Estimation and Evaluation of the Resource-Constrained Optimal Dynamic Treatment Rule: An Application to HIV Care Retention

TL;DR

This paper discusses the resource-constrained optimal dynamic treatment rule (RC ODTR) algorithm, illustrating its application in HIV care to optimize resource allocation for cash transfers, highlighting benefits and practical implementation guidance.

Contribution

It introduces novel approaches for presenting and applying the RC ODTR algorithm, including a real-world HIV care case study demonstrating its utility and insights.

Findings

Deploying CCTs to 10% of the population yields clinical and monetary benefits.

Loosening constraints, not heterogeneity, explains incremental benefits.

Provides practical guidance and software for implementing the RC ODTR.

Abstract

The optimal strategy for deploying a treatment in a population may recommend giving all in the population that treatment. Such a strategy may not be feasible, especially in resource-limited settings. One approach for determining how to allocate a treatment in such settings is the resource-constrained optimal dynamic treatment rule (RC ODTR) SuperLearner algorithm, developed by Luedtke and van der Laan. In this paper, we describe this algorithm, offer various novel approaches for presenting the RC ODTR and its value in terms of benefit and cost, and provide practical guidance on implementing the algorithm (including software). In particular, we apply this method to the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV care (NCT02338739) trial to determine how to best allocate conditional cash transfers (CCTs) for increasing HIV care adherence given varying constraints on the proportion of people who can receive CCTs in the population, providing one of the few applied illustrations of this method and novel substantive findings. We find that there are clinical and monetary benefits to deploying CCTs to a small percent (e.g., 10\%) of the population compared to administering the care standard for all; however, results suggest that these incremental benefits are only due to the loosening of constraints, rather than a presence of treatment effect heterogeneity strong enough to drive a more efficient and effective constrained allocation approach.