ArticleJournal of the American Statistical Association2017
Interactive Q-learning for Quantiles.
Article in Journal of the American Statistical Association, 2017. 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.
- Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.Statistical methods in medical research · 2024Article
- Variable selection for individualised treatment rules with discrete outcomes.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring.Biometrika · 2023Article
- A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity.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
- Performance Guarantees for Policy Learning.Annales de l'I.H.P. Probabilites et statistiques · 2020Article
- Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.Journal of the American Statistical Association · 2020Article
- Robust regression for optimal individualized treatment rules.Statistics in medicine · 2019Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
- Efficient augmentation and relaxation learning for individualized treatment rules using observational data.Journal of machine learning research : JMLR · 2019Article
- A Bayesian nonparametric approach to causal inference on quantiles.Biometrics · 2018Article
- Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.Statistical methods in medical research · 2017Article
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
A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms.
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.