Adjustment for selection bias in cohort studies: an application of a probit model with selectivity to life course epidemiology.
Sample attrition is potentially a source of bias in cohort studies. The outcome may not be observed in a considerable proportion of the subjects. This article proposes the application of a probit model with sample selection to handle the problem. Two equations are simultaneously estimated and their error terms allowed to correlate: one regressing an observed outcome on a set of baseline variables, another regressing the probability of the outcome being observed upon a set of (perhaps the same) baseline variables. The method was applied to a study of a birth cohort, half of whose members were interviewed again at age 26. Baseline variables were observed for all the subjects included. The focus was on the association between birth weight and mental health in adults. The probit model with sample selection revealed a stronger and more significant (P = 0.037) relation between birth weight and mental health than an ordinary probit regression model (P = 0.170). Interpretation and practical considerations are discussed.
Cheung
Y B
YB
Division of Clinical Trials & Epidemiological Sciences, National Cancer Centre, 11 Hospital Drive, Singapore 169610, Singapore. ctecyb@nccs.com.sg
D000328:Adult; D000704:Analysis of Variance; D015982:Bias; D001724:Birth Weight; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology*; D011336:Probability; D012107:Research Design / Q000592:standards*; D006113:United Kingdom / Q000453:epidemiology