Multi-state survival models for interval-censored data

✍ Scribed by van den Hout, Ardo

• Publisher: Chapman and Hall/CRC
• Year: 2017
• Tongue: English
• Leaves: 257
• Series: Monographs on Statistics et Applied Probability 152
• Category: Library

✦ Synopsis

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process.

The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference.

Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

✦ Table of Contents

Content: Preface Introduction Multi-state survival models Basic concepts Examples Overview of methods and literature Data used in this book Modelling Survival Data Features of survival data and basic terminology Hazard, density and survivor function Parametric distributions for time to event data Regression models for the hazard Piecewise-constant hazard Maximum likelihood estimation Example: survival in the CAV study Progressive Three-State Survival Model Features of multi-state data and basic terminology Parametric models Regression models for the hazards Piecewise-constant hazards Maximum likelihood estimation A simulation study Example General Multi-State Survival Model Discrete-time Markov process Continuous-time Markov processes Hazard regression models for transition intensities Piecewise-constant hazards Maximum likelihood estimation Scoring algorithm Model comparison Example Model validation Example Frailty Models Mixed-effects models and frailty terms Parametric frailty distributions Marginal likelihood estimation Monte-Carlo Expectation-Maximisation algorithm Example: frailty in ELSA Non-parametric frailty distribution Example: frailty in ELSA (continued) Bayesian Inference for Multi-State Survival Models Introduction Gibbs sampler Deviance Information Criterion (DIC) Example: frailty in ELSA (continued) Inference using the BUGS software Redifual State-Specific Life Expectancy Introduction Definitions and data considerations Computation: integration Example: a three-state survival process Computation: micro-simulation Example: life expectancies in CFAS Further Topics Discrete-time models for continuous-time processes Using cross-sectional data Missing state data Modelling the first observed state Misclassification of states Smoothing splines and scoring Semi-Markov models Matrix P(t) When Matrix Q is Constant Two-state models Three-state models Models with more than three states Scoring for the Progressive Three-State Model Some Code for the R and BUGS Software General-purpose optimiser Code for Chapter 2 Code for Chapter 3 Code for Chapter 4 Code for numerical integration Code for Chapter 6 Bibliography Index

✦ Subjects

Biometry;Demography;Statistical methods;Multi-State Information System;Censored observations;Stochastic processes