ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2023
Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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
9 citing papers in PubMed.
- The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions.American journal of preventive medicine · 2007Trial
- Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens.Journal of the American Statistical Association · 2024Article
- Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity.Statistical methods in medical research · 2023Article
- When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.The American statistician · 2020Article
- ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL.Statistica Sinica · 2018Article
- High-Dimensional Inference for Personalized Treatment Decision.Electronic journal of statistics · 2018Article
- Collaborative double robust targeted maximum likelihood estimation.The international journal of biostatistics · 2010Article
- Targeted maximum likelihood based causal inference: Part I.The international journal of biostatistics · 2010Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time. We propose modified Q-learning with generalized estimating equations to address these limitations and apply it to the M-bridge trial, which evaluates adaptive interventions to prevent problematic drinking among college freshmen. Simulation studies demonstrate our proposed method improves efficiency and robustness.
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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.