Abstract
Proportional rates models are frequently used for the analysis of recurrent event data with multiple event categories. When some of the event categories are missing, a conventional approach is to either exclude the missing data for a complete-case analysis or employ a parametric model for the missing event type. It is well known that the complete-case analysis is inconsistent when the missingness depends on covariates, and the parametric approach may incur bias when the model is misspecified. In this paper, we aim to provide a more robust approach using a rate proportion method for the imputation of missing event types. We show that the log-odds of the event type can be written as a semiparametric generalized linear model, facilitating a theoretically justified estimation framework. Comprehensive simulation studies were conducted demonstrating the improved performance of the semiparametric method over parametric procedures. Multiple types of Pseudomonas aeruginosa infections of young cystic fibrosis patients were analyzed to demonstrate the feasibility of our proposed approach.
Highlights
Recurrent event data with multiple categories frequently arise in medical science and population health studies
Taking cystic fibrosis (CF) as an example, recurrent Pseudomonas aeruginosa (PA) infections are commonly observed in patients with CF
We extend the proportional rates method previously developed for estimation of pijðtÞ8 using a semiparametric approach that exploits a special form of the rate proportion of event type j to the overall rate function
Summary
Recurrent event data with multiple categories frequently arise in medical science and population health studies. The recurrent infections with mucoid strains often become persistent and chronic, causing increased CF mortality and morbidity.[1,2,3] As another example, patients who received renal transplants may have different types of recurrent infections.[4] End-stage renal disease patients who received continuous ambulatory peritoneal dialysis may have multiple types of treatment failures that make the patient switch to other dialysis methods.[5] When modeling this kind of recurrent event data, a proportional rates model that is conditional only on the current value of covariates is commonly used.[6] Let NÃijðtÞ denote the number of recurrent events up to time t for.
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