ArticleStatistics in medicine2022
A flexible approach for causal inference with multiple treatments and clustered survival outcomes.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 13 citations in OpenAlex.
- Multi-Level Variable Selection Using a BART-Enhanced Mixed-Effects Framework.Statistics in medicine · 2026Article
- Joint mixed-effects models for causal inference in clustered network-based observational studies.Statistical methods in medical research · 2026Article
- Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes.Lifetime data analysis · 2026Article
- A Bayesian Approach to the G-Formula via Iterative Conditional Regression.Statistics in medicine · 2025Article
- Evaluating Meta-Learners to Analyze Treatment Heterogeneity in Survival Data: Application to Electronic Health Records of Pediatric Asthma Care in COVID-19 Pandemic.Statistics in medicine · 2025Article
- causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes.Observational studies · 2025Article
- A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Estimating the causal effects of multiple intermittent treatments with application to COVID-19.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.International journal of environmental research and public health · 2022Review
- A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.International journal of environmental research and public health · 2022Article
- A flexible approach for causal inference with multiple treatments and clustered survival outcomes.Statistics in medicine · 2022Article
- CIMTx: An R Package for Causal Inference with Multiple Treatments using Observational Data.The R journal · 2022Article
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 at 2 institutions in 1 country.
Funding
Abstract
When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the
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.