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Applied Survival Analysis Using R 1st ed. 2016 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 226 pages, aukštis x plotis: 235x155 mm, weight: 3927 g, 26 Illustrations, color; 40 Illustrations, black and white; XIV, 226 p. 66 illus., 26 illus. in color., 1 Paperback / softback
  • Serija: Use R!
  • Išleidimo metai: 20-May-2016
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 331931243X
  • ISBN-13: 9783319312439
Kitos knygos pagal šią temą:
Applied Survival Analysis Using R 1st ed. 2016Didesnis paveikslėlis
  • Formatas: Paperback / softback, 226 pages, aukštis x plotis: 235x155 mm, weight: 3927 g, 26 Illustrations, color; 40 Illustrations, black and white; XIV, 226 p. 66 illus., 26 illus. in color., 1 Paperback / softback
  • Serija: Use R!
  • Išleidimo metai: 20-May-2016
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 331931243X
  • ISBN-13: 9783319312439
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319312439.html
Raktažodžiai:
Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis.

Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.

Recenzijos

This book describes the principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R. The intended audience includes students taking a master's level course in statistical theory and analysts who need to work with survival time data. This is an excellent overview of the main principles of survival analysis and its applications with examples using R for the intended audience. (Hemang B. Panchal, Doodys Book Reviews, August, 2016)

