{"product_id":"statistical-methods-for-survival-data-analysis-9781118095027","title":"Statistical Methods for Survival Data Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePraise for the \u003ci\u003eThird Edition\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject. \u003ci\u003eStatistics in Medical Research\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eUpdated and expanded to reflect the latest developments, \u003ci\u003eStatistical Methods for Survival Data Analysis, Fourth Edition\u003c\/i\u003e continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences.\u003c\/p\u003e \u003cp\u003eEmphasizing the latest research and providing the most up-to-date information regarding software applications in the field, \u003ci\u003eSt\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e“In summary, this book continues to improve, and the fourth edition is a welcome addition to the available books on survival analysis. The expanded sections on modelling and the addition of R software examples are particularly helpful.”  (\u003ci\u003eInternational Statistical Review\u003c\/i\u003e, 1 October 2015)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePreface xi\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 1.1 Preliminaries 1\u003cbr\u003e \u003cbr\u003e 1.2 Censored Data 2\u003cbr\u003e \u003cbr\u003e 1.3 Scope of the Book 5\u003cbr\u003e \u003cbr\u003e \u003cb\u003e2 Functions of Survival Time 8\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 2.1 Definitions 8\u003cbr\u003e \u003cbr\u003e 2.2 Relationships of the Survival Functions 15\u003cbr\u003e \u003cbr\u003e Exercises 16\u003cbr\u003e \u003cbr\u003e \u003cb\u003e3 Examples of Survival Data Analysis 19\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 3.1 Example 3.1: Comparison of Two Treatments and Three Diets 19\u003cbr\u003e \u003cbr\u003e 3.2 Example 3.2: Comparison of Two Survival Patterns Using Life Tables 26\u003cbr\u003e \u003cbr\u003e 3.3 Example 3.3: Fitting Survival Distributions to Tumor-Free Times 28\u003cbr\u003e \u003cbr\u003e 3.4 Example 3.4: Comparing Survival of a Cohort with that of a General Population — Relative Survival 30\u003cbr\u003e \u003cbr\u003e 3.5 Example 3.5: Identification of Risk Factors for Incident Events 33\u003cbr\u003e \u003cbr\u003e 3.6 Example 3.6: Identification of Risk Factors for the Prevalence of Age-Related Macular Degeneration 38\u003cbr\u003e \u003cbr\u003e 3.7 Example 3.7: Identification of Significant Risk Factors for Incident Hypertension Using Related Data (Repeated Measurements) in a Longitudinal Study 46\u003cbr\u003e \u003cbr\u003e Exercises 54\u003cbr\u003e \u003cbr\u003e \u003cb\u003e4 Nonparametric Methods of Estimating Survival Functions 68\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 4.1 Product-Limit Estimates of Survivorship Function 69\u003cbr\u003e \u003cbr\u003e 4.2 N elson–Aalen Estimates of Survivorship Function 82\u003cbr\u003e \u003cbr\u003e 4.3 Life-Table Analysis 83\u003cbr\u003e \u003cbr\u003e 4.4 Relative Survival Rates 96\u003cbr\u003e \u003cbr\u003e 4.5 Standardized Rates and Ratios 98\u003cbr\u003e \u003cbr\u003e Exercises 104\u003cbr\u003e \u003cbr\u003e \u003cb\u003e5 Nonparametric Methods for Comparing Survival Distributions 108\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 5.1 Comparison of Two Survival Distributions 108\u003cbr\u003e \u003cbr\u003e 5.2 The Mantel and Haenszel Test 123\u003cbr\u003e \u003cbr\u003e 5.3 Comparison of K (K \u0026gt; 2) Samples 128\u003cbr\u003e \u003cbr\u003e Exercises 130\u003cbr\u003e \u003cbr\u003e \u003cb\u003e6 Some Well-Known Parametric Survival Distributions And Their Applications 133\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 6.1 Exponential Distribution 133\u003cbr\u003e \u003cbr\u003e 6.2 Weibull Distribution 138\u003cbr\u003e \u003cbr\u003e 6.3 Lognormal Distribution 143\u003cbr\u003e \u003cbr\u003e 6.4 Gamma, Generalized Gamma, and Extended Generalized Gamma Distributions 148\u003cbr\u003e \u003cbr\u003e 6.5 Log-Logistic Distribution 153\u003cbr\u003e \u003cbr\u003e 6.6 O ther Survival Distributions 155\u003cbr\u003e \u003cbr\u003e Exercises 159\u003cbr\u003e \u003cbr\u003e \u003cb\u003e7 Estimation Procedures for Parametric Survival Distributions Without Covariates 161\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 7.1 General Maximum Likelihood Estimation Procedure 161\u003cbr\u003e \u003cbr\u003e 7.2 Exponential Distribution 