Comparison of statistical methods for recurrent event analysis using pediatrics asthma data

When the same type of event is experienced by a subject more than once it is called recurrent event, which possess two important characteristics, namely "within-subject correlation" and "time-varying covariate." As a result, the traditional statistical methods do not work well on recurrent event data. Over the past few decades, many alternatives methods have been proposed for the analysis of recurrent event data. In this article, the six most prominent methods for recurrent event analysis have been compared on pediatric asthma data. Three variance corrected models (viz "Anderson and Gill [AG] model," "Prentice, William, and Peterson-Counting Process [PWP-CP] model," and "Prentice, William, and Peterson-Gap Time [PWP-GT] model") and three corresponding frailty variants (AG-frailty, PWP-CP-frailty, and PWP-GT-frailty) were compared using three mathematical criterion (AIC, BIC, and log-likelihood) and one graphical criteria (Cox-Snell goodness of fit, visual test). All model comparison indices showed the PWP-GT model as the most appropriate model on asthma data over other models. By using PWP-GT model, seven predictors of asthma exacerbation (viz "abdominal pain at previous visit," "Z5 (%) at previous visit," "diagnosis of asthma at previous visit," "calendar month of exacerbation," "history of maternal asthma," "monthly per capita income," and "emotional stress") were identified. The PWP-GT model was identified as the most appropriate model over other models on pediatrics asthma data.