Restricted mean survival time in cluster randomized trials with a small number of clusters: Improving variance estimation of the intervention effect from the pseudo-values regression

In randomized clinical trials with a time-to-event outcome, the intervention effect could be quantified by a difference in restricted mean survival time (ΔRMST) between the intervention and control groups, defined as the expected survival duration gain due to the intervention over a fixed follow-up period. In cluster randomized trials (CRTs), social units are randomized to intervention or control groups; the correlation between survival times of the individuals within the same cluster must be taken into account in the statistical analysis. In a previous work, we proposed the use of pseudo-values regression, based on generalized estimating equations (GEEs), for estimating ΔRMST in CRTs. We showed that this method correctly estimated the ΔRMST and controlled the type I error rate in CRTs with at least 50 clusters. Here, we propose methods for CRTs with a small number of clusters (<50). We evaluated the performance of four bias-corrections of the GEE sandwich variance estimator of the intervention effect. We also considered the use of a Student <i>t</i> distribution as an alternative to the normal distribution of the GEE Wald test statistic for testing the intervention effect and constructing the confidence interval. With a simulation study, assuming proportional or non-proportional hazards, we showed that the Student <i>t</i> distribution outperformed the normal distribution in terms of type I error rate, and the Fay and Graubard bias-corrected variance led to an appropriate type I error rate whatever the number of clusters. Therefore, we recommend the use of the Fay and Graubard variance estimator combined with a Student <i>t</i> distribution for the pseudo-values regression to correctly estimate the variance of the intervention effect. Finally, we provide an illustrative analysis of the DEMETER trial evaluating the use of a specific endotracheal tube for subglottic secretion drainage to prevent ventilator-associated pneumonia, by comparing each of the methods considered.

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Journal
Statistical Methods in Medical Research
Published
2026-01-12
DOI
https://doi.org/10.1177/09622802251406581
Primary Topic
Advanced Causal Inference Techniques
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article
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article

Restricted mean survival time in cluster randomized trials with a small number of clusters: Improving variance estimation of the intervention effect from the pseudo-values regression

Agnès Caille, Étienne Dantan, JA Thompson, F. Le Vilain-Abraham et al.
Statistical Methods in Medical Research
Advanced Causal Inference Techniques
article

Restricted mean survival time in cluster randomized trials with a small number of clusters: Improving variance estimation of the intervention effect from the pseudo-values regression

Agnès Caille, Étienne Dantan, JA Thompson, F. Le Vilain-Abraham, Solène Desmée
article en

Abstract

In randomized clinical trials with a time-to-event outcome, the intervention effect could be quantified by a difference in restricted mean survival time (ΔRMST) between the intervention and control groups, defined as the expected survival duration gain due to the intervention over a fixed follow-up period. In cluster randomized trials (CRTs), social units are randomized to intervention or control groups; the correlation between survival times of the individuals within the same cluster must be taken into account in the statistical analysis. In a previous work, we proposed the use of pseudo-values regression, based on generalized estimating equations (GEEs), for estimating ΔRMST in CRTs. We showed that this method correctly estimated the ΔRMST and controlled the type I error rate in CRTs with at least 50 clusters. Here, we propose methods for CRTs with a small number of clusters (<50). We evaluated the performance of four bias-corrections of the GEE sandwich variance estimator of the intervention effect. We also considered the use of a Student <i>t</i> distribution as an alternative to the normal distribution of the GEE Wald test statistic for testing the intervention effect and constructing the confidence interval. With a simulation study, assuming proportional or non-proportional hazards, we showed that the Student <i>t</i> distribution outperformed the normal distribution in terms of type I error rate, and the Fay and Graubard bias-corrected variance led to an appropriate type I error rate whatever the number of clusters. Therefore, we recommend the use of the Fay and Graubard variance estimator combined with a Student <i>t</i> distribution for the pseudo-values regression to correctly estimate the variance of the intervention effect. Finally, we provide an illustrative analysis of the DEMETER trial evaluating the use of a specific endotracheal tube for subglottic secretion drainage to prevent ventilator-associated pneumonia, by comparing each of the methods considered.

Statistical Methods in Medical Research
Université de Tours (FR), Inserm (FR), London School of Hygiene & Tropical Medicine (GB), Centre Hospitalier Universitaire de Tours (FR), Clinical Investigation Center Plurithematic Tours (FR), Centre Hospitalier Départemental Vendée (FR), methodS in Patient-centered outcomes and HEalth ResEarch, Nantes Université (FR)
Openalex Percentile: Top 91%
Advanced Causal Inference Techniques
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