ArticleBiometrics2015
Reader reaction to "a robust method for estimating optimal treatment regimes" by Zhang et al. (2012).
Article in Biometrics, 2015. 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.
- Model selection for survival individualized treatment rules using the jackknife estimator.BMC medical research methodology · 2022Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- Predictive approaches to heterogeneous treatment effects: a scoping review.BMC medical research methodology · 2020Article
- The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.Annals of internal medicine · 2020Article
- Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups.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
- Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.Biometrics · 2018Article
- Interpretable Dynamic Treatment Regimes.Journal of the American Statistical Association · 2018Article
- Comment.Journal of the American Statistical Association · 2016Article
- Using decision lists to construct interpretable and parsimonious treatment regimes.Biometrics · 2015Article
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Authors and funding
3 authors.
Funding
Abstract
A recent article (Zhang et al., 2012, Biometrics 168, 1010-1018) compares regression based and inverse probability based methods of estimating an optimal treatment regime and shows for a small number of covariates that inverse probability weighted methods are more robust to model misspecification than regression methods. We demonstrate that using models that fit the data better reduces the concern about non-robustness for the regression methods. We extend the simulation study of Zhang et al. (2012, Biometrics 168, 1010-1018), also considering the situation of a larger number of covariates, and show that incorporating random forests into both regression and inverse probability weighted based methods improves their properties.
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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.