I’m delighted to announce that registration is now open for our very first training school, covering a range of topics in advanced survival analysis 🥳. We’ll be covering everything from flexible parametric models to competing risks, multi-state models and joint models. We could not be more excited for you to join us in Stockholm, 20th –Continue reading “Red Door Analytics Winter School – Registration now open!”
Author Archives: Michael Crowther
Training course at Lifetime Data Science conference 2023
I’m slightly (very) late in advertising this, but I will be at the 3rd Conference on Lifetime Data Science, to be held in Raleigh, North Carolina, USA, during May 31 – June 2, 2023, teaching a one-day course on Flexible parametric competing risks and multi-state models. Full conference details are here. The programme looks ratherContinue reading “Training course at Lifetime Data Science conference 2023”
Launching our first online training course!
We could not be more excited to launch our very first online training course in survival analysis – and it’s now live! Click here or the image below to take you straight to all the details. This course is the culmination of so much work, predominantly by our talented course instructor and Director of AppliedContinue reading “Launching our first online training course!”
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”
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”
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”