Randomization inference for stepped-wedge cluster-randomized trials: An application to community-based health insurance

Ji, Xinyao; Fink, Günther; Robyn, Paul Jacob; Small, Dylan S.

doi:10.1214/16-aoas969

Cited by 29 publications

(49 citation statements)

References 34 publications

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“…More research, with specific simulation settings derived from representative trials in various domains, is necessary to determine the relative performance of these methods across a wide variety of settings. Various data‐generating processes and assumptions about those processes—including specific nonnormal random effects, different correlation structures, and treatment effects that vary by time or cluster—have been proposed in prior research on SW‐CRTs . Some of these may be more reasonable in some individual fields than in others, and so research to determine which methods are best suited to specific SW‐CRT settings, considering the outcome, cluster, and intervention of interest, would be very valuable.…”

Section: Discussionmentioning

confidence: 99%

“…First, we consider the commonly used mixed effects model with a random intercept for cluster and fixed effects for time:

h (E [Y_{i, j}]) = μ + α_{i} + θ_{j} + X_{i, j} β,

where h is the link function, μ is the global mean under control in period 1,

α_{i} \overset{iid}{\sim} N (0, τ^{2})

, and θ 1 =0 for identifiability . Generalized linear mixed model theory can be used for asymptotic inference, and permutation tests (and associated confidence intervals) can be used for exact inference with this model …”

Section: Methodsmentioning

confidence: 99%

“…Misspecified random effects distributions can affect inference on the fixed effect estimators (ie, the treatment effect estimator), although the effect on fixed effect estimates themselves is unclear and context dependent . Finally, for the relatively small number of clusters in many SW‐CRTs, asymptotic inference based on the assumption of normally distributed random effects—as is frequently made for analysis using mixed effects models—can lead to inflated type I error and poor confidence interval coverage …”

Section: Introductionmentioning

confidence: 99%

“…27,[29][30][31][32][33][34] Finally, for the relatively small number of clusters in many SW-CRTs, asymptotic inference based on the assumption of normally distributed random effects-as is frequently made for analysis using mixed effects models-can lead to inflated type I error and poor confidence interval coverage. 35,36 Various methods have been proposed to remedy these issues. One approach, proposed by Wang and De Gruttola 35 and by Ji et al, 36 uses permutation tests to ensure nominal type I error and accurate inference, even for small numbers of clusters, as long as the effect estimate is unbiased.…”

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confidence: 99%

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Novel methods for the analysis of stepped wedge cluster randomized trials

Kennedy‐Shaffer

Gruttola

Lipsitch

2019

Statistics in Medicine

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Stepped wedge cluster randomized trials (SW‐CRTs) have become increasingly popular and are used for a variety of interventions and outcomes, often chosen for their feasibility advantages. SW‐CRTs must account for time trends in the outcome because of the staggered rollout of the intervention. Robust inference procedures and nonparametric analysis methods have recently been proposed to handle such trends without requiring strong parametric modeling assumptions, but these are less powerful than model‐based approaches. We propose several novel analysis methods that reduce reliance on modeling assumptions while preserving some of the increased power provided by the use of mixed effects models. In one method, we use the synthetic control approach to find the best matching clusters for a given intervention cluster. Another method makes use of within‐cluster crossover information to construct an overall estimator. We also consider methods that combine these approaches to further improve power. We test these methods on simulated SW‐CRTs, describing scenarios in which these methods have increased power compared with existing nonparametric methods while preserving nominal validity when mixed effects models are misspecified. We also demonstrate theoretical properties of these estimators with less restrictive assumptions than mixed effects models. Finally, we propose avenues for future research on the use of these methods; motivation for such research arises from their flexibility, which allows the identification of specific causal contrasts of interest, their robustness, and the potential for incorporating covariates to further increase power. Investigators conducting SW‐CRTs might well consider such methods when common modeling assumptions may not hold.

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Section: Discussionmentioning

confidence: 99%

“…First, we consider the commonly used mixed effects model with a random intercept for cluster and fixed effects for time:

h (E [Y_{i, j}]) = μ + α_{i} + θ_{j} + X_{i, j} β,

where h is the link function, μ is the global mean under control in period 1,

α_{i} \overset{iid}{\sim} N (0, τ^{2})

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Novel methods for the analysis of stepped wedge cluster randomized trials

Kennedy‐Shaffer

Gruttola

Lipsitch

2019

Statistics in Medicine

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“…Since the cluster is the unit of randomization in a stepped wedge trial, an alternative approach to evaluating the intervention may be based on a permutation test that permutes the treatment sequences among the clusters. Ji et al (2017) considered properties of permutation tests for stepped wedge designs when the underlying mean (fixed effect) structure of the data-generating process is correctly specified, although they do consider situations in which the variance structure is misspecified. Wang and DeGruttola (2017) also investigated the behavior of permutation tests compared to mixed-effects models when the mixed-effects model fixed effects and variance structure are correctly specified but the error distribution may be misspecified.…”

Section: Introductionmentioning

confidence: 99%

Robust inference for the stepped wedge design

2019

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Stepped wedge designed trials are a type of cluster‐randomized study in which the intervention is introduced to each cluster in a random order over time. This design is often used to assess the effect of a new intervention as it is rolled out across a series of clinics or communities. Based on a permutation argument, we derive a closed‐form expression for an estimate of the intervention effect, along with its standard error, for a stepped wedge design trial. We show that these estimates are robust to misspecification of both the mean and covariance structure of the underlying data‐generating mechanism, thereby providing a robust approach to inference for the intervention effect in stepped wedge designs. We use simulations to evaluate the type 1 error and power of the proposed estimate and to compare the performance of the proposed estimate to the optimal estimate when the correct model specification is known. The limitations, possible extensions, and open problems regarding the method are discussed.

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Robust analysis of stepped wedge trials using composite likelihood models

Voldal,

Kenny,

Xia

et al. 2024

Statistics in Medicine

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Stepped wedge trials (SWTs) are a type of cluster randomized trial that involve repeated measures on clusters and design‐induced confounding between time and treatment. Although mixed models are commonly used to analyze SWTs, they are susceptible to misspecification particularly for cluster‐longitudinal designs such as SWTs. Mixed model estimation leverages both “horizontal” or within‐cluster information and “vertical” or between‐cluster information. To use horizontal information in a mixed model, both the mean model and correlation structure must be correctly specified or accounted for, since time is confounded with treatment and measurements are likely correlated within clusters. Alternative non‐parametric methods have been proposed that use only vertical information; these are more robust because between‐cluster comparisons in a SWT preserve randomization, but these non‐parametric methods are not very efficient. We propose a composite likelihood method that focuses on vertical information, but has the flexibility to recover efficiency by using additional horizontal information. We compare the properties and performance of various methods, using simulations based on COVID‐19 data and a demonstration of application to the LIRE trial. We found that a vertical composite likelihood model that leverages baseline data is more robust than traditional methods, and more efficient than methods that use only vertical information. We hope that these results demonstrate the potential value of model‐based vertical methods for SWTs with a large number of clusters, and that these new tools are useful to researchers who are concerned about misspecification of traditional models.

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Randomization inference for stepped-wedge cluster-randomized trials: An application to community-based health insurance

Cited by 29 publications

References 34 publications

Novel methods for the analysis of stepped wedge cluster randomized trials

Novel methods for the analysis of stepped wedge cluster randomized trials

Robust inference for the stepped wedge design

Robust analysis of stepped wedge trials using composite likelihood models

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