## 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”

## stmt: Modelling multiple timescales using flexible parametric survival models in Stata

After many years of working on the stmt package in Stata, our paper Flexible parametric survival analysis with multiple timescales: Estimation and implementation using stmt was recently published in the Stata Journal (1). stmt can be installed by typing in Stata: The aim of this paper was to describe and illustrate how to model multipleContinue reading “stmt: Modelling multiple timescales using flexible parametric survival models in Stata”

## Introducing the {msm.stacked} R package

Today we introduce {msm.stacked}, an R package that can be used to simplify the calculation of state transition probabilities over time and the creation of stacked probability plots from multi-state model fits from the {msm} package. Let me show you some examples of the package functionality in practice. We start by building upon the exampleContinue reading “Introducing the {msm.stacked} R package”

## 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”

## 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”

## 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 data with a general user-specified hazard function, with the generality achieved by using numerical integration toContinue reading “A user-defined/custom hazard model”