Adaptive Treatment Strategies With Survival Outcomes: An Application to the Treatment of Type 2 Diabetes Using a Large Observational Database. Issue 5 (3rd January 2020)

• Record Type: Journal Article
• Authors: Simoneau, Gabrielle; Moodie, Erica E M; Azoulay, Laurent; Platt, Robert W
• Is Part Of: American journal of epidemiology. Volume 189:Issue 5(2020)
• Page Start: 461
• Page End: 469
• Publication Date: 2020-01-03
• Subjects: accelerated failure time models -- balancing weights -- causal inference -- censored data -- dynamic treatment regimens -- dynamic weighted survival modeling -- individualized treatment rules -- precision medicine; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwz272
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Sequences of treatments that adapt to a patient's changing condition over time are often needed for the management of chronic diseases. An adaptive treatment strategy (ATS) consists of personalized treatment rules to be applied through the course of a disease that input the patient's characteristics at the time of decision-making and output a recommended treatment. An optimal ATS is the sequence of tailored treatments that yields the best clinical outcome for patients sharing similar characteristics. Methods for estimating optimal adaptive treatment strategies, which must disentangle short- and long-term treatment effects, can be theoretically involved and hard to explain to clinicians, especially when the outcome to be optimized is a survival time subject to right-censoring. In this paper, we describe dynamic weighted survival modeling, a method for estimating an optimal ATS with survival outcomes. Using data from the Clinical Practice Research Datalink, a large primary-care database, we illustrate how it can answer an important clinical question about the treatment of type 2 diabetes. We identify an ATS pertaining to which drug add-ons to recommend when metformin in monotherapy does not achieve the therapeutic goals.