ArticleStatistical methods in medical research2019
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
25 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
- Extension of efficacy range for targeted malaria-elimination interventions due to spillover effects.Nature medicine · 2024Trial
- A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.Statistics in medicine · 2026Article
- Model-robust standardization in stepped wedge cluster randomized trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes.Statistics in medicine · 2026Article
- Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification.Biostatistics (Oxford, England) · 2025Article
- Causal Forests Versus Inverse Probability of Treatment Weighting to Adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study Based on US Hospital Electronic Health Record Data.Pharmacoepidemiology and drug safety · 2025Article
- Causal inference in randomized trials with partial clustering.Clinical trials (London, England) · 2025Article
- Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Association between the age-adjusted Charlson Comorbidity Index and complications after kidney transplantation: a retrospective observational cohort study.BMC nephrology · 2024Observational
- Cluster Randomized Trials Designed to Support Generalizable Inferences.Evaluation review · 2024Article
- Delaying high school start times impacts depressed mood among students: evidence from a natural experiment.Social psychiatry and psychiatric epidemiology · 2024Article
- Demystifying estimands in cluster-randomised trials.Statistical methods in medical research · 2024Article
- Blurring cluster randomized trials and observational studies: Two-Stage TMLE for subsampling, missingness, and few independent units.Biostatistics (Oxford, England) · 2024Article
- Model-robust and efficient covariate adjustment for cluster-randomized experiments.Journal of the American Statistical Association · 2024Article
- Understanding the impact of radiotherapy fractionation on overall survival in a large head and neck squamous cell carcinoma dataset: a comprehensive approach combining mechanistic and machine learning models.Frontiers in oncology · 2024Article
- Pre-pregnancy body mass index and adverse maternal and perinatal outcomes in twins: A population retrospective cohort study.International journal of obesity (2005) · 2023Article
- Defining and estimating effects in cluster randomized trials: A methods comparison.Statistics in medicine · 2023Review
- Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.Biostatistics (Oxford, England) · 2023Article
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and population effects of interventions with proven individual-level efficacy. In these settings, individual-level outcomes are correlated due to shared cluster-level factors, including the exposure, as well as social or biological interactions between individuals. To flexibly and efficiently estimate the effect of a cluster-level exposure, we present two targeted maximum likelihood estimators (TMLEs). The first TMLE is developed under a non-parametric causal model, which allows for arbitrary interactions between individuals within a cluster. These interactions include direct transmission of the outcome (i.e. contagion) and influence of one individual's covariates on another's outcome (i.e. covariate interference). The second TMLE is developed under a causal sub-model assuming the cluster-level and individual-specific covariates are sufficient to control for confounding. Simulations compare the alternative estimators and illustrate the potential gains from pairing individual-level risk factors and outcomes during estimation, while avoiding unwarranted assumptions. Our results suggest that estimation under the sub-model can result in bias and misleading inference in an observational setting. Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model. We illustrate our approach with an application to HIV prevention and treatment.
Indexed as
Identifiers
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.