A Method to Visualize and Adjust for Selection Bias in Prevalent Cohort Studies

• Published in: American journal of epidemiology Vol. 174; no. 8; pp. 969 - 976
• Main Authors: Törner, Anna, Dickman, Paul, Duberg, Ann-Sofi, Kristinsson, Sigurdur, Landgren, Ola, Björkholm, Magnus, Svensson, Åke
• Format: Journal Article
• Language: English
• Published: Cary, NC Oxford University Press 15.10.2011; Oxford Publishing Limited (England)
• Subjects: Bias; Biological and medical sciences; Cohort Studies; Cross-Sectional Studies - methods; Disease Progression; Epidemiologic Research Design; Epidemiology; Folkhälsovetenskap; General aspects; Hepatitis; Hepatitis C - epidemiology; Human viral diseases; Humans; Immunodeficiencies. Immunoglobulinopathies; Immunoglobulinopathies; Immunopathology; Infectious diseases; matematisk statistik; Mathematical Statistics; Medical sciences; Miscellaneous; Paraproteinemias - epidemiology; Proportional Hazards Models; Public health; Public health. Hygiene; Public health. Hygiene-occupational medicine; Selection; Selection Bias; Sweden; Time Factors; Viral diseases; Viral hepatitis; Sweden; selection bias; cohort studies; cubic spline; monoclonal gammopathy of undetermined significance; epidemiologic methods; hepatitis C; Immunopathology; Prevalence; Methodology; Hepatic disease; Method; Epidemiology; Monoclonal gammopathy of undetermined significance; Infection; Immunoglobulinopathy; Viral disease; Cohort study; Digestive diseases; Selection criterion; Methodological bias; Public health; Viral hepatitis C
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwr211

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.