Modeling semicontinuous longitudinal data with order constraints

Semicontinuous longitudinal data are characterized by within-subjects repeated measurements that either indicate absence of abnormality or reflect different amount of abnormality. Joint models for semicontinuous longitudinal data have been increasingly receiving attention in the literature. Such models permit flexible characterization of covariates-outcome associations. Order-restricted statistical inference has been well established in the literature but has not yet been applied to joint models for semicontinuous longitudinal data. We incorporate general order-restricted inference into the general joint models for semicontinuous longitudinal data previously proposed. We develop computational methods to address general order restrictions. Through simulations and a real-data example, we demonstrate the advantages of order-restricted inference in terms of increased power in hypothesis testing and increased precision in parameter estimation.