Evidence map›Paper›PMID 35124836›Full record

ArticleStatistics in medicine2022

Bayesian set of best dynamic treatment regimes: Construction and sample size calculation for SMARTs with binary outcomes.

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

Abstract read
In one paragraph

Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Review
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

5 authors.

William J ArtmanDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.ORCID 0000-0002-0576-3686
Brent A JohnsonDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.
Kevin G LynchCenter for Clinical Epidemiology and Biostatistics (CCEB) and Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
James R McKayDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ashkan ErtefaieDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Funding

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 AA027571NIAAA NIH HHS R21AA027571NIDA NIH HHS R01 DA048764NIDA NIH HHS R01DA048764NINDS NIH HHS R61 NS120240NINDS NIH HHS R61NS120240
6 · The paper itself

Abstract

Sequential, multiple assignment, randomized trials (SMARTs) compare sequences of treatment decision rules called dynamic treatment regimes (DTRs). In particular, the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) SMART aimed to determine the best DTRs for patients with a substance use disorder. While many authors have focused on a single pairwise comparison, addressing the main goal involves comparisons of >2 DTRs. For complex comparisons, there is a paucity of methods for binary outcomes. We fill this gap by extending the multiple comparisons with the best (MCB) methodology to the Bayesian binary outcome setting. The set of best is constructed based on simultaneous credible intervals. A substantial challenge for power analysis is the correlation between outcome estimators for distinct DTRs embedded in SMARTs due to overlapping subjects. We address this using Robins' G-computation formula to take a weighted average of parameter draws obtained via simulation from the parameter posteriors. We use non-informative priors and work with the exact distribution of parameters avoiding unnecessary normality assumptions and specification of the correlation matrix of DTR outcome summary statistics. We conduct simulation studies for both the construction of a set of optimal DTRs using the Bayesian MCB procedure and the sample size calculation for two common SMART designs. We illustrate our method on the ENGAGE SMART. The R package SMARTbayesR for power calculations is freely available on the Comprehensive R Archive Network (CRAN) repository. An RShiny app is available at https://wilart.shinyapps.io/shinysmartbayesr/.

Indexed as

Research DesignBayes TheoremComputer SimulationHumansSample Sizebinary outcomedynamic treatment regimesmultiple comparisons with the bestpower analysissequential multiple assignment randomized trials

Identifiers

PMID35124836
PMCPMC9363159

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.