STJM: Stata module to fit shared parameter joint models of longitudinal and survival data. stjm fits shared parameter joint models for longitudinal and survival data using maximum likelihood. A single continuous longitudinal response and a single survival outcome are allowed. A linear mixed effects model is used for the longitudinal submodel, which lets time be modelled using fixed and/or random fractional polynomials or resticted cubic splines. Six choices are currently available for the survival submodel, including the exponential, Weibull, Gompertz, 2-component mixture Weibull-Weibull and 2-component mixture Weibull-exponential proportional hazards models. Furthermore, the flexible parametric survival model (see stpm2), modelled on the log cumulative hazard scale is also available. The association between the two processes can be induced via the default current value parameterisation, the first derivative of the longitudinal submodel (slope), and/or a random coefficient such as the intercept. Adaptive or non-adaptive Gauss-Hermite quadrature, coded in Mata, can be used to evaluate the joint likelihood. Under all survival submodels except the flexible parametric model, Gauss-Kronrod quadrature is used to evaluate the cumulative hazard. The dataset must be stset correctly into enter and exit times, using the enter option. stjm uses _t0 to denote measurement times. Delayed entry models are allowed. Users of Stata 11 should use stjm11