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Springer Statistical Modelling of Survival Data with Random Effects - H-Likelihood

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Description

This comprehensive guide introduces likelihood inference for correlated survival data using the hierarchical (h-) likelihood approach, solving computational challenges that traditional methods cannot handle. Written for statisticians, researchers and practitioners working with clustered or frailty survival data, the book presents an advanced methodology that overcomes the intractable integration problems inherent in conventional likelihood-based approaches. The text combines rigorous mathematical foundations with practical applications, delivering both theoretical derivations and proofs alongside real-world examples. Included software packages in R, particularly the frailtyHL package, enable immediate implementation of these methods. This resource is essential for anyone analysing correlated survival data in medical research, epidemiology or life sciences, offering solutions that bridge the gap between classical survival analysis and modern computational statistics.

Key Features

Introduces hierarchical likelihood (h-likelihood) inference specifically designed for correlated and clustered survival data analysis.

Overcomes computational difficulties and intractable integration problems present in traditional likelihood-based survival methods.

Provides complete technical materials including mathematical derivations and formal proofs throughout each chapter.

Includes R software implementation via the frailtyHL package for practical application of h-likelihood approaches.

Addresses random effects and frailty models in survival analysis with advanced statistical methodology.

Part of Springer's Statistics for Biology and Health series, ensuring peer-reviewed academic rigour and relevance to life sciences research.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
11 December 2018
Listed Since
12 January 2019

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