Flexible joint modeling of longitudinal and time-to-event data

The joint modeling of longitudinal and time-to-event data has exploded in the methodological literature in the past decade; however, the availability of software to implement the methods lags behind. The most common form of joint model assumes that the association between the survival and longitudinal processes are underlined by shared random effects. As a result, computationally intensive numerical integration techniques such as Gauss-Hermite quadrature are required to evaluate the likelihood. We describe a new user-written command jm, which allows the user to jointly model a continuous longitudinal response and an event of interest. We assume a linear mixed-effects model for the longitudinal submodel, thereby allowing flexibility through the use of fixed and/or random fractional polynomials of time. We also assume a flexible parametric model (stpm2) for the survival submodel. Flexible parametric models are fitted on the log cumulative hazard scale, which has direct computational benefits because it avoids the use of numerical integration to evaluate the cumulative hazard. We describe the features of jm through application to a dataset investigating the effect of serum albumin level on time to death from any cause in 252 patients suffering end-stage renal disease.