

Maintaining the validity of inference in small‐sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations

Ford

Westgate

2020

Stepped wedge cluster trials are an increasingly popular alternative to traditional parallel cluster randomized trials. Such trials often utilize a small number of clusters and numerous time intervals, and these components must be considered when choosing an analysis method. A generalized linear mixed model containing a random intercept and fixed time and intervention covariates is the most common analysis approach. However, the sole use of a random intercept applies a constant intraclass correlation coefficient structure, which is an assumption that is likely to be violated given stepped wedge trials (SWTs) have multiple time intervals. Alternatively, generalized estimating equations (GEE) are robust to the misspecification of the working correlation structure, although it has been shown that small‐sample adjustments to standard error estimates and the use of appropriate degrees of freedom are required to maintain the validity of inference when the number of clusters is small. In this article, we show, using an extensive simulation study based on a motivating example and a more general design, the use of GEE can maintain the validity of inference in small‐sample SWTs with binary outcomes. Furthermore, we show which combinations of bias corrections to standard error estimates and degrees of freedom work best in terms of attaining nominal type I error rates.