Survival Analysis in Medicine and Genetics Chapman & Hall/CRC Biostatistics Series
Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields.
The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples.
Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.
Introduction: Examples and Basic Principles. Analysis Trilogy: Estimation, Test, and Regression. Analysis of Interval Censored Data. Special Modeling Methodology. Diagnostic Medicine for Survival Analysis. Survival Analysis with High-Dimensional Covariates. Bibliography. Index.
Jialiang Li is an associate professor in the Department of Statistics and Applied Probability at the National University of Singapore, an associate professor at the Duke-NUS Graduate Medical School, and a scientist at the Singapore Eye Research Institute. He is on the editorial board of Biometrics and has published 70 peer-reviewed research papers in scientific journals. He has been a recipient the Young Scientist Award from the National University of Singapore and the New Investigator Grant and Cooperative Basic Research Grant from the National Medical Research Council.
Shuangge Ma is an associate professor in the Department of Biostatistics, Yale School of Public Health at Yale University. He earned a PhD in statistics from the University of Wisconsin and completed postdoctoral training in the Department of Biostatistics at the University of Washington. His research interests include survival analysis, semiparametric methods, bioinformatics, cancer studies, and health economics.
Date de parution : 01-2023
15.6x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 53,83 €
Ajouter au panierDate de parution : 07-2013
15.6x23.4 cm
Thèmes de Survival Analysis in Medicine and Genetics :
Mots-clés :
Data Set; Cox Model; Statistical Developments In Medicine And Genetics; Cox PH Model; Fine-Gray Model For Competing Risks Data; Additive Risk Model; Variable Selection Methods; Roc Curve; Censored Survival Time Data In Medical And Genetic Research; Aft Model; Analyzing High-Dimensional Survival Data; Iterative Convex Minorant Algorithm; Nonparametric Regression For Survival Analysis; Panel Count Data; High-Dimensional Genetic Data Analysis; Diagnostic Accuracy Measures; Time-Dependent Diagnostic Accuracy Studies; Continuous Diagnostic Test; Interval Censored; Sample Size Calculation; Survival Function; Km Estimator; Weighted Bootstrap; Scad Penalty; Bridge Penalty; Roc Analysis; Kaplan Meier Weights; Frailty Model; High Dimensional Covariates; Log Rank Test; Binary Diagnostic Test; Van Der Vaart; Lasso Estimate