Book•
The Frailty Model
23 Oct 2007-
TL;DR: In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data.
Abstract: Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.
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TL;DR: Treatment with subcutaneous canakinumab once every 8 weeks was associated with a rapid remission of symptoms in most patients with CAPS and evaluated therapeutic responses using disease-activity scores and analysis of levels of C-reactive protein (CRP) and serum amyloid A protein (SAA).
Abstract: Background The cryopyrin-associated periodic syndrome (CAPS) is a rare inherited inflammatory disease associated with overproduction of interleukin-1. Canakinumab is a human anti–interleukin-1β monoclonal antibody. Methods We performed a three-part, 48-week, double-blind, placebo-controlled, randomized withdrawal study of canakinumab in patients with CAPS. In part 1, 35 patients received 150 mg of canakinumab subcutaneously. Those with a complete response to treatment entered part 2 and were randomly assigned to receive either 150 mg of canakinumab or placebo every 8 weeks for up to 24 weeks. After the completion of part 2 or at the time of relapse, whichever occurred first, patients proceeded to part 3 and received at least two more doses of canakinumab. We evaluated therapeutic responses using disease-activity scores and analysis of levels of C-reactive protein (CRP) and serum amyloid A protein (SAA). Results In part 1 of the study, 34 of the 35 patients (97%) had a complete response to canakinumab. Of ...
751 citations
TL;DR: Three families of regression models for the analysis of multilevel survival data incorporate cluster-specific random effects that modify the baseline hazard function and can be incorporated to account for within-cluster homogeneity in outcomes.
Abstract: Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).
283 citations
Cites background from "The Frailty Model"
...We refer the interested reader to comprehensive discussions of frailty models (Wienke, 2011; Hougaard, 2000; Duchateau & Janssen, 2008)....
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...…distribution, the log-normal distribution (the frailty terms will have a log-normal distribution while the random effects will have a normal distribution), positive stable frailty distributions and power variance function distributions (Hougaard, 2000; Wienke, 2011; Duchateau & Janssen, 2008)....
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TL;DR: Copula modeling has taken the world of finance and insurance, and well beyond, by storm as mentioned in this paper, and why is this? In this article, I review the early start of this development, discuss some important current research, mainly from an applications point of view, and comment on potential future developments.
Abstract: Copula modeling has taken the world of finance and insurance, and well beyond, by storm. Why is this? In this article, I review the early start of this development, discuss some important current research, mainly from an applications point of view, and comment on potential future developments. An alternative title of the article would be "Demystifying the copula craze." The article also contains what I would like to call the copula must-reads.
246 citations
TL;DR: This article will present a review of the many approaches proposed in the statistical literature, and four main model families will be presented, discussed and compared.
Abstract: Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.
241 citations
TL;DR: The aim of this article is to present the new version of an R package called frailtypack, which allows to fit Cox models and four types of frailty models (shared, nested, joint, additive) that could be useful for several issues within biomedical research.
Abstract: Frailty models are very useful for analysing correlated survival data, when observations are clustered into groups or for recurrent events. The aim of this article is to present the new version of an R package called frailtypack. This package allows to fit Cox models and four types of frailty models (shared, nested, joint, additive) that could be useful for several issues within biomedical research. It is well adapted to the analysis of recurrent events such as cancer relapses and/or terminal events (death or lost to follow-up). The approach uses maximum penalized likelihood estimation. Right-censored or left-truncated data are considered. It also allows stratification and time-dependent covariates during analysis.
215 citations
Cites methods from "The Frailty Model"
...Frailty models (Duchateau and Janssen 2008; Hougaard 2000; Wienke 2010; Hanagal 2011) are extensions of the Cox proportional hazards model (Cox 1972) which is the most popular model in survival analysis....
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...Fit the different models and print the parameter estimates The objective of the study described in Gonzalez et al. (2005) is to analyse the hospital readmission times related to colorectal cancer after surgical procedure....
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