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Between-within models for survival analysis.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden .
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden .
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID iD: 0000-0002-6851-3297
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden .
2013 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 32, no 18, p. 3067-3076Article in journal (Refereed) Published
Abstract [en]

A popular way to control for confounding in observational studies is to identify clusters of individuals (e.g., twin pairs), such that a large set of potential confounders are constant (shared) within each cluster. By studying the exposure-outcome association within clusters, we are in effect controlling for the whole set of shared confounders. An increasingly popular analysis tool is the between-within (BW) model, which decomposes the exposure-outcome association into a 'within-cluster effect' and a 'between-cluster effect'. BW models are relatively common for nonsurvival outcomes and have been studied in the theoretical literature. Although it is straightforward to use BW models for survival outcomes, this has rarely been carried out in practice, and such models have not been studied in the theoretical literature. In this paper, we propose a gamma BW model for survival outcomes. We compare the properties of this model with the more standard stratified Cox regression model and use the proposed model to analyze data from a twin study of obesity and mortality. We find the following: (i) the gamma BW model often produces a more powerful test of the 'within-cluster effect' than stratified Cox regression; and (ii) the gamma BW model is robust against model misspecification, although there are situations where it could give biased estimates.

Place, publisher, year, edition, pages
Hoboken, USA: Wiley-Blackwell, 2013. Vol. 32, no 18, p. 3067-3076
National Category
Medical and Health Sciences Public Health, Global Health, Social Medicine and Epidemiology Computational Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-54537DOI: 10.1002/sim.5767ISI: 000324991400002PubMedID: 23456754Scopus ID: 2-s2.0-84880037983OAI: oai:DiVA.org:oru-54537DiVA, id: diva2:1064339
Funder
Swedish Research CouncilAvailable from: 2017-01-12 Created: 2017-01-12 Last updated: 2017-11-29Bibliographically approved

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Larsson, Henrik

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