Using decision lists to construct interpretable and parsimonious treatment regimes.

TitleUsing decision lists to construct interpretable and parsimonious treatment regimes.
Publication TypeJournal Article
Year of Publication2015
AuthorsZhang, Yichi, Eric B. Laber, Anastasios Tsiatis, and Marie Davidian
Date Published2015 Dec
KeywordsBiometry, Breast Neoplasms, Clinical Protocols, Clinical Trials as Topic, Computer Simulation, Decision Trees, Depression, Evidence-Based Medicine, Female, Humans, Models, Statistical, Precision Medicine

A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high-quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long-term, joint effort of statisticians and clinical scientists. In this collaborative process, the statistician must integrate clinical science into the statistical models underlying a treatment regime and the clinician must scrutinize the estimated treatment regime for scientific validity. To facilitate meaningful information exchange, it is important that estimated treatment regimes be interpretable in a subject-matter context. We propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if-then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for broad application in practice. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulation experiments. The proposed method is illustrated with data from two clinical trials.

Alternate JournalBiometrics
Original PublicationUsing decision lists to construct interpretable and parsimonious treatment regimes.
PubMed ID26193819
PubMed Central IDPMC4715597
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 HL118336 / HL / NHLBI NIH HHS / United States