Flexible parametric survival analysis with frailty, take two

Hi everyone, Today we are going to continue learning about flexible parametric survival models with random effects, as a follow-up to a previous post on our blog. Specifically, we are going to replicate the analysis from that post, but with a twist: today, we are going to use the {merlin} package in R. {merlin} in R isContinue reading “Flexible parametric survival analysis with frailty, take two”

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”

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”

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)”

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”