Evidence map›Paper›PMID 33728637›Full record

ArticleBiometrics2022

Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders.

Daniel O Scharfstein, Jon Steingrimsson, Aidan McDermott, Chenguang Wang, Souvik Ray, Aimee Campbell, Edward Nunes, Abigail Matthews

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Daniel O ScharfsteinDepartment of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah, USA.ORCID 0000-0001-7482-9653
Jon SteingrimssonDepartment of Biostatistics, Brown University, Providence, Rhode Island, USA.ORCID 0000-0003-2116-9377
Aidan McDermottDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Chenguang WangDivision of Biostatistics and Bioinformatics, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-7085-3303
Souvik RayDepartment of Statistics, Stanford University, Stanford, California, USA.
Aimee CampbellDepartment of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, USA.
Edward NunesDepartment of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, USA.
Abigail MatthewsThe EMMES Company LLC, Rockville, Maryland, USA.

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
Missing Data Matters: Substance Use Disorder Clinical TrialsR01DA046534 · NIDA · UNIVERSITY OF UTAH · PI SCHARFSTEIN, DANIEL OSCAR · 2018 to 2020
$1.4M
NCATS NIH HHS UL1 TR002538NIDA NIH HHS R01 DA046534
6 · The paper itself

Abstract

In this paper, we present a method for conducting global sensitivity analysis of randomized trials in which binary outcomes are scheduled to be collected on participants at prespecified points in time after randomization and these outcomes may be missing in a nonmonotone fashion. We introduce a class of missing data assumptions, indexed by sensitivity parameters, which are anchored around the missing not at random assumption introduced by Robins (Statistics in Medicine, 1997). For each assumption in the class, we establish that the joint distribution of the outcomes is identifiable from the distribution of the observed data. Our estimation procedure uses the plug-in principle, where the distribution of the observed data is estimated using random forests. We establish

Indexed as

Research DesignSubstance-Related DisordersComputer SimulationData Interpretation, StatisticalHumansRandomized Controlled Trials as Topicexponential tiltingmissing not at randomrandom forests

Identifiers

PMID33728637
PMCPMC10392106

What OpenQuestion holds

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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.