Title | DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Jiang, Runchao, Wenbin Lu, Rui Song, Michael G. Hudgens, and Sonia Naprvavnik |
Journal | Ann Appl Stat |
Volume | 11 |
Issue | 3 |
Pagination | 1763-1786 |
Date Published | 2017 Sep |
ISSN | 1932-6157 |
Abstract | In many biomedical settings, assigning every patient the same treatment may not be optimal due to patient heterogeneity. Individualized treatment regimes have the potential to dramatically improve clinical outcomes. When the primary outcome is censored survival time, a main interest is to find optimal treatment regimes that maximize the survival probability of patients. Since the survival curve is a function of time, it is important to balance short-term and long-term benefit when assigning treatments. In this paper, we propose a doubly robust approach to estimate optimal treatment regimes that optimize a user specified function of the survival curve, including the restricted mean survival time and the median survival time. The empirical and asymptotic properties of the proposed method are investigated. The proposed method is applied to a data set from an ongoing HIV/AIDS clinical observational study conducted by the University of North Carolina (UNC) Center of AIDS Research (CFAR), and shows the proposed methods significantly improve the restricted mean time of the initial treatment duration. Finally, the proposed methods are extended to multi-stage studies. |
DOI | 10.1214/17-AOAS1057 |
Alternate Journal | Ann Appl Stat |
Original Publication | Doubly robust estimation of optimal treatment regimes for survival data-with application to an HIV/AIDS study. |
PubMed ID | 29308102 |
PubMed Central ID | PMC5749433 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States P30 AI050410 / AI / NIAID NIH HHS / United States R01 AI029168 / AI / NIAID NIH HHS / United States R37 AI029168 / AI / NIAID NIH HHS / United States |