An introduction to survival analysis using Stata / Mario A. Cleves ... [and others].

Format:

Book

Author/Creator:

Cleves, Mario Alberto, 1954-

Language:

English

Subjects (All):

Statistics--Econometric models.

Statistics.

Survival analysis (Biometry).

Stata.

Econometric models.

Physical Description:

372 pages : illustrations ; 24 cm

Edition:

Second edition.

Place of Publication:

College Station, Tex. : Stata Press, 2008.

Summary:

An Introduction to Survival Analysis Using Stata, Second Edition provides new researchers with the foundation for understanding the various approaches for analyzing time-to-event data. This book serves not only as a tutorial for those wishing to learn survival analysis but as a valuable reference for experienced researchers interested in using Stata to analyze survival data.

The book is written for professional researchers from all disciplines, including biostatistics, epidemiology, public health, medicine, sociology, economics, political science, engineering, and other fields where survival analysis is applicable. Although the book assumes knowledge of statistical principles, basic probability, and a working knowledge of Stata, it is practical rather than mathematical in its approach to the subject. The reader of this book should come away not just with an understanding of the formulas but with an intuition of how the various survival analysis estimators work and what information they exploit. The reader will also come away with a deeper and more comprehensive knowledge of the syntax, features, and underpinnings of Stata's survival analysis routines.

The second edition has been updated to highlight the new features of Stata 10, in particular, power and sample-size calculations for survival data. Among the other additions are in-graph at-risk tables for Kaplan-Meier and related curves, survival analysis for survey data, and regression models with flexible functional forms via fractional polynomials. The authors are also the authors of Stata statistical software, in particular, Stata's widely used survival analysis suite.

Contents:

