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Case–control analysis with a continuous outcome variable
Author(s) -
Jiang Yannan,
Scott Alastair,
Wild Chris J.
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3474
Subject(s) - outcome (game theory) , computer science , variable (mathematics) , econometrics , statistics , mathematics , mathematical economics , mathematical analysis
It is not uncommon for a continuous outcome variable Y to be dichotomized and analysed using logistic regression. Moser and Coombs ( Statist. Med. 2004; 23 :1843–1860) provide a method for converting the output from a standard linear regression analysis using the original continuous outcome Y to give much more efficient inferences about the same odds‐ratio parameters being estimated by the logistic regression. However, these results apply only to prospective studies. This paper follows up Moser and Coombs by providing an efficient linear‐model‐based solution for data collected using case–control studies. Gains in statistical efficiency of up to 240 per cent are obtained even with small to moderate odds ratios. Differences in design efficiency between case–control and prospective sampling designs are found to be much smaller, however, when linear‐model‐based analyses are being used than they are when logistic regression analyses are being used. Copyright © 2008 John Wiley & Sons, Ltd.