# Analyzing Sequential, Multiple Assignment, Randomized Trials in the Presence of Partial Compliance

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2020 · $383,812

## 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
and the Adaptive Treatment for Cocaine Dependence trials, sequential multiple assignment
randomized trials (SMART) designed to find a (personalized) rescue treatment for alcohol
or/and cocaine dependent patients. One of the main challenges in these trials is the high rate of
noncompliance to the assigned treatments. 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 data.
A SMART trial is a multi-stage trial that can inform the design of an adaptive treatment strategy
(ATS) which formalizes an individualized treatment plan and where current treatment strategy
can depend on a patient's past medical and treatment history. An optimal ATS is one that
maximizes a specified health outcome of interest. Existing methods in analyzing SMART data
are limited to intention-to-treat (ITT) analyses. That is the treatment effect at each stage is
estimated based on the treatment group to which an individual was randomized at that stage
regardless of whether the individual complied with their assigned treatment. One major concern
is that the relationship between the experimental manipulation and the outcome may be
confounded by treatment noncompliance.
We develop methodologies that can be used to adjust for noncompliance in analyzing data
collected in SMARTs. Specifically, we extend the principal strata framework and Bayesian
Copulas to multi-stage randomized trials setting and propose novel procedures that estimate the
mean outcome under different ATSs. We also propose a novel Bayesian machine learning
approach that can be used to construct deeply tailored (i.e., individualized) treatment strategies
that take into account patients' demographic factors, measures of mental health and alcohol
use, obsessive-compulsive drinking and alcohol craving scales, physical composite scores.
Finally, we will develop easy-to-use, publicly available open-source software leveraging the R
and Python languages that implements our methods. This will provide an expandable platform
that will assist researchers in developing new optimal ATSs for patients suffering from
alcoholism and other substance use disorders.

## Key facts

- **NIH application ID:** 10017030
- **Project number:** 5R01DA048764-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Ashkan Ertefaie
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $383,812
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017030

## Citation

> US National Institutes of Health, RePORTER application 10017030, Analyzing Sequential, Multiple Assignment, Randomized Trials in the Presence of Partial Compliance (5R01DA048764-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10017030. Licensed CC0.

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