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Correcting for selective participation in cohort studies using auxiliary register data without identification of non-participants

Journal article
Authors Carl Bonander
A. Nilsson
Göran Bergström
J. Bjork
Ulf Strömberg
Published in Scandinavian Journal of Public Health
Pages 8
ISSN 1403-4948
Publication year 2020
Published at Institute of Medicine, School of Public Health and Community Medicine
Institute of Medicine, Department of Molecular and Clinical Medicine
Pages 8
Language en
Links dx.doi.org/10.1177/1403494819890784
Keywords External validity, selection bias, transportability, non-response, inverse probability weighting, generalizing evidence, transportability, probability, trials, Public, Environmental & Occupational Health
Subject categories Clinical Medicine

Abstract

Aims: Selective participation may hamper the validity of population-based cohort studies. The resulting bias can be alleviated by linking auxiliary register data to both the participants and the non-participants of the study, estimating propensity scores for participation and correcting for participation based on these. However, registry holders may not be allowed to disclose sensitive data on (invited) non-participants. Our aim is to provide guidance on how adequate bias correction can be achieved by using auxiliary register data but without disclosing information that could be linked to the subset of non-participants. Methods: We show how existing methods can be used to estimate generalisation weights under various data disclosure scenarios where invited non-participants are indistinguishable from uninvited ones. We also demonstrate how the methods can be implemented using Nordic register data. Results: Inverse-probability-of-sampling weights estimated within a random sample of the target population in which the non-respondents are disclosed are equivalent in expectation to analogous weights in a scenario where the non-participants and uninvited individuals from the population are indistinguishable. To minimise the risk of disclosure when the entire population is invited to participate, investigators should instead consider inverse-odds-of-sampling weights, a method that has previously been suggested for transporting study results to external populations. Conclusions: Generalisation weights can be estimated from auxiliary register data without disclosing information on invited non-participants.

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