Analysis of stepped wedge cluster randomized trials in the presence of a time‐varying treatment effect

Kenny, Avi; Voldal, Emily C; Xia, Fan; Heagerty, Patrick J.

doi:10.1002/sim.9511

Cited by 20 publications

(34 citation statements)

References 39 publications

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“…This supports the general recommendation of including time × group random effects in the analysis of multiple period GRT data as there is little indication of any penalty of having it and the potential for a substantial problem if left out. This is consistent with other findings in the literature suggesting that the drawbacks of overfitting tend to be less severe than those associated with under-fitting [35][36][37].…”

Section: Discussionsupporting

confidence: 92%

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

Moyer

Heagerty

Murray

2022

Trials

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Background Multiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model. Methods Continuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group. Results Results indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance. Conclusion The time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.

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Section: Discussionsupporting

confidence: 92%

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

Moyer

Heagerty

Murray

2022

Trials

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“…For example, in a stepped-wedge CRT assessing the effect of exercise on clinical depression, the full effect may take weeks or months to reach. As another example, Kenny et al (2022) conducted a secondary analysis of data from the Washington State Community-Level Expedited Partner Treatment (EPT) Randomized Trial, which sought to test the effect of EPT, an intervention in which the sex partners of individuals with sexually transmitted diseases are treated without evaluation, on rates of chlamydia and gonorrhea (Golden et al, 2015). When the instantaneous and sustained treatment effect was assumed, the parameter estimates indicated a small beneficial treatment effect.…”

Section: Introductionmentioning

confidence: 99%

“…If the form of exposure-time treatment effect heterogeneity is known a priori, one can proceed by modeling the treatment effect as an explicit, parametric function of exposure time. For example, Hughes et al (2015) proposed fixed-effects parameterizations which include delayed, linear-time, and exponential-time treatment effect functions, and Kenny et al (2022) further developed overall summary measures based on such time-specific treatment effects. The knowledge of the true functional form for exposure-time treatment heterogeneity, however, may not be available in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Exposure‐time treatment effect heterogeneity has previously been explored under the linear mixed model framework with a continuous outcome (Hughes et al., 2015; Kenny et al., 2022; Nickless et al., 2018). If the form of exposure‐time treatment effect heterogeneity is known a priori, one can proceed by modeling the treatment effect as an explicit, parametric function of exposure time.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Exposure-Time Treatment Effect Heterogeneity in Stepped-Wedge Cluster Randomized Trials

Maleyeff

Haneuse

et al. 2022

Biometrics

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A stepped‐wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure‐time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure‐time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure‐time periods and may result in more precise average and exposure‐time‐specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite‐sample operating characteristics, and to generate practical guidance on model choices for assessing exposure‐time treatment effect heterogeneity in stepped‐wedge CRTs.

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“…It is of interest to further explore design and analysis strategies for SW‐CRTs with multivariate outcomes when the intervention effects are unknown functions of the exposure time. Under the SW‐CRT design with a single primary endpoint, Kenny et al 31 found that assuming an immediate treatment effect when the true treatment effect is a function of exposure time can lead to estimation bias and invalid inference. It is likely that the same conclusion would apply to the multivariate linear mixed models with more than one primary endpoint.…”

Section: Discussionmentioning

confidence: 99%

Power analyses for stepped wedge designs with multivariate continuous outcomes

Davis‐Plourde

Taljaard

2022

Statistics in Medicine

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Multivariate outcomes are common in pragmatic cluster randomized trials. While sample size calculation procedures for multivariate outcomes exist under parallel assignment, none have been developed for a stepped wedge design. In this article, we present computationally efficient power and sample size procedures for stepped wedge cluster randomized trials (SW‐CRTs) with multivariate outcomes that differentiate the within‐period and between‐period intracluster correlation coefficients (ICCs). Under a multivariate linear mixed model, we derive the joint distribution of the intervention test statistics which can be used for determining power under different hypotheses and provide an example using the commonly utilized intersection‐union test for co‐primary outcomes. Simplifications under a common treatment effect and common ICCs across endpoints and an extension to closed‐cohort designs are also provided. Finally, under the common ICC across endpoints assumption, we formally prove that the multivariate linear mixed model leads to a more efficient treatment effect estimator compared to the univariate linear mixed model, providing a rigorous justification on the use of the former with multivariate outcomes. We illustrate application of the proposed methods using data from an existing SW‐CRT and present extensive simulations to validate the methods.

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Analysis of stepped wedge cluster randomized trials in the presence of a time‐varying treatment effect

Cited by 20 publications

References 39 publications

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

Assessing Exposure-Time Treatment Effect Heterogeneity in Stepped-Wedge Cluster Randomized Trials

Power analyses for stepped wedge designs with multivariate continuous outcomes

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