ArticleStatistical methods in medical research2017
Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
Article in Statistical methods in medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
- Effectiveness of screening and ultra-brief intervention for hazardous drinking in primary care: pragmatic cluster randomised controlled trial.BMJ (Clinical research ed.) · 2025Trial
- Effect of a Nutrition Intervention on Mediterranean Diet Adherence Among Firefighters: A Cluster Randomized Clinical Trial.JAMA network open · 2023Trial
- Trial
- Performance changes in automated lesion detection under federated learning with sequential institution addition.International journal of computer assisted radiology and surgery · 2026Article
- How is missing data handled in cluster randomized controlled trials? A review of trials published in the NIHR Journals Library 1997-2024.Clinical trials (London, England) · 2026Review
- Article
- 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
- Clinical outcomes and cost-effectiveness of collaborative treatment with Korean and Western medicine in patients with facial palsy: a multicenter prospective observational study.BMC complementary medicine and therapies · 2025Observational
- Estimating marginal treatment effect in cluster randomized trials with multi-level missing outcomes.Biometrics · 2024Article
- How should a cluster randomized trial be analyzed?Journal of epidemiology and population health · 2024Article
- Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT).BMC medical research methodology · 2023Article
- Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.Biostatistics (Oxford, England) · 2023Article
- Methodological challenges in pragmatic trials in Alzheimer's disease and related dementias: Opportunities for improvement.Clinical trials (London, England) · 2022Article
- Multimethod assessment of pubertal timing and associations with internalizing psychopathology in early adolescent girls.Journal of psychopathology and clinical science · 2022Article
- Article
- Missing binary outcomes under covariate-dependent missingness in cluster randomised 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
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Authors and funding
3 authors.
Funding
Abstract
Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.
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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.