ArticleBiometrics2016
Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes.
Article in Biometrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies.BMC medical research methodology · 2023Pooled it
- On the mixed-model analysis of covariance in cluster-randomized trials.Statistical science : a review journal of the Institute of Mathematical Statistics · 2026Article
- Handling incomplete outcomes and covariates in cluster-randomized trials: doubly robust estimation, efficiency considerations, and sensitivity analysis.Biometrics · 2026Article
- Covariate adjustment in cluster randomised trials: a practical guide.BMJ (Clinical research ed.) · 2025Article
- Handling Missing Outcome Data in Cluster Randomized Trials With Both Individual- and Cluster-Level Dropout.Statistics in medicine · 2025Article
- Estimating marginal treatment effect in cluster randomized trials with multi-level missing outcomes.Biometrics · 2024Article
- Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes.Statistics in medicine · 2024Article
- Bias-corrected and doubly robust inference for the three-level longitudinal cluster-randomized trials with missing continuous outcomes and small number of clusters: Simulation study and application to a study for adults with serious mental illnesses.Contemporary clinical trials communications · 2023Article
- Covariate adjustment in randomized clinical trials with missing covariate and outcome data.Statistics in medicine · 2023Article
- Sample size calculation for randomized trials via inverse probability of response weighting when outcome data are missing at random.Statistics in medicine · 2023Article
- Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.Biostatistics (Oxford, England) · 2023Article
- Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.Observational studies · 2023Article
- A stochastic second-order generalized estimating equations approach for estimating association parameters.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2020Article
- A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure.Statistical methods in medical research · 2019Article
- An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness.Communications in statistics: Simulation and computation · 2019Article
- The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials.Statistics in medicine · 2017Article
- Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.American journal of public health · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR); it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.