Using conditional logistic regression to fit proportional odds models to interval censored data.
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.
Rabinowitz
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Department of Statistics, Columbia University, New York, New York 10027, USA. dan@wald.stat.columbia.edu
Betensky
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Tsiatis
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D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000276:immunology / Q000401:mortality; D019380:Anti-HIV Agents / Q000627:therapeutic use; D001699:Biometry / Q000379:methods*; D018791:CD4 Lymphocyte Count; D004359:Drug Therapy, Combination; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D016019:Survival Analysis; D016047:Zalcitabine / Q000627:therapeutic use; D015215:Zidovudine / Q000627:therapeutic use
D019380:Anti-HIV Agents; D015215:Zidovudine; D016047:Zalcitabine