ArticleStatistics in medicine2014
Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Statistical Methods to Adjust for Treatment Switching in Real-World Clinical Studies: A Scoping Review and Descriptive Comparison.Clinical pharmacology and therapeutics · 2025Article
- Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.Statistical methods in medical research · 2024Article
- Trends in the survival benefit of repeat kidney transplantation over the past 3 decades.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2023Article
- Matching with time-dependent treatments: A review and look forward.Statistics in medicine · 2020Review
- Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies.Statistical methods in medical research · 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
- Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models.Biometrics · 2017Article
- Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies.American journal of epidemiology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
For patients who were previously treated for prostate cancer, salvage hormone therapy is frequently given when the longitudinal marker prostate-specific antigen begins to rise during follow-up. Because the treatment is given by indication, estimating the effect of the hormone therapy is challenging. In a previous paper we described two methods for estimating the treatment effect, called two-stage and sequential stratification. The two-stage method involved modeling the longitudinal and survival data. The sequential stratification method involves contrasts within matched sets of people, where each matched set includes people who did and did not receive hormone therapy. In this paper, we evaluate the properties of these two methods and compare and contrast them with the marginal structural model methodology. The marginal structural model methodology involves a weighted survival analysis, where the weights are derived from models for the time of hormone therapy. We highlight the different conditional and marginal interpretations of the quantities being estimated by the three methods. Using simulations that mimic the prostate cancer setting, we evaluate bias, efficiency, and accuracy of estimated standard errors and robustness to modeling assumptions. The results show differences between the methods in terms of the quantities being estimated and in efficiency. We also demonstrate how the results of a randomized trial of salvage hormone therapy are strongly influenced by the design of the study and discuss how the findings from using the three methodologies can be used to infer the results of a trial.
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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.