165\u003cbr\u003e \u003cbr\u003e 7.3 Weibull Distribution 178\u003cbr\u003e \u003cbr\u003e 7.4 Lognormal Distribution 180\u003cbr\u003e \u003cbr\u003e 7.5 The Extended Generalized Gamma Distribution 183\u003cbr\u003e \u003cbr\u003e 7.6 The Log-Logistic Distribution 184\u003cbr\u003e \u003cbr\u003e 7.7 Gompertz Distribution 185\u003cbr\u003e \u003cbr\u003e 7.8 Graphical Methods 186\u003cbr\u003e \u003cbr\u003e Exercises 203\u003cbr\u003e \u003cbr\u003e \u003cb\u003e8 Tests of Goodness-of-Fit and Distribution Selection 206\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 8.1 Goodness-of-Fit Test Statistics Based on Asymptotic Likelihood Inferences 207\u003cbr\u003e \u003cbr\u003e 8.2 Tests for Appropriateness of a Family of Distributions 210\u003cbr\u003e \u003cbr\u003e 8.3 Selection of a Distribution by Using BIC or AIC Procedure 216\u003cbr\u003e \u003cbr\u003e 8.4 Tests for a Specific Distribution with Known Parameters 217\u003cbr\u003e \u003cbr\u003e 8.5 Hollander and Proschan’s Test for Appropriateness of a Given Distribution with Known Parameters 220\u003cbr\u003e \u003cbr\u003e Exercises 224\u003cbr\u003e \u003cbr\u003e \u003cb\u003e9 Parametric Methods for Comparing Two Survival Distributions 226\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 9.1 Log-Likelihood Ratio Test for Comparing Two Survival Distributions 226\u003cbr\u003e \u003cbr\u003e 9.2 Comparison of Two Exponential Distributions 229\u003cbr\u003e \u003cbr\u003e 9.3 Comparison of Two Weibull Distributions 234\u003cbr\u003e \u003cbr\u003e 9.4 Comparison of Two Gamma Distributions 236\u003cbr\u003e \u003cbr\u003e Exercises 237\u003cbr\u003e \u003cbr\u003e \u003cb\u003e10 Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors 239\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 10.1 Preliminary Examination of Data 240\u003cbr\u003e \u003cbr\u003e 10.2 General Structure of Parametric Regression Models and Their Asymptotic Likelihood Inference 242\u003cbr\u003e \u003cbr\u003e 10.3 Exponential AFT Model 246\u003cbr\u003e \u003cbr\u003e 10.4 Weibull AFT Model 255\u003cbr\u003e \u003cbr\u003e 10.5 Lognormal AFT Model 258\u003cbr\u003e \u003cbr\u003e 10.6 The Extended Generalized Gamma AFT Model 261\u003cbr\u003e \u003cbr\u003e 10.7 Log-Logistic AFT Model 264\u003cbr\u003e \u003cbr\u003e 10.8 O ther Parametric Regression Models 268\u003cbr\u003e \u003cbr\u003e 10.9 Model Selection Methods 270\u003cbr\u003e \u003cbr\u003e Exercises 279\u003cbr\u003e \u003cbr\u003e \u003cb\u003e11 Identification of Risk Factors Related to Survival Time: Cox Proportional Hazards Model 282\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 11.1 The Proportional Hazards Model 282\u003cbr\u003e \u003cbr\u003e 11.2 The Partial Likelihood Function 285\u003cbr\u003e \u003cbr\u003e 11.3 Identification of Significant Covariates 302\u003cbr\u003e \u003cbr\u003e 11.4 Estimation of the Survivorship Function with Covariates 309\u003cbr\u003e \u003cbr\u003e 11.5 Adequacy Assessment of the Proportional Hazards Model 317\u003cbr\u003e \u003cbr\u003e Exercises 334\u003cbr\u003e \u003cbr\u003e \u003cb\u003e12 Identification of Prognostic Factors Related to Survival Time: Non-Proportional Hazards Models 337\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 12.1 Models with Time-Dependent Covariates 337\u003cbr\u003e \u003cbr\u003e 12.2 Stratified Proportional Hazards Model 346\u003cbr\u003e \u003cbr\u003e 12.3 Competing Risks Model 350\u003cbr\u003e \u003cbr\u003e 12.4 Recurrent Event Models 353\u003cbr\u003e \u003cbr\u003e 12.5 Models for Related Observations 370\u003cbr\u003e \u003cbr\u003e Exercises 382\u003cbr\u003e \u003cbr\u003e \u003cb\u003e13 Identification of Risk Factors Related to Dichotomous and Polychotomous Outcomes 384\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 13.1 Univariate Analysis 385\u003cbr\u003e \u003cbr\u003e 13.2 Logistic and Conditional Logistic Regression Model for Dichotomous Outcomes 392\u003cbr\u003e \u003cbr\u003e 13.3 Models for Polychotomous Outcomes 421\u003cbr\u003e \u003cbr\u003e 13.4 Models for Related Observations 432\u003cbr\u003e \u003cbr\u003e Exercises 440\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAppendix 443\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003eReferences 466\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003eIndex 477\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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