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Statistical methods for survival data analysis / Elisa T. Lee.

By: Lee, Elisa TMaterial type: TextTextSeries: Wiley series in probability and mathematical statistics. Applied probability and statisticsPublication details: New York : Wiley, c1992. Edition: 2nd edISBN: 0471615927Subject(s): Medicine -- Research -- Statistical methods | Failure time data analysis | Prognosis -- Statistical methodsDDC classification: 610.72 LOC classification: R853.S7
Contents:
1. Introduction. 1.1. Preliminaries. 1.2. Censored Data. 1.3. Scope of the Book. Bibliographical Remarks -- 2. Functions of Survival Time. 2.1. Definitions. 2.2. Relationships of the Survival Functions. Bibliographical Remarks. Exercises -- 3. Examples of Survival Data Analysis. Example 3.1. Comparison of Two Treatments and Three Diets. Example 3.2. Comparison of Two Survival Patterns Using Life Tables. Example 3.3. Fitting Survival Distributions to Remission Data. Example 3.4. Relative Mortality and Identification of Prognostic Factors. Example 3.5. Identification of Risk Factors. Bibliographical Remarks. Exercises -- 4. Nonparametric Methods of Estimating Survival Functions. 4.1. Product-Limit Estimates of Survivorship Function. 4.2. Life-Table Analysis. 4.3. Relative, Five-Year, and Corrected Survival Rates. 4.4. Standardized Rates and Ratios. Bibliographical Remarks. Exercises -- 5. Nonparametric Methods for Comparing Survival Distributions. 5.1. Comparison of Two Survival Distributions. 5.2. The Mantel and Haenszel Test. 5.3. Comparison of K (K>2) Samples. Bibliographical Remarks. Exercises -- 6. Some Well-Known Survival Distributions and Their Applications. 6.1. The Exponential Distribution. 6.2. The Weibull Distribution. 6.3. The Lognormal Distribution. 6.4. The Gamma Distribution. 6.5. Other Survival Distributions. Bibliographical Remarks. Exercises -- 7. Graphical Methods for Survival Distribution Fitting and Goodness-of-Fit Tests. 7.1. Introduction. 7.2. Probability Plotting. 7.3. Hazard Plotting. 7.4. Tests of Goodness-of-Fit. 7.5. Computer Programs for the Gamma Probability Plot. Bibliographical Remarks. Exercises -- 8. Analytical Estimation Procedures for Survival Distributions. 8.1. The Exponential Distribution. 8.2. The Weibull Distribution. 8.3. The Lognormal Distribution. 8.4. The Gamma Distribution. 8.5. A Regression Method for Survival Distribution. Fitting. Bibliographical Remarks. Exercises -- 9. Parametric Methods for Comparing Two Survival Distributions. 9.1. Comparison of Two Exponential Distributions. 9.2. Comparison of Two Weibull Distributions. 9.3. Comparison of Two Gamma Distributions. Bibliographical Remarks. Exercises -- 10. Identification of Prognostic Factors Related to Survival Time. 10.1. Preliminary Examination of Data. 10.2. Nonparametric Methods. 10.3. Parametric Regression Methods. Bibliographical Remarks. Exercises -- 11. Identification of Risk Factors Related to Dichotomous Data. 11.1. Univariate Analysis. 11.2. The Linear Discriminant Function. 11.3. The Linear Logistic Regression Method. Bibliographical Remarks. Exercises -- 12. Planning and Design of Clinical Trials (I). 12.1. History of Clinical Trials. 12.2. Phase I and II Trials. 12.3. Phase III Trials. Bibliographical Remarks. Exercises -- 13. Planning and Design of Clinical Trials (II). 13.1. Preparation of Protocols. 13.2. Randomization. 13.3. The Use of Prognostic Factors in Clinical Trials. 13.4. Controls in Cancer Clinical Studies. Bibliographical Remarks. Exercises. Appendix A The Newton-Raphson Method -- Appendix B Computer Program GAMPLOT -- Appendix C Statistical Tables.
Summary: Defined in a broad sense, survival data refers to data that involve the remaining time until a certain event such as a death a relapse or the onset of a disease. Over the past decade, applications of the statistical methods for survival data analysis have extended far beyond biomedical and reliability research to a host of other fields, including criminology, sociology, marketing, and health insurance practice. Statistical Methods for Survival Data Analysis, Second Edition has been designed to meet the need for a single volume covering the many methodologies appropriate for the analysis of survival data. Emphasizing applications over rigorous mathematics, this extremely useful reference provides thorough discussion of the most commonly used parametric and nonparametric methods in survival data analysis, as well as guidelines for the planning and design of clinical trials. These statistical methods and guidelines are applicable to many types of research, including clinical investigations, epidemiologic studies, social science research, and studies in other areas. Numerous real-life examples are employed to illustrate key concepts and several large data sets are included as examples. Other features include discussion of clinical life-table analysis and population life-tables; a goodness-of-fit test for modeling survival data that involve censored observations; expanded treatment of Cox's proportional hazards model; discussion of the relationship between odds ratios and coefficients of the linear logistic regression model; discussion of computer programs for survival data analysis that generate the gamma probability plot; methods for determining sample sizes in clinical survival time trials; discussion of the issues of repeated significance testing and group; sequential design; and a reference list which includes a large number of recently published papers. Statistical Methods for Survival Data Analysis, Second Edition is an essential reference for biomedical investigators, statisticians, epidemiologists, and researchers in other disciplines involved or interested in the analysis of survival data.
Holdings
Item type Home library Call number Status Date due Barcode Item holds
Two Week Loan Two Week Loan College Lane Learning Resources Centre
Main Shelves 		610.72 LEE (Browse shelf(Opens below)) Available 4403730527
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Enhanced descriptions from Syndetics:

"A Wiley-Interscience publication.".

