ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2023
Transfer learning of individualized treatment rules from experimental to real-world data.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 18 citations in OpenAlex.
- Transportable inference using target population summary statistics under covariate shift.Biometrics · 2026Article
- Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules Under Effect Heterogeneity.Statistics in medicine · 2026Article
- Robust transfer learning for individualized treatment rules in the presence of missing data.Biostatistics (Oxford, England) · 2025Article
- Response to comment on "Transporting survival of an HIV clinical trial to the external target populations by Lee et al. (2024)".Journal of biopharmaceutical statistics · 2025Article
- Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.Statistical science : a review journal of the Institute of Mathematical Statistics · 2024Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
- RWD-INTEGRATED RANDOMIZED CLINICAL TRIAL ANALYSIS.Biopharmaceutical report · 2022Article
- Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population.Journal of causal inference · 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
2 authors at 1 institution in 1 country.
Funding
Abstract
Individualized treatment effect lies at the heart of precision medicine. Interpretable individualized treatment rules (ITRs) are desirable for clinicians or policymakers due to their intuitive appeal and transparency. The gold-standard approach to estimating the ITRs is randomized experiments, where subjects are randomized to different treatment groups and the confounding bias is minimized to the extent possible. However, experimental studies are limited in external validity because of their selection restrictions, and therefore the underlying study population is not representative of the target real-world population. Conventional learning methods of optimal interpretable ITRs for a target population based only on experimental data are biased. On the other hand, real-world data (RWD) are becoming popular and provide a representative sample of the target population. To learn the generalizable optimal interpretable ITRs, we propose an integrative transfer learning method based on weighting schemes to calibrate the covariate distribution of the experiment to that of the RWD. Theoretically, we establish the risk consistency for the proposed ITR estimator. Empirically, we evaluate the finite-sample performance of the transfer learner through simulations and apply it to a real data application of a job training program.
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.