1 The problem of survival analysis 1

1.1 Parametric modeling 2

1.2 Semiparametric modeling 3

1.3 Nonparametric analysis 5

1.4 Linking the three approaches 5

2 Describing the distribution of failure times 7

2.1 The survivor and hazard functions 7

2.2 The quantile function 10

2.3 Interpreting the cumulative hazard and hazard rate 13

2.3.1 Interpreting the cumulative hazard 13

2.3.2 Interpreting the hazard rate 15

2.4 Means and medians 16

3 Hazard models 19

3.1 Parametric models 20

3.2 Semiparametric models 21

3.3 Analysis time (time at risk) 24

4 Censoring and truncation 29

4.1 Censoring 29

4.1.1 Right censoring 30

4.1.2 Interval censoring 32

4.1.3 Left censoring 34

4.2 Truncation 34

4.2.1 Left truncation (delayed entry) 35

4.2.2 Interval truncation (gaps) 36

4.2.3 Right truncation 36

5 Recording survival data 37

5.1 The desired format 37

5.2 Other formats 40

5.3 Example: Wide-form snapshot data 44

6 Using stset 47

6.1 A short lesson on dates 48

6.2 Purposes of the stset command 51

6.3 Syntax of the stset command 51

6.3.1 Specifying analysis time 52

6.3.2 Variables defined by stset 55

6.3.3 Specifying what constitutes failure 57

6.3.4 Specifying when subjects exit from the analysis 59

6.3.5 Specifying when subjects enter the analysis 62

6.3.6 Specifying the subject-ID variable 65

6.3.7 Specifying the begin-of-span variable 67

6.3.8 Convenience options 70

7 After stset 73

7.1 Look at stset's output 73

7.2 List some of your data 76

7.3 Use stdescribe 77

7.4 Use stvary 78

7.5 Perhaps use stfill 80

7.6 Example: Hip fracture data 82

8 Nonparametric analysis 91

8.1 Inadequacies of standard univariate methods 91

8.2 The Kaplan-Meier estimator 93

8.2.1 Calculation 93

8.2.2 Censoring 96

8.2.3 Left truncation (delayed entry) 97

8.2.4 Interval truncation (gaps) 99

8.2.5 Relationship to the empirical distribution function 99

8.2.6 Other uses of sts list 101

8.2.7 Graphing the Kaplan-Meier estimate 102

8.3 The Nelson-Aalen estimator 107

8.4 Estimating the hazard function 113

8.5 Estimating mean and median survival times 117

8.6 Tests of hypothesis 122

8.6.1 The log-rank test 123

8.6.2 The Wilcoxon test 125

8.6.3 Other tests 125

8.6.4 Stratified tests 126

9 The Cox proportional hazards model 129

9.1 Using stcox 130

9.1.1 The Cox model has no intercept 131

9.1.2 Interpeting coefficients 131

9.1.3 The effect of units on coefficients 133

9.1.4 Estimating the baseline cumulative hazard and survivor functions 135

9.1.5 Estimating the baseline hazard function 139

9.1.6 The effect of units on the baseline functions 143

9.2 Likelihood calculations 145

9.2.1 No tied failures 145

9.2.2 Tied failures 148

The marginal calculation 148

The partial calculation 149

The Breslow approximation 150

The Efron approximation 151

9.3 Stratified analysis 152

9.3.1 Obtaining coefficient estimates 152

9.3.2 Obtaining estimates of baseline functions 155

9.4 Cox models with shared frailty 156

9.4.1 Parameter estimation 157

9.4.2 Obtaining estimates of baseline functions 161

9.5 Cox models with survey data 164

9.5.1 Declaring survey characteristics 165

9.5.2 Fitting a Cox model with survey data 166

9.5.3 Some caveats of analyzing survival data from complex survey designs 168

10 Model building using stcox 171

10.1 Indicator variables 171

10.2 Categorical variables 172

10.3 Continuous variables 174

10.3.1 Fractional polynomials 176

10.4 Interactions 180

10.5 Time-varying variables 183

10.5.1 Using stcox, tvc() texp() 185

10.5.2 Using stsplit 187

10.6 Modeling group effects: fixed-effects, random-effects, stratification, and clustering 191

11 The Cox model: Diagnostics 197

11.1 Testing the proportional-hazards assumption 197

11.1.1 Tests based on reestimation 197

11.1.2 Test based on Schoenfeld residuals 200

11.1.3 Graphical methods 203

11.2 Residuals 206

Reye's syndrome data 207

11.2.1 Determining functional form 208

11.2.2 Goodness of fit 213

11.2.3 Outliers and influential points 217

12 Parametric models 221

12.1 Motivation 221

12.2 Classes of parametric models 224

12.2.1 Parametric proportional hazards models 225

12.2.2 Accelerated failure-time models 231

12.2.3 Comparing the two parameterizations 233

13 A survey of parametric regression models in Stata 237

13.1 The exponential model 239

13.1.1 Exponential regression in the PH metric 239

13.1.2 Exponential regression in the AFT metric 246

13.2 Weibull regression 248

13.2.1 Weibull regression in the PH metric 248

Fitting null models 253

13.2.2 Weibull regression in the AFT metric 257

13.3 Gompertz regression (PH metric) 258

13.4 Lognormal regression (AFT metric) 261

13.5 Loglogistic regression (AFT metric) 265

13.6 Generalized gamma regression (AFT metric) 268

13.7 Choosing among parametric models 270

13.7.1 Nested models 270

13.7.2 Nonnested models 273

14 Postestimation commands for parametric models 275

14.1 Use of predict after streg 275

14.1.1 Predicting the time of failure 277

14.1.2 Predicting the hazard and related functions 283

14.1.3 Calculating residuals 286

14.2 Using stcurve 288

15 Generalizing the parametric regression model 293

15.1 Using the ancillary() option 293

15.2 Stratified models 299

15.3 Frailty models 302

15.3.1 Unshared frailty models 303

15.3.2 Example: Kidney data 304

15.3.3 Testing for heterogeneity 309

15.3.4 Shared frailty models 316

16 Power and sample-size determination for survival analysis 325

16.1 Estimating sample size 327

16.1.1 Multiple-myeloma data 328

16.1.2 Comparing two survivor functions nonparametrically 329

16.1.3 Comparing two exponential survivor functions 333

16.1.4 Cox regression models 337

16.2 Accounting for withdrawal and accrual of subjects 340

16.2.1 The effect of withdrawal or loss to follow-up 340

16.2.2 The effect of accrual 341

16.3 Estimating power and effect size 351

16.4 Tabulating or graphing results 353.

Notes:

Includes bibliographical references (pages [357]-361) and indexes.

ISBN:

9781597180412

1597180416

OCLC:

221164304