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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 aContinue 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,Continue reading “Simulation and estimation of three-level survival models: IPD meta-analysis of recurrent event data”
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)Continue reading “Probabilistic sensitivity analysis and 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 onContinue reading “Joint longitudinal-survival models with time-dependent effects (non-proportional hazards)”
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, butContinue 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 onContinue 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 useContinue 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 functionContinue reading “Flexible parametric survival analysis with frailty”
A user-defined/custom hazard model
This tutorial will illustrate some of the more advanced capabilities of merlin when modelling survival data, but with the aim of using an accessible example. During my PhD, Paul Lambert and I developed stgenreg in Stata for modelling survival dataContinue reading “A user-defined/custom hazard model”
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 distributedContinue reading “Joint longitudinal and competing risks models: Simulation, estimation and prediction”
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