Includes bibliographical references (p. 453-477) and index.

1. Introduction. 1.1. Preliminaries. 1.2. Censored Data. 1.3. Scope of the Book. Bibliographical Remarks -- 2. Functions of Survival Time. 2.1. Definitions. 2.2. Relationships of the Survival Functions. Bibliographical Remarks. Exercises -- 3. Examples of Survival Data Analysis. Example 3.1. Comparison of Two Treatments and Three Diets. Example 3.2. Comparison of Two Survival Patterns Using Life Tables. Example 3.3. Fitting Survival Distributions to Remission Data. Example 3.4. Relative Mortality and Identification of Prognostic Factors. Example 3.5. Identification of Risk Factors. Bibliographical Remarks. Exercises -- 4. Nonparametric Methods of Estimating Survival Functions. 4.1. Product-Limit Estimates of Survivorship Function. 4.2. Life-Table Analysis. 4.3. Relative, Five-Year, and Corrected Survival Rates. 4.4. Standardized Rates and Ratios. Bibliographical Remarks. Exercises -- 5. Nonparametric Methods for Comparing Survival Distributions. 5.1. Comparison of Two Survival Distributions. 5.2. The Mantel and Haenszel Test. 5.3. Comparison of K (K>2) Samples. Bibliographical Remarks. Exercises -- 6. Some Well-Known Survival Distributions and Their Applications. 6.1. The Exponential Distribution. 6.2. The Weibull Distribution. 6.3. The Lognormal Distribution. 6.4. The Gamma Distribution. 6.5. Other Survival Distributions. Bibliographical Remarks. Exercises -- 7. Graphical Methods for Survival Distribution Fitting and Goodness-of-Fit Tests. 7.1. Introduction. 7.2. Probability Plotting. 7.3. Hazard Plotting. 7.4. Tests of Goodness-of-Fit. 7.5. Computer Programs for the Gamma Probability Plot. Bibliographical Remarks. Exercises -- 8. Analytical Estimation Procedures for Survival Distributions. 8.1. The Exponential Distribution. 8.2. The Weibull Distribution. 8.3. The Lognormal Distribution. 8.4. The Gamma Distribution. 8.5. A Regression Method for Survival Distribution. Fitting. Bibliographical Remarks. Exercises -- 9. Parametric Methods for Comparing Two Survival Distributions. 9.1. Comparison of Two Exponential Distributions. 9.2. Comparison of Two Weibull Distributions. 9.3. Comparison of Two Gamma Distributions. Bibliographical Remarks. Exercises -- 10. Identification of Prognostic Factors Related to Survival Time. 10.1. Preliminary Examination of Data. 10.2. Nonparametric Methods. 10.3. Parametric Regression Methods. Bibliographical Remarks. Exercises -- 11. Identification of Risk Factors Related to Dichotomous Data. 11.1. Univariate Analysis. 11.2. The Linear Discriminant Function. 11.3. The Linear Logistic Regression Method. Bibliographical Remarks. Exercises -- 12. Planning and Design of Clinical Trials (I). 12.1. History of Clinical Trials. 12.2. Phase I and II Trials. 12.3. Phase III Trials. Bibliographical Remarks. Exercises -- 13. Planning and Design of Clinical Trials (II). 13.1. Preparation of Protocols. 13.2. Randomization. 13.3. The Use of Prognostic Factors in Clinical Trials. 13.4. Controls in Cancer Clinical Studies. Bibliographical Remarks. Exercises. Appendix A The Newton-Raphson Method -- Appendix B Computer Program GAMPLOT -- Appendix C Statistical Tables.

Defined in a broad sense, survival data refers to data that involve the remaining time until a certain event such as a death a relapse or the onset of a disease. Over the past decade, applications of the statistical methods for survival data analysis have extended far beyond biomedical and reliability research to a host of other fields, including criminology, sociology, marketing, and health insurance practice. Statistical Methods for Survival Data Analysis, Second Edition has been designed to meet the need for a single volume covering the many methodologies appropriate for the analysis of survival data. Emphasizing applications over rigorous mathematics, this extremely useful reference provides thorough discussion of the most commonly used parametric and nonparametric methods in survival data analysis, as well as guidelines for the planning and design of clinical trials. These statistical methods and guidelines are applicable to many types of research, including clinical investigations, epidemiologic studies, social science research, and studies in other areas. Numerous real-life examples are employed to illustrate key concepts and several large data sets are included as examples. Other features include discussion of clinical life-table analysis and population life-tables; a goodness-of-fit test for modeling survival data that involve censored observations; expanded treatment of Cox's proportional hazards model; discussion of the relationship between odds ratios and coefficients of the linear logistic regression model; discussion of computer programs for survival data analysis that generate the gamma probability plot; methods for determining sample sizes in clinical survival time trials; discussion of the issues of repeated significance testing and group; sequential design; and a reference list which includes a large number of recently published papers. Statistical Methods for Survival Data Analysis, Second Edition is an essential reference for biomedical investigators, statisticians, epidemiologists, and researchers in other disciplines involved or interested in the analysis of survival data.

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