Evidence map›Paper›PMID 19184975›Full record

ArticleStatistics in medicine2009

Mediation analysis with principal stratification.

Robert Gallop, Dylan S Small, Julia Y Lin, Michael R Elliott, Marshall Joffe, Thomas R Ten Have

Registry-linked trialAbstract read
In one paragraph

Article in Statistics in medicine, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02129244 (A Nurse Case Management Intervention to Improve MDR-TB/HIV Co-Infection Outcomes), which is not on this map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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.

NCT02129244 naactive not recruitingnot on this mapstarted 2014, after this paper: background citation

A Nurse Case Management Intervention to Improve MDR-TB/HIV Co-Infection Outcomes

TypeinterventionalSponsorJohns Hopkins UniversityRan2014 to 2026Enrolled3,600ConditionsMDR-TB, HIVArmsNCM-Plus
3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Trial
  2. Article
  3. Semiparametric Latent Class Analysis of Recurrent Event Data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022
    Article
  4. Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021
    Article
  5. Instrumental variable analyses for causal inference: Application to multilevel analyses of the alliance-outcome relation.Psychotherapy research : journal of the Society for Psychotherapy Research · 2020
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Estimating controlled direct effects of restrictivefeeding practices in the 'Early dieting in girls' study.Journal of the Royal Statistical Society. Series C, Applied statistics · 2016
    Article
  11. Article
  12. Article
  13. Estimating the health benefit of reducing indoor air pollution in a randomized environmental intervention.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2015
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Comments: Should Principal Stratification Be Used to Study Mediational Processes?Journal of research on educational effectiveness · 2012
    Article
  19. On causal inference in the presence of interference.Statistical methods in medical research · 2012
    Article
  20. Conditioning on intermediates in perinatal epidemiology.Epidemiology (Cambridge, Mass.) · 2012
    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.

Robert GallopDepartment of Mathematics, Applied Statistics Program, West Chester University, 323B Anderson Hall, West Chester, PA 19383, U.S.A. rgallop@wcupa.edu
Dylan S Small
Julia Y Lin
Michael R Elliott
Marshall Joffe
Thomas R Ten Have

Funding

Causal Methods for Mediation and InteractionR01MH078016 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI JOFFE, MARSHALL M · 2007 to 2010
$1.3M
NIMH NIH HHS R01 MH078016NIMH NIH HHS R01MH-078016
6 · The paper itself

Abstract

In assessing the mechanism of treatment efficacy in randomized clinical trials, investigators often perform mediation analyses by analyzing if the significant intent-to-treat treatment effect on outcome occurs through or around a third intermediate or mediating variable: indirect and direct effects, respectively. Standard mediation analyses assume sequential ignorability, i.e. conditional on covariates the intermediate or mediating factor is randomly assigned, as is the treatment in a randomized clinical trial. This research focuses on the application of the principal stratification (PS) approach for estimating the direct effect of a randomized treatment but without the standard sequential ignorability assumption. This approach is used to estimate the direct effect of treatment as a difference between expectations of potential outcomes within latent subgroups of participants for whom the intermediate variable behavior would be constant, regardless of the randomized treatment assignment. Using a Bayesian estimation procedure, we also assess the sensitivity of results based on the PS approach to heterogeneity of the variances among these principal strata. We assess this approach with simulations and apply it to two psychiatric examples. Both examples and the simulations indicated robustness of our findings to the homogeneous variance assumption. However, simulations showed that the magnitude of treatment effects derived under the PS approach were sensitive to model mis-specification.

Indexed as

Data Interpretation, StatisticalTreatment OutcomeBayes TheoremModels, StatisticalOutcome Assessment, Health CareRandomized Controlled Trials as TopicSuicide Prevention

Identifiers

PMID19184975
PMCPMC2669107

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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.