ArticleJournal of biopharmaceutical statistics2018
Estimating the Optimal Personalized Treatment Strategy Based on Selected Variables to Prolong Survival via Random Survival Forest with Weighted Bootstrap.
Article in Journal of biopharmaceutical statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Deep survival analysis for interpretable time-varying prediction of preeclampsia risk.Journal of biomedical informatics · 2024Article
- Estimating the optimal individualized treatment rule from a cost-effectiveness perspective.Biometrics · 2022Article
- Estimating heterogeneous survival treatment effect in observational data using machine learning.Statistics in medicine · 2021Article
- Enabling Counterfactual Survival Analysis with Balanced Representations.ACM CHIL 2021 : proceedings of the 2021 ACM Conference on Health, Inference, and Learning : April 8-9, 2021, Virtual Event. ACM Conference on Health, Inference, and Learning (2021 : Online) · 2021Article
- Quantifying treatment effects using the personalized chance of longer survival.Statistics in medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
A personalized treatment policy requires defining the optimal treatment for each patient based on their clinical and other characteristics. Here we consider a commonly encountered situation in practice, when analyzing data from observational cohorts, that there are auxiliary variables which affect both the treatment and the outcome, yet these variables are not of primary interest to be included in a generalizable treatment strategy. Furthermore, there is not enough prior knowledge of the effect of the treatments or of the importance of the covariates for us to explicitly specify the dependency between the outcome and different covariates, thus we choose a model that is flexible enough to accommodate the possibly complex association of the outcome on the covariates. We consider observational studies with a survival outcome and propose to use Random Survival Forest with Weighted Bootstrap (RSFWB) to model the counterfactual outcomes while marginalizing over the auxiliary covariates. By maximizing the restricted mean survival time, we estimate the optimal regime for a target population based on a selected set of covariates. Simulation studies illustrate that the proposed method performs reliably across a range of different scenarios. We further apply RSFWB to a prostate cancer study.
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.