ArticleBiometrics2019
A Bayesian hierarchical model estimating CACE in meta-analysis of randomized clinical trials with noncompliance.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach.American journal of epidemiology · 2022Pooled it
- Variability in Causal Effects and Noncompliance in a Multisite Trial: A Bivariate Hierarchical Generalized Random Coefficients Model for a Binary Outcome.Statistics in medicine · 2024Article
- Generalizing the intention-to-treat effect of an active control from historical placebo-controlled trials: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study.Journal of the American Statistical Association · 2024Article
- A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials.Systematic reviews · 2021Article
- A Bayesian Hierarchical CACE Model Accounting for Incomplete Noncompliance With Application to a Meta-analysis of Epidural Analgesia on Cesarean Section.Journal of the American Statistical Association · 2021Article
- Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Noncompliance to assigned treatment is a common challenge in analysis and interpretation of randomized clinical trials. The complier average causal effect (CACE) approach provides a useful tool for addressing noncompliance, where CACE is defined as the average difference in potential outcomes for the response in the subpopulation of subjects who comply with their assigned treatments. In this article, we present a Bayesian hierarchical model to estimate the CACE in a meta-analysis of randomized clinical trials where compliance may be heterogeneous between studies. Between-study heterogeneity is taken into account with study-specific random effects. The results are illustrated by a re-analysis of a meta-analysis comparing the effect of epidural analgesia in labor versus no or other analgesia in labor on the outcome cesarean section, where noncompliance varied between studies. Finally, we present simulations evaluating the performance of the proposed approach and illustrate the importance of including appropriate random effects and the impact of over- and under-fitting.
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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.