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”

Simulation and estimation of three-level survival models: IPD meta-analysis of recurrent event data

In this example I’ll look at the analysis of clustered survival data with three levels. This kind of data arises in the meta-analysis of recurrent event times, where we have observations (events or censored), k (level 1), nested within patients, j (level 2), nested within trials, i (level 3). Random intercepts The first example willContinue reading “Simulation and estimation of three-level survival models: IPD meta-analysis of recurrent event data”

Survival analysis with interval censoring

Interval censoring occurs when we don’t know the exact time an event occurred, only that it occurred within a particular time interval. Such data is common in ophthalmology and dentistry, where events are only picked up at scheduled appointments, but they actually occurred at some point since the previous visit. Arguably, we could say allContinue reading “Survival analysis with interval censoring”

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”

Mixed effects for the level 1 variance function in a multilevel model

In this example, we look at a paper by the late great statistician Harvey Goldstein and colleagues (Goldstein et al., 2017) that proposed a two-level model with complex level 1 variation. This will be a nice illustration of the use of family(user) combined with the family(null) options of merlin to provide an accessible implementation ofContinue reading “Mixed effects for the level 1 variance function in a multilevel model”

Flexible parametric survival analysis with frailty

This example takes a look at incorporating a frailty, or random intercept, into a flexible parametric survival model, and how to fit them in Stata. First we’ll use merlin to estimate our model, and then the more user-friendly wrapper function stmixed. More details on these models can be found in the following papers: Crowther MJ,Continue reading “Flexible parametric survival analysis with frailty”

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”

Defining a transition matrix for multi-state modelling

In this post we’ll take a look at how to define a custom transition matrix for use with our multistate package in Stata. The transition matrix A transition matrix governs the movement of a process between possible states. Within multi-state survival analysis, and particularly, the implementation of multi-state models in Stata, the transition matrix containsContinue reading “Defining a transition matrix for multi-state modelling”

multistate v4.4.0: semi-parametric multi-state modelling

multistate version 4.4.0 has been released! Ok, that may have happened a few weeks ago… The headlines: predictms now supports the Cox model as a transition model, estimated usingmerlin or stmerlin Predictions from a multi-state Cox model are implemented using asimulation approach Supported predictions from a multi-state Cox model include transitionprobabilities, probability, and length ofContinue reading “multistate v4.4.0: semi-parametric multi-state modelling”