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Sample size evaluation for a multiply matched case–control study using the score test from a conditional logistic (discrete Cox PH) regression model
Author(s) -
Lachin John M.
Publication year - 2007
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.3057
Subject(s) - covariate , statistics , logistic regression , mathematics , proportional hazards model , score test , conditional logistic regression , nested case control study , sample size determination , econometrics , odds ratio , likelihood ratio test
Abstract The conditional logistic regression model ( Biometrics 1982; 38 :661–672) provides a convenient method for the assessment of qualitative or quantitative covariate effects on risk in a study with matched sets, each containing a possibly different number of cases and controls. The conditional logistic likelihood is identical to the stratified Cox proportional hazards model likelihood, with an adjustment for ties ( J. R. Stat. Soc. B 1972; 34 :187–220). This likelihood also applies to a nested case–control study with multiply matched cases and controls, selected from those at risk at selected event times. Herein the distribution of the score test for the effect of a covariate in the model is used to derive simple equations to describe the power of the test to detect a coefficient θ (log odds ratio or log hazard ratio) or the number of cases (or matched sets) and controls required to provide a desired level of power. Additional expressions are derived for a quantitative covariate as a function of the difference in the assumed mean covariate valuesamong cases and controls and for a qualitative covariate in terms of the difference in the probabilities of exposure for cases and controls. Examples are presented for a nested case–control study and a multiply matched case–control study. Copyright © 2007 John Wiley & Sons, Ltd.