Multilevel mixed effects parametric survival analysis: Estimation, simulation and application

TL;DR

This paper introduces the stmixed command for fitting complex multilevel survival models with flexible options, extending existing tools with new features and demonstrating its application through real and simulated data.

Contribution

The paper presents stmixed, a new user-written command that extends multilevel survival analysis capabilities with flexible modeling options and simulation features.

Findings

stmixed can fit models with multiple levels and random effects

It supports spline-based and user-defined hazard models

Demonstrated on kidney disease data and simulated datasets

Abstract

In this article, I present the user written stmixed command for the fitting of multilevel survival models, which serves as both an alternative to Stata's official mestreg, and a complimentary program with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston-Parmar and the log hazard equivalent) or user-defined hazard models. Simple or complex time-dependent effects can be included, as well as the addition of expected mortality for a relative survival model. Left-truncation/delayed entry can be used and t-distributed random effects are provided as an alternative to Gaussian random effects. The methods are illustrated with a commonly used dataset of patients with kidney disease suffering recurrent infections, and a simulated example, illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, 2013). stmixed is part of the merlin family (Crowther 2017, 2018).