Illustrates how to fit a model using patient data and then predict in a second dataset specifically constructed to contain only the covariates for which we wish to predict. Age is modelled using a restricted cubic spline.
Illustrates how to fit a model using patient data and then predict in a second dataset specifically constructed to contain only the covariates for which we wish to predict. Age is modelled using a restricted cubic spline.
We will partition the total effect of sex into the natural indirect effect (mediated by stage) and the natural direct effect. We then illustrate how to estimate the proportion of the sex difference mediated by stage. Emphasis is on illustrating how these quantities can be estimated in Stata using the standsurv command; we won't discuss the neccessary assumptions and their appropriateness.
A Comparison of Cox and flexible parametric models with application to studying sex differences in survival of patients diagnosed with melanoma. Illustration of how to plot the time-varying hazard ratio from a flexible parametric model.
In this tutorial, we examine sex difference in survival for patients diagnosed with melanoma and illustrate how to estimate the causal effect of sex on patient survival using regression standardisation (also known and G-Computation). We discuss various approaches to estimating adjusted survival curves and illustrate the meansurv and stpm2_standsurv post-estimation commands to stpm2 (for fitting flexible parametric models in Stata).