Constructing Evidence-Based Tailoring Variables for Adaptive Interventions

TL;DR

This paper discusses how to empirically develop and select tailoring variables for adaptive interventions, emphasizing the use of randomized experiments to obtain causal evidence for decision-making.

Contribution

It introduces a systematic framework for developing tailoring variables and highlights the advantages of experimental designs over secondary data analysis.

Findings

Secondary data can inform tailoring variables but require assumptions.

Experimental designs like multi-arm RCTs provide direct causal evidence.

More research needed on designing effective, scalable tailoring variables.

Abstract

Background: Adaptive interventions provide a guide for using ongoing information about individuals to decide whether and how to modify the type, amount, delivery modality, or timing of treatment, to improve intervention effectiveness while reducing cost and burden. The variables that inform treatment modification decisions are called tailoring variables. Specifying a tailoring variable requires describing what should be measured, when to measure it, when the measure should be used to make decisions, and what cutoffs should be used in making decisions. These questions are causal and prescriptive (what to do, when), not merely predictive. They involve tradeoffs between specificity and sensitivity, and between waiting for sufficient information versus intervening quickly. Purpose: There is little specific guidance in the literature on how to empirically choose tailoring variables, including cutoffs, measurement times, and decision times. Methods: We review possible approaches for comparing potential tailoring variables and propose a framework for systematically developing tailoring variables. Results: Although secondary observational data can be used to select tailoring variables, additional assumptions are needed. A specifically designed experiment for optimization (an optimization randomized controlled trial), e.g., a multi-arm randomized trial, sequential multiple assignment randomized trial, factorial experiment, or hybrid design, may provide a more direct way to answer these questions. Conclusions: Using randomization directly to inform tailoring variables provides the most direct causal evidence but requires more effort and resources than secondary data analysis. More research is needed on how best to design tailoring variables for effective, scalable interventions.