1 Introduction
1(10)
1.1 What is Survival Analysis?
1(1)
1.2 What You Need to Know to Use This Book
2(1)
1.3 Survival Data and Censoring
2(4)
1.4 Some Examples of Survival Data Sets
6(3)
1.5 Additional Notes
9(2)
2 Basic Principles of Survival Analysis
11(14)
2.1 The Hazard and Survival Functions
11(2)
2.2 Other Representations of a Survival Distribution
13(1)
2.3 Mean and Median Survival Time
14(1)
2.4 Parametric Survival Distributions
15(4)
2.5 Computing the Survival Function from the Hazard Function
19(1)
2.6 A Brief Introduction to Maximum Likelihood Estimation
20(3)
2.7 Additional Notes
23(2)
3 Nonparametric Survival Curve Estimation
25(18)
3.1 Nonparametric Estimation of the Survival Function
25(5)
3.2 Finding the Median Survival and a Confidence Interval for the Median
30(2)
3.3 Median Follow-Up Time
32(1)
3.4 Obtaining a Smoothed Hazard and Survival Function Estimate
32(4)
3.5 Left Truncation
36(5)
3.6 Additional Notes
41(2)
4 Nonparametric Comparison of Survival Distributions
43(12)
4.1 Comparing Two Groups of Survival Times
43(6)
4.2 Stratified Tests
49(3)
4.3 Additional Note
52(3)
5 Regression Analysis Using the Proportional Hazards Model
55(18)
5.1 Covariates and Nonparametric Survival Models
55(1)
5.2 Comparing Two Survival Distributions Using a Partial Likelihood Function
56(3)
5.3 Partial Likelihood Hypothesis Tests
59(4)
5.3.1 The Wald Test
60(1)
5.3.2 The Score Test
60(1)
5.3.3 The Likelihood Ratio Test
60(3)
5.4 The Partial Likelihood with Multiple Covariates
63(1)
5.5 Estimating the Baseline Survival Function
64(1)
5.6 Handling of Tied Survival Times
65(4)
5.7 Left Truncation
69(2)
5.8 Additional Notes
71(2)
6 Model Selection and Interpretation
73(14)
6.1 Covariate Adjustment
73(1)
6.2 Categorical and Continuous Covariates
74(4)
6.3 Hypothesis Testing for Nested Models
78(3)
6.4 The Akaike Information Criterion for Comparing Non-nested Models
81(3)
6.5 Including Smooth Estimates of Continuous Covariates in a Survival Model
84(2)
6.6 Additional Note
86(1)
7 Model Diagnostics
87(14)
7.1 Assessing Goodness of Fit Using Residuals
87(7)
7.1.1 Martingale and Deviance Residuals
87(5)
7.1.2 Case Deletion Residuals
92(2)
7.2 Checking the Proportion Hazards Assumption
94(6)
7.2.1 Log Cumulative Hazard Plots
94(2)
7.2.2 Schoenfeld Residuals
96(4)
7.3 Additional Note
100(1)
8 Time Dependent Covariates
101(12)
8.1 Introduction
101(5)
8.2 Predictable Time Dependent Variables
106(4)
8.2.1 Using the Time Transfer Function
107(2)
8.2.2 Time Dependent Variables That Increase Linearly with Time
109(1)
8.3 Additional Note
110(3)
9 Multiple Survival Outcomes and Competing Risks
113(24)
9.1 Clustered Survival Times and Frailty Models
113(8)
9.1.1 Marginal Survival Models
115(1)
9.1.2 Frailty Survival Models
116(1)
9.1.3 Accounting for Family-Based Clusters in the "ashkenazi" Data
117(3)
9.1.4 Accounting for Within-Person Pairing of Eye Observations in the Diabetes Data
120(1)
9.2 Cause-Specific Hazards
121(13)
9.2.1 Kaplan-Meier Estimation with Competing Risks
121(2)
9.2.2 Cause-Specific Hazards and Cumulative Incidence Functions
123(3)
9.2.3 Cumulative Incidence Functions for Prostate Cancer Data
126(2)
9.2.4 Regression Methods for Cause-Specific Hazards
128(3)
9.2.5 Comparing the Effects of Covariates on Different Causes of Death
131(3)
9.3 Additional Notes
134(3)
10 Parametric Models
137(20)
10.1 Introduction
137(1)
10.2 The Exponential Distribution
137(1)
10.3 The Weibull Model
138(15)
10.3.1 Assessing the Weibull Distribution as a Model for Survival Data in a Single Sample
138(3)
10.3.2 Maximum Likelihood Estimation of Weibull Parameters for a Single Group of Survival Data
141(1)
10.3.3 Profile Weibull Likelihood
142(1)
10.3.4 Selecting a Weibull Distribution to Model Survival Data
143(3)
10.3.5 Comparing Two Weibull Distributions Using the Accelerated Failure Time and Proportional Hazards Models
146(2)
10.3.6 A Regression Approach to the Weibull Model
148(1)
10.3.7 Using the Weibull Distribution to Model Survival Data with Multiple Covariates
149(2)
10.3.8 Model Selection and Residual Analysis with Weibull Survival Data
151(2)
10.4 Other Parametric Survival Distributions
153(1)
10.5 Additional Note
154(3)
11 Sample Size Determination for Survival Studies
157(20)
11.1 Power and Sample Size for a Single Arm Study
157(4)
11.2 Determining the Probability of Death in a Clinical Trial
161(2)
11.3 Sample Size for Comparing Two Exponential Survival Distributions
163(2)
11.4 Sample Size for Comparing Two Survival Distributions Using the Log-Rank Test
165(1)
11.5 Determining the Probability of Death from a Non-parametric Survival Curve Estimate
166(3)
11.6 Example: Calculating the Required Number of Patients for a Randomized Study of Advanced Gastric Cancer Patients
169(1)
11.7 Example: Calculating the Required Number of Patients for a Randomized Study of Patients with Metastatic Colorectal Cancer
170(1)
11.8 Using Simulations to Estimate Power
171(3)
11.9 Additional Notes
174(3)
12 Additional Topics
177(24)
12.1 Using Piecewise Constant Hazards to Model Survival Data
177(10)
12.2 Interval Censoring
187(5)
12.3 The Lasso Method for Selecting Predictive Biomarkers
192(9)
A A Basic Guide to Using R for Survival Analysis
201(17)
A.1 The R System
201(11)
A.1.1 A First R Session
202(2)
A.1.2 Scatterplots and Fitting Linear Regression Models
204(3)
A.1.3 Accommodating Non-linear Relationships
207(2)
A.1.4 Data Frames and the Search Path for Variable Names
209(2)
A.1.5 Defining Variables Within a Data Frame
211(1)
A.1.6 Importing and Exporting Data Frames
211(1)
A.2 Working with Dates in R
212(3)
A.2.1 Dates and Leap Years
213(1)
A.2.2 Using the "as.date" Function
213(2)
A.3 Presenting Coefficient Estimates Using Forest Plots
215(2)
A.4 Extracting the Log Partial Likelihood and Coefficient Estimates from a coxph Object
217(1)
References 218(5)
Index 223(2)
R Package Index 225
Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.