## Biostatistics & Epidemiology Summer School returns!

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!”

## Multivariate joint longitudinal-survival models

Joint longitudinal-survival models have been widely developed, but there are many avenues of research where they are lacking in terms of methodological development, and importantly, accessible implementations. We think merlin fills a few gaps. In this post, we’ll take a look at the extension to modelling multiple continuous longitudinal outcomes, jointly with survival. For simplicity,Continue reading “Multivariate joint longitudinal-survival models”

## Joint longitudinal-survival models with time-dependent effects (non-proportional hazards)

In this post we’ll focus on how to model time-dependent effects (non-proportional hazards), specifically within a joint longitudinal-survival model. If this is your first time reading a little about joint models, check out our other posts on joint models on our Tutorials page. Now joint models are becoming commonplace in medical research, but as always,Continue reading “Joint longitudinal-survival models with time-dependent effects (non-proportional hazards)”

## 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”

## An introduction to joint modelling of longitudinal and survival data

This post gives a gentle introduction to the joint longitudinal-survival model framework, and covers how to estimate them using our merlin command in Stata. A joint model consists of a continuous, repeatedly measured (longitudinal) outcome, and a time-to-event, with the two models linked by random effects, or functions of them. Let’s formally define everything weContinue reading “An introduction to joint modelling of longitudinal and survival data”

## Joint frailty models for recurrent and terminal events

In this post we’re going to take a look at joint frailty models, and how to fit them with our merlin command. Importantly, we’ll also discuss how to interpret the results. Joint frailty models An area of intense research in recent years is in the field of joint frailty models, which has become the commonlyContinue reading “Joint frailty models for recurrent and terminal events”

## Simulating survival data with a continuous time-varying covariate…the right way

In this post we’ll take a look at how to simulate survival data with a continuous, time-varying covariate. The aim is to simulate from a data-generating mechanism appropriate for evaluating a joint longitudinal-survival model. We’ll use the survsim command to simulate the survival times, and the merlin command to fit the corresponding true model. Let’sContinue reading “Simulating survival data with a continuous time-varying covariate…the right way”