Tutorial in biostatistics: competing risks and multi‐state models

TLDR

Standard survival analysis measures time to a single event, but when multiple event types exist, competing risks and multi‑state models are required to describe progression to each outcome, and methods for these models have been developed over the past two decades and can be implemented in common statistical packages with some data preparation effort. This tutorial reviews statistical methods for analyzing competing risks and multi‑state models. It focuses on practical aspects such as data preparation, covariate effect estimation, cumulative incidence functions, state and transition probabilities, and demonstrates analyses using standard software. © 2006 John Wiley & Sons, Ltd.

Abstract

Abstract Standard survival data measure the time span from some time origin until the occurrence of one type of event. If several types of events occur, a model describing progression to each of these competing risks is needed. Multi‐state models generalize competing risks models by also describing transitions to intermediate events. Methods to analyze such models have been developed over the last two decades. Fortunately, most of the analyzes can be performed within the standard statistical packages, but may require some extra effort with respect to data preparation and programming. This tutorial aims to review statistical methods for the analysis of competing risks and multi‐state models. Although some conceptual issues are covered, the emphasis is on practical issues like data preparation, estimation of the effect of covariates, and estimation of cumulative incidence functions and state and transition probabilities. Examples of analysis with standard software are shown. Copyright © 2006 John Wiley & Sons, Ltd.