I’m delighted to say that the Summer School on Modern Methods in Biostatistics and Epidemiology is returning to the picturesque CastelBrando with a huge range of courses in June 2023! Join me in Treviso, Italy, where I will be back teaching our week long (half days) course on Joint modelling of longitudinal and survival data,Continue reading “Biostatistics & Epidemiology Summer School returns!”

# Tag Archives: prediction

## Probabilistic sensitivity analysis and survival models

Today we’re going to take a little look into probabilistic sensitivity analysis (PSA), and how it can be implemented within the context of survival analysis. Now PSA is used extensively in health economic modelling, where a particular parameter (or parameters) of interest, are altered or varied, to represent different scenarios and levels of variation. WeContinue reading “Probabilistic sensitivity analysis and survival models”

## Relative survival analysis

Relative survival models are predominantly used in population based cancer epidemiology (Dickman et al. 2004), where interest lies in modelling and quantifying the excess mortality in a population with a particular disease, compared to a reference population, appropriately matched on things like age, gender and calendar time. One of the benefits of the approach isContinue reading “Relative survival analysis”

## Joint longitudinal and competing risks models: Simulation, estimation and prediction

This post takes a look at an extension of the standard joint longitudinal-survival model, which is to incorporate competing risks. Let’s start by formally defining the model. We will assume a continuous longitudinal outcome, where and is our normally distributed residual variability. We call our trajectory function, representing the true underlying value of the continuousContinue reading “Joint longitudinal and competing risks models: Simulation, estimation and prediction”

## Simulation, modelling and prediction with a non-linear covariate effect in survival analysis

Let’s begin. There will be a single continuous covariate, representing age, with a non-linear effect influencing survival. We’ll simulate survival times under a data-generating model that incorporates a non-linear effect of age. We’ll then fit some models accounting for the non-linear effect of age, and finally make predictions for specified values of age. Sounds simple,Continue reading “Simulation, modelling and prediction with a non-linear covariate effect in survival analysis”