ArticleBiometrics2017
Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
- Estimating the causal effects of multiple intermittent treatments with application to COVID-19.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Estimating the optimal individualized treatment rule from a cost-effectiveness perspective.Biometrics · 2022Article
- Ranking tailoring variables for constructing individualized treatment rules: an application to schizophrenia.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- Estimating the optimal timing of surgery by imputing potential outcomes.Statistics in medicine · 2021Article
- Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.Statistics in medicine · 2021Article
- A scoping review of studies using observational data to optimise dynamic treatment regimens.BMC medical research methodology · 2021Article
- Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model.American journal of cancer research · 2020Article
- Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death.Biometrics · 2019Article
- Estimating the Optimal Personalized Treatment Strategy Based on Selected Variables to Prolong Survival via Random Survival Forest with Weighted Bootstrap.Journal of biopharmaceutical statistics · 2018Article
- A Framework for Treatment Decision Making at Prostate Cancer Recurrence.Medical decision making : an international journal of the Society for Medical Decision Making · 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
Prostate cancer patients are closely followed after the initial therapy and salvage treatment may be prescribed to prevent or delay cancer recurrence. The salvage treatment decision is usually made dynamically based on the patient's evolving history of disease status and other time-dependent clinical covariates. A multi-center prostate cancer observational study has provided us data on longitudinal prostate specific antigen (PSA) measurements, time-varying salvage treatment, and cancer recurrence time. These data enable us to estimate the best dynamic regime of salvage treatment, while accounting for the complicated confounding of time-varying covariates present in the data. A Random Forest based method is used to model the probability of regime adherence and inverse probability weights are used to account for the complexity of selection bias in regime adherence. The optimal regime is then identified by the largest restricted mean survival time. We conduct simulation studies with different PSA trends to mimic both simple and complex regime adherence mechanisms. The proposed method can efficiently accommodate complex and possibly unknown adherence mechanisms, and it is robust to cases where the proportional hazards assumption is violated. We apply the method to data collected from the observational study and estimate the best salvage treatment regime in managing the risk of prostate cancer recurrence.
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.