ArticleBiometrika2014
Interactive model building for
Article in Biometrika, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- A multiagent reinforcement learning algorithm for personalized recommendations in bipolar disorder.PNAS nexus · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial.NPJ digital medicine · 2024Article
- Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity.Statistical methods in medical research · 2023Article
- The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions.The international journal of biostatistics · 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
- Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.Journal of machine learning research : JMLR · 2022Article
- Article
- Improved doubly robust estimation in learning optimal individualized treatment rules.Journal of the American Statistical Association · 2021Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity.Journal of the American Statistical Association · 2021Article
- Personalized Policy Learning using Longitudinal Mobile Health Data.Journal of the American Statistical Association · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- Constructing dynamic treatment regimes with shared parameters for censored data.Statistics in medicine · 2020Article
- Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).Psychological methods · 2020Article
- Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.Journal of the American Statistical Association · 2020Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
- A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.Biometrical journal. Biometrische Zeitschrift · 2019Article
- Optimal treatment allocations in space and time for on-line control of an emerging infectious disease.Journal of the Royal Statistical Society. Series C, Applied statistics · 2018Article
- Estimation and evaluation of linear individualized treatment rules to guarantee performance.Biometrics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Evidence-based rules for optimal treatment allocation are key components in the quest for efficient, effective health care delivery. Q-learning, an approximate dynamic programming algorithm, is a popular method for estimating optimal sequential decision rules from data. Q-learning requires the modeling of nonsmooth, nonmonotone transformations of the data, complicating the search for adequately expressive, yet parsimonious, statistical models. The default Q-learning working model is multiple linear regression, which is not only provably misspecified under most data-generating models, but also results in nonregular regression estimators, complicating inference. We propose an alternative strategy for estimating optimal sequential decision rules for which the requisite statistical modeling does not depend on nonsmooth, nonmonotone transformed data, does not result in nonregular regression estimators, is consistent under a broader array of data-generation models than Q-learning, results in estimated sequential decision rules that have better sampling properties, and is amenable to established statistical approaches for exploratory data analysis, model building, and validation. We derive the new method, IQ-learning, via an interchange in the order of certain steps in Q-learning. In simulated experiments IQ-learning improves on Q-learning in terms of integrated mean squared error and power. The method is illustrated using data from a study of major depressive disorder.
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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.