Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models. (19th July 2018)
- Record Type:
- Journal Article
- Title:
- Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models. (19th July 2018)
- Main Title:
- Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models
- Authors:
- Henderson, Nicholas C
Louis, Thomas A
Rosner, Gary L
Varadhan, Ravi - Abstract:
- Summary: Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patientsSummary: Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect. … (more)
- Is Part Of:
- Biostatistics. Volume 21:Number 1(2020)
- Journal:
- Biostatistics
- Issue:
- Volume 21:Number 1(2020)
- Issue Display:
- Volume 21, Issue 1 (2020)
- Year:
- 2020
- Volume:
- 21
- Issue:
- 1
- Issue Sort Value:
- 2020-0021-0001-0000
- Page Start:
- 50
- Page End:
- 68
- Publication Date:
- 2018-07-19
- Subjects:
- Dirichlet process mixture -- Ensemble methods -- Heterogeneity of treatment effect -- Interaction -- Personalized medicine -- Subgroup analysis
Medical statistics -- Periodicals
Biometry -- Periodicals
Health risk assessment -- Periodicals
Medicine -- Research -- Statistical methods -- Periodicals
610.727 - Journal URLs:
- http://www3.oup.co.uk/biosts ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/biostatistics/kxy028 ↗
- Languages:
- English
- ISSNs:
- 1465-4644
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2089.628000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23538.xml