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Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data
Author(s) -
Rabinowitz Daniel,
Betensky Rebecca A.,
Tsiatis Anastasios A.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00511.x
Subject(s) - logistic regression , statistics , confidence interval , odds , odds ratio , regression analysis , regression , econometrics , computer science , mathematics
Summary. An easily implemented approach to fitting the proportional odds regression model to interval‐censored data is presented. The approach is based on using conditional logistic regression routines in standard statistical packages. Using conditional logistic regression allows the practitioner to sidestep complications that attend estimation of the baseline odds ratio function. The approach is applicable both for interval‐censored data in settings in which examinations continue regardless of whether the event of interest has occurred and for current status data. The methodology is illustrated through an application to data from an AIDS study of the effect of treatment with ZDV + ddC versus ZDV alone on 50% drop in CD4 cell count from baseline level. Simulations are presented to assess the accuracy of the procedure.