An Introduction to Survival Analysis Using Stata 3rd Edition by Mario Cleves, William Gould, Roberto Gutierrez, Yulia Marchenko ISBN 978-1597180740 1597180742

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ISBN 10: 1597180742

ISBN 13:  978-1597180740

Author: Mario Cleves, William Gould, Roberto Gutierrez, Yulia Marchenko

An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject.

This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata’s new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression.

After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata’s survival analysis routines.

Table of contents: 

1. Notation and Typography

2. The Problem of Survival Analysis
2.1 Parametric modeling
2.2 Semiparametric modeling
2.3 Nonparametric analysis
2.4 Linking the three approaches

3. Describing the Distribution of Failure Times
3.1 The survivor and hazard functions
3.2 The quantile function
3.3 Interpreting the cumulative hazard and hazard rate
 3.3.1 Interpreting the cumulative hazard
 3.3.2 Interpreting the hazard rate
3.4 Means and medians

4. Hazard Models
4.1 Parametric models
4.2 Semiparametric models
4.3 Analysis time (time at risk)

5. Censoring and Truncation
5.1 Censoring
 5.1.1 Right-censoring
 5.1.2 Interval-censoring
 5.1.3 Left-censoring
5.2 Truncation
 5.2.1 Left-truncation (delayed entry)
 5.2.2 Interval-truncation (gaps)
 5.2.3 Right-truncation

6. Recording Survival Data
6.1 The desired format
6.2 Other formats
6.3 Example: Wide-form snapshot data

7. Using stset
7.1 A short lesson on dates
7.2 Purposes of the stset command
7.3 Syntax of the stset command
 7.3.1 Specifying analysis time
 7.3.2 Variables defined by stset
 7.3.3 Specifying what constitutes failure
 7.3.4 Specifying when subjects exit from the analysis
 7.3.5 Specifying when subjects enter the analysis
 7.3.6 Specifying the subject-ID variable
 7.3.7 Specifying the begin-of-span variable
 7.3.8 Convenience options
7.4 After stset
 7.4.1 Look at stset’s output
 7.4.2 List some of your data
 7.4.3 Use stdescribe
 7.4.4 Use stvary
 7.4.5 Perhaps use stfill
 7.4.6 Example: Hip fracture data

8. Nonparametric Analysis
8.1 Inadequacies of standard univariate methods
8.2 The Kaplan-Meier estimator
 8.2.1 Calculation
 8.2.2 Censoring
 8.2.3 Left-truncation (delayed entry)
 8.2.4 Interval-truncation (gaps)
 8.2.5 Relationship to the empirical distribution function
 8.2.6 Other uses of sts list
 8.2.7 Graphing the Kaplan-Meier estimate
8.3 The Nelson-Aalen estimator
8.4 Estimating the hazard function
8.5 Estimating mean and median survival times
8.6 Tests of hypothesis
 8.6.1 The log-rank test
 8.6.2 The Wilcoxon test
 8.6.3 Other tests
 8.6.4 Stratified tests

9. The Cox Proportional Hazards Model
9.1 Using stcox
 9.1.1 The Cox model has no intercept
 9.1.2 Interpreting coefficients
 9.1.3 The effect of units on coefficients
 9.1.4 Estimating the baseline cumulative hazard and survivor functions
 9.1.5 Estimating the baseline hazard function
 9.1.6 The effect of units on the baseline functions
9.2 Likelihood calculations
 9.2.1 No tied failures
 9.2.2 Tied failures
  - The marginal calculation
  - The partial calculation
  - The Breslow approximation
  - The Efron approximation
 9.2.3 Summary
9.3 Stratified analysis
 9.3.1 Obtaining coefficient estimates
 9.3.2 Obtaining estimates of baseline functions
9.4 Cox models with shared frailty
 9.4.1 Parameter estimation
 9.4.2 Obtaining estimates of baseline functions
9.5 Cox models with survey data
 9.5.1 Declaring survey characteristics
 9.5.2 Fitting a Cox model with survey data
 9.5.3 Some caveats of analyzing survival data from complex survey designs
9.6 Cox model with missing data-multiple imputation
 9.6.1 Imputing missing values
 9.6.2 Multiple-imputation inference

10. Model Building Using stcox
10.1 Indicator variables
10.2 Categorical variables
10.3 Continuous variables
 10.3.1 Fractional polynomials
10.4 Interactions
10.5 Time-varying variables
 10.5.1 Using stcox, tve() texp()
 10.5.2 Using stsplit
10.6 Modeling group effects: fixed-effects, random-effects, stratification, and clustering

11. The Cox Model: Diagnostics
11.1 Testing the proportional-hazards assumption
 11.1.1 Tests based on reestimation
 11.1.2 Test based on Schoenfeld residuals
 11.1.3 Graphical methods
11.2 Residuals and diagnostic measures
 11.2.1 Determining functional form
 11.2.2 Goodness of fit
 11.2.3 Outliers and influential points
 - Reye’s syndrome data

12. Parametric Models
12.1 Motivation
12.2 Classes of parametric models
 12.2.1 Parametric proportional hazards models
 12.2.2 Accelerated failure-time models
 12.2.3 Comparing the two parameterizations

13. A Survey of Parametric Regression Models in Stata
13.1 The exponential model
 13.1.1 Exponential regression in the PH metric
 13.1.2 Exponential regression in the AFT metric
13.2 Weibull regression
 13.2.1 Weibull regression in the PH metric Fitting null models
 13.2.2 Weibull regression in the AFT metric
13.3 Gompertz regression (PH metric)
13.4 Lognormal regression (AFT metric)
13.5 Loglogistic regression (AFT metric)
13.6 Generalized gamma regression (AFT metric)
13.7 Choosing among parametric models
 13.7.1 Nested models
 13.7.2 Nonnested models

14. Postestimation Commands for Parametric Models
14.1 Use of predict after streg
 14.1.1 Predicting the time of failure
 14.1.2 Predicting the hazard and related functions
 14.1.3 Calculating residuals
14.2 Using stcurve

15. Generalizing the Parametric Regression Model
15.1 Using the ancillary() option
15.2 Stratified models
15.3 Frailty models
 15.3.1 Unshared frailty models
 15.3.2 Example: Kidney data
 15.3.3 Testing for heterogeneity
 15.3.4 Shared frailty models

16. Power and Sample-Size Determination for Survival Analysis
16.1 Estimating sample size
 16.1.1 Multiple-myeloma data
 16.1.2 Comparing two survivor functions nonparametrically
 16.1.3 Comparing two exponential survivor functions
 16.1.4 Cox regression models
16.2 Accounting for withdrawal and accrual of subjects
 16.2.1 The effect of withdrawal or loss to follow-up
 16.2.2 The effect of accrual
 16.2.3 Examples
16.3 Estimating power and effect size
16.4 Tabulating or graphing results

17. Competing Risks
17.1 Cause-specific hazards
17.2 Cumulative incidence functions
17.3 Nonparametric analysis
 17.3.1 Breast cancer data
 17.3.2 Cause-specific hazards
 17.3.3 Cumulative incidence functions
17.4 Semiparametric analysis
 17.4.1 Cause-specific hazards
  - Simultaneous regressions for cause-specific hazards
 17.4.2 Cumulative incidence functions
  - Using stcrreg
  - Using stcox
17.5 Parametric analysis

18. References

19. Author Index

20. Subject Index

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Tags: Mario Cleves, William Gould, Roberto Gutierrez, Yulia Marchenko, An Introduction, Survival Analysis, Using Stata