Journal of Probability and Statistics
Volume 2012 (2012), Article ID 931416, 37 pages
http://dx.doi.org/10.1155/2012/931416
Research Article
Secondary Analysis under Cohort Sampling Designs Using Conditional Likelihood
1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada H3A 1A2
2Indic Society for Education and Development (INSEED), Nashik, Maharashtra 422 011, India
3Department of Vaccines, National Institute for Health and Welfare, 00271 Helsinki, Finland
4Department of Mathematics and Statistics, University of Tampere, 33014 Tampere, Finland
5Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
Received 28 July 2011; Revised 29 December 2011; Accepted 24 January 2012
Academic Editor: Kari Auranen
Copyright © 2012 Olli Saarela et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Under cohort sampling designs, additional covariate data are collected on cases of a specific type and a randomly selected subset of noncases, primarily for the purpose of studying associations with a time-to-event response of interest. With such data available, an interest may arise to reuse them for studying associations between the additional covariate data and a secondary non-time-to-event response variable, usually collected for the whole study cohort at the outset of the study. Following earlier literature, we refer to such a situation as secondary analysis. We outline a general conditional likelihood approach for secondary analysis under cohort sampling designs and discuss the specific situations of case-cohort and nested case-control designs. We also review alternative methods based on full likelihood and inverse probability weighting. We compare the alternative methods for secondary analysis in two simulated settings and apply them in a real-data example.