Joint modeling of zero-inflated panel count and severity outcomes.

Panel counts are often encountered in longitudinal, such as diary, studies where individuals are followed over time and the number of events occurring in time intervals, or panels, is recorded. This article develops methods for situations where, in addition to the counts of events, a mark, denoting a measure of severity of the events, is recorded. In many situations there is an association between the panel counts and their marks. This is the case for our motivating application that studies the effect of two hormone therapy treatments in reducing counts and severities of vasomotor symptoms in women after hysterectomy/ovariectomy. We model the event counts and their severities jointly through the use of shared random effects. We also compare, through simulation, the power of testing for the treatment effect based on such joint modeling and an alternative scoring approach, which is commonly employed. The scoring approach analyzes the compound outcome of counts times weighted severity. We discuss this approach and quantify challenges which may arise in isolating the treatment effect when such a scoring approach is used. We also show that the power of detecting a treatment effect is higher when using the joint model than analysis using the scoring approach. Inference is performed via Markov chain Monte Carlo methods.

A comparison of change point models with application to longitudinal lung function measurements in children with cystic fibrosis.

Cystic fibrosis (CF) is a hereditary lung disease characterized by loss of lung function over time. Lung function in CF is believed to decline at a higher rate during the adolescence period. It has been also hypothesized that there is a subgroup of individuals for whom lung disease remains relatively stable with only a slight decline over their lifetime. Using data from the University of Colorado CF Children's Registry, we investigate four change point models to model the decline of lung function in children and adolescents: (i) a two-component mixture random change point model, (ii) a two-component mixture-fixed change point model, (iii) a random change point model, and (iv) a fixed change point model. The models are investigated through posterior predictive simulation at the individual and population levels, and a simulation study examining the effects of model misspecification. The data support the mixed random change point model as the preferred model, with roughly 30% of adolescents experiencing a steady decline of 0.5 %FEV1 per year and 70% experiencing an increase in decline of 4.4 %FEV1 per year beginning on average at 14.6 years of age. Copyright © 2016 John Wiley & Sons, Ltd.