A note on the partial likelihood estimator of the proportional hazards model for combined incident and prevalent cohort data

James H. McVittie (), David B. Wolfson () and David A. Stephens ()
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James H. McVittie: University of Regina
David B. Wolfson: McGill University
David A. Stephens: McGill University

Metrika: International Journal for Theoretical and Applied Statistics, 2023, vol. 86, issue 4, No 5, 487-497

Abstract: Abstract The proportional hazards model has been well studied in the literature for estimating the effect of covariate data on the failure time hazard rate. This model is routinely applied to right-censored incident cohort failure time data as well as left-truncated right-censored failure time data obtained from a prevalent cohort study with follow-up. In a meta-analysis or complex study design, data from both incident cohort and prevalent cohort studies with follow-up may be available. We compare two partial likelihood estimation approaches for the covariate effects using combined incident and prevalent cohort data under the proportional hazards model. We validate the partial likelihood methods through the concept of ancillarity and utilize simulated cohort data to compare the two procedures.

Keywords: Survival analysis; Combined cohort; Partial likelihood; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00184-022-00882-1

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