Objective To investigate whether inverse probability of participation weighting (IPPW) using register data on sociodemographic and disease history variables can improve external validity in a cohort study with selective participation. Study Design and Setting We fitted various IPPW models by logistic regression using register data for the participants (n = 1,111) and nonparticipants (n = 1,132) of a Swedish cohort study. For each of six diagnostic groups, we then estimated (1) weighted disease prevalence proportions and (2) weighted cross-sectional associations (odds ratios) between sociodemographic variables and disease prevalence. Using register data on the remaining individuals of the entire study population of men and women aged 50-64 years (n = 22,259), we addressed how the choice of variables used for IPPW influenced estimation errors. Results Disease prevalence proportions were generally underestimated in the absence of IPPW but became markedly closer to population values after IPPW using sociodemographic variables. We found limited evidence of selective participation bias in association estimates, but IPPW improved external validity when bias was present. Conclusions IPPW using sociodemographic register data can improve the external validity of disease prevalence estimates in cohort studies with selective participation. The performance of IPPW for association estimates merits further investigations in longitudinal settings and larger cohorts.
