JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 155
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306365
Title: Machine Learning Techniques for Constructing Tailoring Variables for Sequential Decisionmaking
Author(s): Daniel Almirall*+
Companies: University of Michigan
Address: 426 Thompson St., Suite 2204, Ann Arbor, MI, 48104-2321, United States
Keywords: adaptive intervention ; generalized boosted models ; unsupervised learning ; supervised learning

Chronic conditions often require that clinicians make sequential, individualized treatment decisions. A dynamic treatment regime (DTR) is a sequence of individually-tailored decision rules that use demographic information, severity and illness history, co-morbidities, and response to prior treatment as inputs and then recommend the type, modality, intensity or delivery of subsequent treatment(s) as outputs. A key step in developing effective DTRs is identifying tailoring variables to use as inputs in the decision rules. Tailoring variables are special types of pre-treatment moderators that pinpoint which treatment is best for whom. Existing methods do not explicitly (a) identify which measures (variable selection), or (b) combine them (feature construction) for use as tailoring variables. In this talk, we propose a framework for adapting and extending machine learning techniques (originally-developed for prediction) for use in tailoring variable selection and feature construction. Using an observational, longitudinal data set, we illustrate the approach by developing a DTR for determining the optimal duration/discontinuation of adolescent substance use treatment.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.