proportional rates
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2021 ◽  
pp. 096228022110239
Author(s):  
Feng-Chang Lin ◽  
Jianwen Cai ◽  
Jason P Fine ◽  
Elisabeth P Dellon ◽  
Charles R Esther

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.



2018 ◽  
Vol 111 (2) ◽  
pp. 289-308 ◽  
Author(s):  
Jonathan E. Friedel ◽  
Ann Galizio ◽  
Meredith S. Berry ◽  
Mary M. Sweeney ◽  
Amy L. Odum


2018 ◽  
Vol 11 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Guanglei Yu ◽  
Liang Zhu ◽  
Jianguo Sun ◽  
Leslie L. Robison




2014 ◽  
Vol 1 (3) ◽  
Author(s):  
Parvin Mansouri ◽  
Ebrahim Hajizadeh ◽  
Freshteh Osmani


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