MRStdLCRT: Model-Robust Standardization for Longitudinal Cluster-Randomized Trials

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Authors: Xi Fang, Fan Li
Cited by: 0
References: 1

Abstract

Provides estimation and leave-one-cluster-out jackknife standard errors for four longitudinal cluster-randomized trial estimands: horizontal individual average treatment effect (h-iATE), horizontal cluster average treatment effect (h-cATE), vertical individual average treatment effect (v-iATE), and vertical cluster-period average treatment effect (v-cATE), using unadjusted and augmented (model-robust standardization) estimators. The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) .

Fields of Study & Subjects

Statistical Methods in Clinical Trials Advanced Causal Inference Techniques Statistical Methods and Bayesian Inference

Publication details

Publication date
15 January 2026
Document type
Dataset