An adaptive intervention is a sequence of individually-tailored decisions rules that specify whether, how or when to alter the intensity, type or dosage of treatment at critical decision points in the course of care. Adaptive interventions (often referred to as dynamic treatment regimes) provide clinicians with a guide for how to adapt and re-adapt treatment over time, in response to the changing needs or circumstances of the individual. They can be used as a guide to individualized (personalized) clinical practice. Sequential, multiple-assignment randomized trials (SMART) were developed explicitly for the purpose of constructing high-quality adaptive interventions using experimental design principles. Most SMARTs have a number of adaptive interventions embedded within them. A common scientific aim is the comparison of study outcomes between these adaptive interventions. In this talk, we review adaptive interventions and SMART, and we report on recent work using a weighted-and-replicated (WR) regression estimator that can be used to compare the adaptive interventions embedded in a SMART. We will discuss simple ways to improve the statistical efficiency of the WR estimator. The methodology will be illustrated using data from a SMART study aimed at developing an adaptive intervention to improve spoken communication in minimally verbal children with autism.
Dept of Biostatistics
biostats [at] columbia [dot] edu