%JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses

Abstract

In clinical research subjects are usually observed during a period of time. Primary and secondary endpoints are often either responses measured longitudinally over time or the time at which an event of interest occurs. Joint modeling is increasingly being used for multiple purposes such as to adjust the analysis of the longitudinal response for informative dropout mechanisms. In this paper we present %JM, a SAS macro that fits jointly generalized mixed models for longitudinal data and proportional hazards models for time-to-event responses. The macro fits normal, binary, binomial and Poisson longitudinal responses and allows choosing among a range of options to fit the trajectories of the longitudinal response over time: a linear function, splines, natural cubic splines and B-splines. For the time-to-event response, that might be right-censored, the macro fits parametric, stratified or not, proportional hazards models with the following baseline risk functions: exponential, Weibull, piecewise exponential and the generalizations of the Weibull and the Gompertz models based on splines. %JM offers several options to connect the longitudinal model and the time-to-event model: current-value-dependent and slope-dependent shared parameters, lagging effects, cumulative effects, random effects coefficients as shared parameters and interaction effects.