# Optimal dynamic treatment strategies for controlling alcohol use: novel methods for selecting and incorporating effect modifiers

> **NIH NIH R21** · UNIVERSITY OF ROCHESTER · 2020 · $185,591

## Abstract

Project Summary:
The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the
type or the dose of treatment to accommodate the specific and changing needs of individuals. This
proposal is motivated by the Extending Treatment Effectiveness of Naltrexone (ExTENd) trial, a sequential
multiple assignment randomized trial (SMART) designed to find a (personalized) rescue treatment for
those who are non-responsive to initial Naltrexone. One of the main challenges in this trial is the presence
of the many variables available for consideration when making treatment decisions at each stage of the
trial. This feature has made it virtually impossible for investigators to fully explore the possibility of building
high quality treatment strategies using the data. Our overarching aim is to address this particular challenge
through developing and subsequently applying new statistical methods to the ExTENd trial data.
A SMART trial is a multi-stage trial that can inform the design of a dynamic treatment regime (DTR) which
formalizes an individualized treatment plan and where current treatment strategy can depend on a patient's
past medical and treatment history. An optimal DTR is one that maximizes a specified health outcome of
interest. Q-learning can be used with data from both SMARTs and observational studies to estimate an
optimal DTR. However, like other model-based approaches, model misspecification can seriously affect
the results and lead to the identification of suboptimal DTRs. The potential for misspecification increases
with the number of variables that may influence treatment decisions through, e.g., incorrect assumptions
on the relationship of variables to the outcome and the inclusion (exclusion) of unimportant (important)
variables. These features represent the main analytical challenges for the ExTENd trial.
We propose a new approach to Q-learning that leverages machine learning approaches to reduce the
chances of misspecifying the relationship between the expected outcome and a given set of variables. We
also develop a variable selection technique specifically designed for Q-learning that enables investigators
to select the important variables from a long list of possibilities (e.g., genetic and demographic information,
medical history over time) when estimating an optimal DTR. In both settings, we will develop new methods
for conducting valid inferences (e.g., confidence intervals and p-values), including when there exist patients
for whom treatment is neither beneficial nor harmful at a given decision stage (i.e., when an important
technical assumption, “uniqueness”, is violated). Finally, we will develop easy-to-use, publicly available
software in the R language that implements our methods. This will allow re-analysis of the ExTENd trial
data with a goal of constructing a DTR that improves upon the current rescue treatment strategy for those
non-responsive to initial Naltrexone. It will also provide a...

## Key facts

- **NIH application ID:** 9953933
- **Project number:** 5R21AA027571-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Ashkan Ertefaie
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $185,591
- **Award type:** 5
- **Project period:** 2019-06-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9953933

## Citation

> US National Institutes of Health, RePORTER application 9953933, Optimal dynamic treatment strategies for controlling alcohol use: novel methods for selecting and incorporating effect modifiers (5R21AA027571-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9953933. Licensed CC0.

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