Evidence map›Paper›PMID 40771550›Full record

ArticleThe annals of applied statistics2024

A MARGINAL STRUCTURAL MODEL FOR PARTIAL COMPLIANCE IN SMARTS.

By William J Artman, Indrabati Bhattacharya, Ashkan Ertefaie, Kevin G Lynch, James R McKay, Brent A Johnson

Abstract read
In one paragraph

Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

By William J ArtmanDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center.
Indrabati BhattacharyaDepartment of Statistics, Florida State University.
Ashkan ErtefaieDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center.
Kevin G LynchCenter for Clinical Epidemiology and Biostatistics (CCEB) and Department of Psychiatry, University of Pennsylvania.
James R McKayDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania.
Brent A JohnsonDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center.

Funding

Advancing Personalized Medicine in PD Using Harmonized Multi-Site Clinical DataR33NS120240 · NINDS · UNIVERSITY OF ROCHESTER · PI ERTEFAIE, ASHKAN, MCDERMOTT, MICHAEL P · 2022 to 2024
$1.7M
Analyzing Sequential, Multiple Assignment, Randomized Trials in the Presence of Partial ComplianceR01DA048764 · NIDA · UNIVERSITY OF ROCHESTER · PI ERTEFAIE, ASHKAN · 2019 to 2022
$1.6M
Advancing personalized medicine in PD using harmonized multi-site clinical dataR61NS120240 · NINDS · UNIVERSITY OF ROCHESTER · PI ERTEFAIE, ASHKAN, MCDERMOTT, MICHAEL P · 2020 to 2021
$1.1M
Optimal dynamic treatment strategies for controlling alcohol use: novel methods for selecting and incorporating effect modifiersR21AA027571 · NIAAA · UNIVERSITY OF ROCHESTER · PI ERTEFAIE, ASHKAN · 2019 to 2020
$423k
NIAAA NIH HHS R21 AA027571NIDA NIH HHS R01 DA048764NINDS NIH HHS R33 NS120240NINDS NIH HHS R61 NS120240
6 · The paper itself

Abstract

The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type and/or the dose of treatment to accommodate the specific and changing needs of individuals. The Adaptive Treatment for Alcohol and Cocaine Dependence study (ENGAGE) is a sequential multiple assignment randomized trial (SMART) that provided longitudinal data for constructing dynamic treatment regimes (DTRs) to improve patients' engagement in therapy. However, the high rate of noncompliance and lack of analytic tools to account for noncompliance has impeded researchers from using the data to achieve the main goal of the trial; namely, construction of individually tailored DTRs. We address this by defining our target parameter as the mean outcome under different DTRs for potential compliance strata and propose a marginal structural model with principal stratification to estimate this quantity. We model the principal strata using a Bayesian semiparametric approach. An important feature of our work is that we consider partial rather than binary compliance strata, which is more relevant in longitudinal studies. We assess the performance of our method through simulation. We illustrate its application on ENGAGE and demonstrate the optimal DTRs depend on compliance strata compared with ignoring compliance information as in intention-to-treat analyses.

Indexed as

Dynamic treatment regimemarginal structural modelsnonparametric Bayespartial complianceprincipal stratificationsequential multiple assignment randomized trial

Identifiers

PMID40771550
PMCPMC12326262

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.