A method to visualize and adjust for selection bias in prevalent cohort studies

Anna Törner, Paul Dickman, Ann Sofi Duberg, Sigurdur Kristinsson, Ola Landgren, Magnus Björkholm, Åke Svensson

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Selection bias and confounding are concerns in cohort studies where the reason for inclusion of subjects in the cohort may be related to the outcome of interest. Selection bias in prevalent cohorts is often corrected by excluding observation time and events during the first time period after inclusion in the cohort. This time period must be chosen carefully-long enough to minimize selection bias but not too long so as to unnecessarily discard observation time and events. A novel method visualizing and estimating selection bias is described and exemplified by using 2 real cohort study examples: a study of hepatitis C virus infection and a study of monoclonal gammopathy of undetermined significance. The method is based on modeling the hazard for the outcome of interest as a function of time since inclusion in the cohort. The events studied were "hospitalizations for kidney-related disease" in the hepatitis C virus cohort and "death" in the monoclonal gammopathy of undetermined significance cohort. Both cohorts show signs of considerable selection bias as evidenced by increased hazard in the time period after inclusion in the cohort. The method was very useful in visualizing selection bias and in determining the initial time period to be excluded from the analyses.

Original languageEnglish (US)
Pages (from-to)969-976
Number of pages8
JournalAmerican journal of epidemiology
Volume174
Issue number8
DOIs
StatePublished - Oct 15 2011
Externally publishedYes

Keywords

  • cohort studies
  • cubic spline
  • epidemiologic methods
  • hepatitis C
  • monoclonal gammopathy of undetermined significance
  • selection bias

ASJC Scopus subject areas

  • Epidemiology

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