stjm11 fits shared parameter joint models for longitudinal and survival data. 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. Four choices are currently available for the survival submodel; the first being the flexible parametric survival model (see stpm2 available on SSC), modelled on the log cumulative hazard scale. The remaining choices include the exponential, Weibull and Gompertz proportional hazard models. The association between the two processes can be induced via the default current value parameterisation, the first derivative of the longitudinal submodel, 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 an expoenential/Weibull/Gompertz survival submodel, 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 12.1 should use stjm.