Modelling Survival Data in Medical Research
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Modelling Survival Data in Medical Research: (Chapman & Hall/CRC Texts in Statistical Science)

Modelling Survival Data in Medical Research: (Chapman & Hall/CRC Texts in Statistical Science)


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About the Book

Data collected on the time to an event-such as the death of a patient in a medical study-is known as survival data. The methods for analyzing survival data can also be used to analyze data on the time to events such as the recurrence of a disease or relief from symptoms. Modelling Survival Data in Medical Research begins with an introduction to sur

Table of Contents:
Survival Analysis Special Features of Survival Data Some Examples Survivor Function and Hazard Function Further Reading Some Non-Parametric Procedures Estimating the Survivor Function Estimating the Hazard Function Estimating the Median and Percentiles of Survival Times Confidence Intervals for the Median and Percentiles Comparison of Two Groups of Survival Data Comparison of Three or More Groups of Survival Data Stratified Tests Log-Rank Test for Trend Further Reading Modelling Survival Data Modelling the Hazard Function The Linear Component of the Proportional Hazards Model Fitting the Proportional Hazards Model Confidence Intervals and Hypothesis for the ss's Comparing Alternative Models Strategy for Model Selection Interpretation of Parameter Estimates Estimating the Hazard and Survivor Functions Proportional Hazards Modelling and the Log-Rank Test The Weibull Model for Survival Data Models for the Hazard Function Assessing the Suitability of a Parametric Model Fitting a Parametric Model to a Single Sample A Model for the Comparison of Two Groups The Weibull Proportional Hazards Model Comparing Alternative Weibull Models An Alternative Form of the Proportional Hazards Model Further Reading Model Checking in the Proportional Hazards Model Residuals for the Cox Regression Model Plots Based on Residuals Some Comments and Recommendations Identification of Influential Observations Treatment of Influential Observations Residuals for the Weibull Proportional Hazards Model Identification of Influential Observations Testing the Assumption of Proportional Hazards Further Reading Some Other Parametric Models for Survival Data Probability Distributions for Survival Data Exploratory Analyses The Accelerated Failure Time Model Log-Linear Form of the Accelerated Failure Time Model Fitting and Comparing Accelerated Failure Time Models The Proportional Odds Model Further Reading Time Dependent Variables Types of Time-Dependent Variable A Model with Time-Dependent Variables Some Applications of Time-Dependent Variables Comparison of Treatments Two Examples Further Examples Interval-Censored Survival Data Modelling Interval-Censored Survival Data Modelling the Recurrence Probability in the Follow-Up Period Modelling the Recurrence Probability at Different Times Discussion Further Reading Sample Size Requirements for a Survival Study Distinguishing between Two Treatment Groups Calculating the Required Number of Deaths Calculating the Required Number of Patients Further Reading Some Additional Topics Non-Proportional Hazards Model Choice Informative Censoring Multistate Models Computer Software for Survival Analysis Computational Methods Used in Packages for Survival Analysis SAS BMDP SPSS GLIM and Genstat Illustrations of the Use of SAS, BMDP and SPSS SAS Macros for Model Checking Relative Merits of SAS, BMDP and SPSS for Survival Analysis Appendix A: Maximum Likelihood Estimation A.1: Inference about a Single Unknown Parameter A.2: Inference about a Vector of Unknown Parameters Appendix B: Standard Error of Weibull Percentiles B.1: Standard Error of a Percentile of the Weibull Distribution B.2: Standard Error of a Percentile in the Weibull Model Appendix C: Two SAS Macros C.1: The SAS Macro coxdiag C.2: The SAS Macro weibdiag References Index of Examples Index

Review :
"In summary…a useful book that has particular merit for the applied statistician. Chapters 1-6 and 11 alone supply a wonderful introduction to survival analysis. The mathematical statistician unfamiliar with survival analysis who desires to become quickly abreast will also gain much from the book." Journal of the American Statistical Association "Students found the presentation of the material and examples to be very helpful…an excellent book…I highly recommend this book for practising statisticians engaged in analysing univariate survival data." …This book will not only serve the statistical practitioner in the medical and pharmaceutical research areas well, but will be a convenient text for the lecturer aiming to include a useful applied component into a post-graduate statistics or operational research degree course." Journal of the Royal Statistics Society "The book would be a popular text for courses and a well-thumbed addition to any medical statistician's collection. It is sufficiently general to be of interest to industrial statisticians concerned with lifetime testing but the focus is clearly on survival of patients under treatment." The Statistician


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Product Details
  • ISBN-13: 9781135441395
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: Chapman and Hall
  • Language: English
  • No of Pages: 368
  • ISBN-10: 1135441391
  • Publisher Date: 01 Dec 1993
  • Binding: Digital (delivered electronically)
  • No of Pages: 368
  • Series Title: Chapman & Hall/CRC Texts in Statistical Science


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Modelling Survival Data in Medical Research: (Chapman & Hall/CRC Texts in Statistical Science)
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