Stat Med. 2024 Mar 15;43(6):1170-1193. doi: 10.1002/sim.9999. Epub 2024 Jan 20.
ABSTRACT
This research introduces a multivariate τ $$ tau $$ -inflated beta regression ( τ $$ tau $$ -IBR) modeling approach for the analysis of censored recurrent event data that is particularly useful when there is a mixture of (a) individuals who are generally less susceptible to recurrent events and (b) heterogeneity in duration of event-free periods amongst those who experience events. The modeling approach is applied to a restructured version of the recurrent event data that consists of censored longitudinal times-to-first-event in τ $$ tau $$ length follow-up windows that potentially overlap. Multiple imputation (MI) and expectation-solution (ES) approaches appropriate for censored data are developed as part of the model fitting process. A suite of useful analysis outputs are provided from the τ $$ tau $$ -IBR model that include parameter estimates to help interpret the (a) and (b) mixture of event times in the data, estimates of mean τ $$ tau $$ -restricted event-free duration in a τ $$ tau $$ -length follow-up window based on a patient’s covariate profile, and heat maps of raw τ $$ tau $$ -restricted event-free durations observed in the data with censored observations augmented via averages across MI datasets. Simulations indicate good statistical performance of the proposed τ $$ tau $$ -IBR approach to modeling censored recurrent event data. An example is given based on the Azithromycin for Prevention of COPD Exacerbations Trial.
PMID:38386367 | DOI:10.1002/sim.9999
