Evidence map›Paper›PMID 25868551›Full record

ArticleAmerican journal of epidemiology2015

Controlling Time-Dependent Confounding by Health Status and Frailty: Restriction Versus Statistical Adjustment.

Leah J McGrath, Alan R Ellis, M Alan Brookhart

Abstract read
In one paragraph

Article in American journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Propensity Score Methods for Confounding Control in Observational Studies of Therapeutics for COVID-19 Infection.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Comparative Effectiveness of High-Dose Versus Standard-Dose Influenza Vaccine Among Patients Receiving Maintenance Hemodialysis.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2020
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. 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

3 authors.

Leah J McGrath
Alan R Ellis
M Alan Brookhart

Funding

Propensity Scores and Preventive Drug Use in the ElderlyR01AG023178 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STURMER, TIL · 2005 to 2014
$2.7M
A retrospective cohort study of the safety of testosterone therapy in older menR01AG042845 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BROOKHART, MAURICE ALAN · 2012 to 2013
$476k
Patterns of use, comparative effectiveness, and safety of rotavirus vaccinesR21HD080214 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BROOKHART, MAURICE ALAN · 2014 to 2015
$413k
NIA NIH HHS R01 AG023178NIA NIH HHS R01 AG042845NICHD NIH HHS R21 HD080214NICHD NIH HHS R21HD080214
6 · The paper itself

Abstract

Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illness. We explored bias reduction methods while estimating vaccine effectiveness in a cohort of adult hemodialysis patients. Using the United States Renal Data System and linked data from a commercial dialysis provider, we estimated vaccine effectiveness using a Cox proportional hazards marginal structural model of all-cause mortality before and during 3 influenza seasons in 2005/2006 through 2007/2008. To improve confounding control, we added frailty indicators to the model, measured time-varying confounders at different time intervals, and restricted the sample in multiple ways. Crude and baseline-adjusted marginal structural models remained strongly biased. Restricting to a healthier population removed some unmeasured confounding; however, this reduced the sample size, resulting in wide confidence intervals. We estimated an influenza vaccine effectiveness of 9% (hazard ratio = 0.91, 95% confidence interval: 0.72, 1.15) when bias was minimized through cohort restriction. In this study, the healthy-user bias could not be controlled through statistical adjustment; however, sample restriction reduced much of the bias.

Indexed as

Confounding Factors, EpidemiologicHealth StatusInfluenza VaccinesKidney Failure, ChronicAgedCohort StudiesFemaleHumansMaleMiddle AgedMortalityProportional Hazards ModelsInfluenza Vaccinesbias (epidemiology)confounding factors (epidemiology)influenza vaccinesrenal dialysis

Identifiers

PMID25868551
PMCPMC4479111

What OpenQuestion holds

Textmetadata
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.