Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures

Jan Feifel (), Madlen Gebauer, Martin Schumacher and Jan Beyersmann
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Jan Feifel: Ulm University
Madlen Gebauer: Ulm University
Martin Schumacher: University Medical Center Freiburg
Jan Beyersmann: Ulm University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 1, No 2, 44 pages

Abstract: Abstract For large cohort studies with rare outcomes, the nested case-control design only requires data collection of small subsets of the individuals at risk. These are typically randomly sampled at the observed event times and a weighted, stratified analysis takes over the role of the full cohort analysis. Motivated by observational studies on the impact of hospital-acquired infection on hospital stay outcome, we are interested in situations, where not necessarily the outcome is rare, but time-dependent exposure such as the occurrence of an adverse event or disease progression is. Using the counting process formulation of general nested case-control designs, we propose three sampling schemes where not all commonly observed outcomes need to be included in the analysis. Rather, inclusion probabilities may be time-dependent and may even depend on the past sampling and exposure history. A bootstrap analysis of a full cohort data set from hospital epidemiology allows us to investigate the practical utility of the proposed sampling schemes in comparison to a full cohort analysis and a too simple application of the nested case-control design, if the outcome is not rare.

Keywords: Cox proportional hazards model; Cost-effective sampling; Matched case-control study; Hospital-acquired pneumonia; Time-dependent covariate (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-018-9453-4

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