Evidence map›Paper›PMID 30537944›Full record

ArticleBMC medical research methodology2018

Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts.

Meredith B Brooks, Salmaan Keshavjee, Irina Gelmanova, Nataliya A Zemlyanaya, Carole D Mitnick, Justin Manjourides

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 41% of its field
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 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  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 at 3 institutions in 1 country.

Meredith B BrooksDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA. Meredith_Brooks@hms.harvard.edu.ORCID 0000-0003-1022-2515
Salmaan KeshavjeeDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA.
Irina GelmanovaPartners In Health, 800 Boylston Street, Suite 300, Boston, MA, 02199, USA.
Nataliya A ZemlyanayaPartners In Health Russia, Moscow, Russian Federation.
Carole D MitnickDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA.
Justin ManjouridesDepartment of Health Sciences, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA.
Northeastern University · USPartners In Health · USBrigham and Women's Hospital · US

Funding

The Harvard TH Chan School of Public Health Center for Work, Health and WellbeingU19OH008861 · OH · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI SORENSEN, GLORIAN C · 2007 to 2025
$23.9M
NIOSH CDC HHS U19 OH008861
6 · The paper itself

Abstract

backgroundMultidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes. When using Cox proportional hazards models to analyze these cohorts, this leads to censoring subjects at the time of the initial treatment outcome, instead of them providing full survival data. This may violate the non-informative censoring assumption of the model and may produce biased effect estimates. To address this problem, we develop a tool to predict vital status at the end of a cohort period using the initial treatment outcome and assess its ability to reduce bias in treatment effect estimates.

methodsWe derive and apply a logistic regression model to predict vital status at the end of the cohort period and modify the unobserved survival outcomes to better match the predicted survival experience of study subjects. We compare hazard ratio estimates for effect of an aggressive treatment regimen from Cox proportional hazards models using time to initial treatment outcome, predicted vital status, and true vital status at the end of the cohort period.

resultsModels fit from initial treatment outcomes underestimate treatment effects by up to 22.1%, while using predicted vital status reduced this bias by 5.4%. Models utilizing the predicted vital status produce effect estimates consistently stronger and closer to the true treatment effect than estimates produced by models using the initial treatment outcome.

conclusionsAlthough studies often use initial treatment outcomes to estimate treatment effects, this may violate the non-informative censoring assumption of the Cox proportional hazards model and result in biased treatment effect estimates. Using predicted vital status at the end of the cohort period may reduce this bias in the analyses of MDR-TB treatment cohorts, yielding more accurate, and likely larger, treatment effect estimates. Further, these larger effect sizes can have downstream impacts on future study design by increasing power and reducing sample size needs.

Indexed as

AdultCohort StudiesFemaleHumansLogistic ModelsMaleMiddle AgedOutcome Assessment, Health CarePrognosisProportional Hazards ModelsROC CurveSurvival AnalysisTuberculosis, Multidrug-ResistantYoung AdultCox proportional hazardsMultidrug-resistant tuberculosisPrediction models

Identifiers

PMID30537944
PMCPMC6290510
OpenAlexW2904353288

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.