Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben

2017 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Statistical Modelling of Survival Data with Random Effects

H-Likelihood Approach

verfasst von: Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee

Verlag: Springer Singapore

Buchreihe : Statistics for Biology and Health

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten
insite
SUCHEN

Über dieses Buch

This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
The likelihood, introduced by R.A Fisher (PTRS 222: 309–368, 1922), plays an important role in statistical inference about fixed unknowns, namely parameters. The beauty of the likelihood is that once the statistical model is specified parametrically or nonparametrically, the associated inference procedures for the parameters of interest are straightforward.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 2. Classical Survival Analysis
Abstract
Let T be time-to-event (failure time), which is a nonnegative random variable. In medicine, a typical example is time from the onset of a condition or an initiation of treatment to death. In studies of reliability of products (or components), time to failure of light bulbs, for example, is often of interest. Rather than using such specific terms, economists refer to durations between events (e.g., duration of unemployment). The distribution of failure time is usually non-normal and skewed.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 3. H-Likelihood Approach to Random-Effect Models
Abstract
In this chapter, we introduce an h-likelihood approach to the general class of statistical models with random effects. Consider a linear mixed model (LMM), for \(i=1, \ldots , q\) and \(j=1, \ldots , n_{i}\), \(y_{ij}=x_{ij}^{T}{\varvec{\beta }}+v_{i}+e_{ij}\), where \(y_{ij}\) is an observed random variable (response), \( x_{ij}=(x_{ij1}, \ldots , x_{ijp})^{T}\) is a vector of covariates, \({\varvec{ \beta }}\) is a vector of fixed effects, \(v_{i}\sim N(0,\alpha )\) is an i.i.d. random variable for the random effects, \(e_{ij}\sim N(0,\phi )\) is an i.i.d random error or measurement error, and \(v_{i}\) and \(e_{ij}\) are independent. Parameters \(\phi \) and \({\varvec{\alpha }}\) are the variance components. In this model, there are two types of unknowns; the fixed unknowns \(\theta =(\beta ,\phi ,\alpha )^{T} \) and the random unknowns \(v=(v_{1}, \ldots , v_{q})^{T}\).
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 4. Simple Frailty Models
Abstract
The concept of frailty was first introduced by Vaupel et al. (1979) (Demography 16:439–454, 1979) to account for the impact of individual heterogeneity in univariate (independent) survival data. In this chapter, we introduce the frailty model, an extension of the Cox PH model, for analyzing correlated survival data. The frailty is modeled by an unobserved random effect acting multiplicatively on the individual hazard rate to describe the individual heterogeneity and the correlation (dependency) among survival data from the same subject or cluster (Clayton 1978 (Clayton DG in Biometrika 65:141–151, 1978); Hougaard 2000 (Hougaard P in Analysis of multivariate survival data. Springer, New York, 2000); Duchateau and Janssen 2008 (Duchateau L, Janssen P in The frailty model. Springer, New York, 2008)). Even if heterogeneity and correlation are different concepts, they can both be modeled by frailties.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 5. Multicomponent Frailty Models
Abstract
Time-to-event data (recurrent or multiple event times) are often observed on the same subject (or cluster), and the frailty models are useful for analysis of such data. In practice, the multicomponent frailty models are of interest with complicated frailty structures, nested or crossed.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 6. Competing Risks Frailty Models
Abstract
Competing risks data arise when an occurrence of an event precludes other types of events from being observed. In this chapter, we extend the h-likelihood inference procedures for frailty models to competing risks models. We first review existing methods for competing risk models without the frailty.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 7. Variable Selection for Frailty Models
Abstract
Including only the relevant variables in the model is crucial in statistical inference, improving the quality of estimation, prediction, and interpretation. If there exist many potential variables with equal status, i.e., no prior preference among them, then having as few variables as possible in the model would often facilitate clearer interpretation. When there are many potential variables, over-fitting can become a serious problem. However, missing relevant variables would be also undesirable.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 8. Mixed-Effects Survival Models
Abstract
The frailty model accounts for dependence between survival times, by including a random effect acting multiplicatively on the individual hazard rate.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 9. Joint Model for Repeated Measures and Survival Data
Abstract
In this chapter, through the h-likelihood approach, we study the joint model, for which the response variables of interest would involve repeated measurements over time on the same subject as well as time to an event of interest with or without competing risks.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Chapter 10. Further Topics
Abstract
We have previously presented the h-likelihood procedures for the analysis of survival data under competing risks. There are still many unresolved problems in this area. In this chapter, we present some further topics to highlight that the h-likelihood approach can be extended to more complex multistate survival data. We deal with competing-risks data with missing causes of failure and the semi-competing-risks data.
Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Backmatter
Metadaten
Titel
Statistical Modelling of Survival Data with Random Effects
verfasst von
Il Do Ha
Jong-Hyeon Jeong
Youngjo Lee
Copyright-Jahr
2017
Verlag
Springer Singapore
Electronic ISBN
978-981-10-6557-6
Print ISBN
978-981-10-6555-2
DOI
https://doi.org/10.1007/978-981-10-6557-6