Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up

McVittie James (), Wolfson David (), Stephens David, Addona Vittorio and Buckeridge David
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McVittie James: McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, Quebec Canada
Wolfson David: McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, Quebec Canada
Stephens David: McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, Quebec Canada
Addona Vittorio: Macalester College, Mathematics, Statistics and Computer Science, St.Paul, Minnesota, United States
Buckeridge David: McGill University, Epidemiology, Biostatistics and Occupational Health, Montreal, Quebec Canada

The International Journal of Biostatistics, 2021, vol. 17, issue 2, 283-293

Abstract: A classical problem in survival analysis is to estimate the failure time distribution from right-censored observations obtained from an incident cohort study. Frequently, however, failure time data comprise two independent samples, one from an incident cohort study and the other from a prevalent cohort study with follow-up, which is known to produce length-biased observed failure times. There are drawbacks to each of these two types of study when viewed separately. We address two main questions here: (i) Can our statistical inference be enhanced by combining data from an incident cohort study with data from a prevalent cohort study with follow-up? (ii) What statistical methods are appropriate for these combined data? The theory we develop to address these questions is based on a parametrically defined failure time distribution and is supported by simulations. We apply our methods to estimate the duration of hospital stays.

Keywords: combined cohort; maximum likelihood estimation; survival analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/ijb-2020-0042

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