Sampling strategies for selecting general population comparison cohorts
Authors Heide-Jørgensen U, Adelborg K, Kahlert J, Sørensen HT, Pedersen L
Received 3 February 2018
Accepted for publication 2 July 2018
Published 25 September 2018 Volume 2018:10 Pages 1325—1337
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Irene Petersen
Uffe Heide-Jørgensen, Kasper Adelborg, Johnny Kahlert, Henrik Toft Sørensen, Lars Pedersen
Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
Background: For a patient cohort, access to linkable population-based registries permits sampling of a comparison cohort from the general population, thereby contributing to the understanding of the disease in a population context. However, sampling without replacement in random order can lead to immortal time bias by conditioning on the future.
Aim: We compared the following strategies for sampling comparison cohorts in matched cohort studies with respect to time to ischemic stroke and mortality: sampling without replacement in random order; sampling with replacement; and sampling without replacement in chronological order.
Methods: We constructed index cohorts of individuals from the Danish general population with no particular trait, except being alive and without ischemic stroke on the index date. We also constructed index cohorts of persons aged >50 years from the general population. We then applied the sampling strategies to sample comparison cohorts (5:1 or 1:1) from the Danish general population and compared outcome risks between the index and comparison cohorts. Finally, we sampled comparison cohorts for a heart failure cohort using each strategy.
Results: We observed increased outcome risks in comparison cohorts sampled 5:1 without replacement in random order compared to the index cohorts. However, these increases were minuscule unless index persons were aged >50 years. In this setting, sampling without replacement in chronological order failed to sample a sufficient number of comparators, and the mortality risks in these comparison cohorts were lower than in the index cohorts. Sampling 1:1 showed no systematic difference between comparison and index cohorts. When we sampled comparison cohorts for the heart failure patients, we observed a pattern similar to when index persons were aged >50 years.
Conclusion: When index persons were aged >50 years, ie, had high outcome risks, sampling 5:1 without replacement introduced bias. Sampling with replacement or 1:1 did not introduce bias.
Keywords: matched cohort study, survival analysis, population-based registry, observational study
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