**An Introduction to Survival Analysis Using Stata, Revised Third Edition**

77,000원

Authors: Mario Cleves, William W. Gould, and Yulia V. Marchenko Publisher: Stata Press Copyright: 2016 ISBN-13: 978-1-59718-174-7 Pages: 428; paperback

*An Introduction to Survival Analysis Using Stata, Revised Third Edition* is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines.

The revised third edition has been updated for Stata 14, and it includes a new section on predictive margins and marginal effects, which demonstrates how to obtain and visualize marginal predictions and marginal effects using the margins and marginsplot commands after survival regression models.

Survival analysis is a field of its own that requires specialized data management and analysis procedures. To meet this requirement, Stata provides the st family of commands for organizing and summarizing survival data.

This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth usage guide for Stata's most widely used stcommands, and a collection of tips for using Stata to analyze survival data and to present the results. This book develops from first principles the statistical concepts unique to survival data and assumes only a knowledge of basic probability and statistics and a working knowledge of Stata.

The first three chapters of the text cover basic theoretical concepts: hazard functions, cumulative hazard functions, and their interpretations; survivor functions; hazard models; and a comparison of nonparametric, semiparametric, and parametric methodologies. Chapter 4 deals with censoring and truncation. The next three chapters cover the formatting, manipulation, stsetting, and error checking involved in preparing survival data for analysis using Stata's st analysis commands. Chapter 8 covers nonparametric methods, including the Kaplan–Meier and Nelson–Aalen estimators and the various nonparametric tests for the equality of survival experience.

Chapters 9–11 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, model diagnostics, and regression with survey data. The next four chapters cover parametric models, which are fit using Stata's streg command. These chapters include detailed derivations of all six parametric models currently supported in Stata and methods for determining which model is appropriate, as well as information on stratification, obtaining predictions, and advanced topics such as frailty models. Chapter 16 is devoted to power and sample-size calculations for survival studies. The final chapter covers survival analysis in the presence of competing risks.

Mario Cleves is Professor and the Biostatistics Section Chief in the Department of Pediatrics at the University of Arkansas for Medical Sciences. William Gould is President and Head of Development at StataCorp. Yulia V. Marchenko is Executive Director of Statistics at StataCorp.

1.2 Semiparametric modeling

1.3 Nonparametric analysis

1.4 Linking the three approaches

2.2 The quantile function

2.3 Interpreting the cumulative hazard and hazard rate

2.3.2 Interpreting the hazard rate

3.2 Semiparametric models

3.3 Analysis time (time at risk)

4.1.2 Interval-censoring

4.1.3 Left-censoring

4.2.2 Right-truncation

4.2.3 Gaps

5.2 Other formats

5.3 Example: Wide-form snapshot data

6.2 Purposes of the stset command

6.3 Syntax of the stset command

6.3.2 Variables defined by stset

6.3.3 Specifying what constitutes failure

6.3.4 Specifying when subjects exit from the analysis

6.3.5 Specifying when subjects enter the analysis

6.3.6 Specifying the subject-ID variable

6.3.7 Specifying the begin-of-span variable

6.3.8 Convenience options

7.2 List some of your data

7.3 Use stdescribe

7.4 Use stvary

7.5 Perhaps use stfill

7.6 Example: Hip-fracture data

8.2 The Kaplan–Meier estimator

8.2.2 Censoring

8.2.3 Left-truncation (delayed entry)

8.2.4 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

8.4 Estimating the hazard function

8.5 Estimating mean and median survival times

8.6 Tests of hypothesis

8.6.2 The Wilcoxon test

8.6.3 Other tests

8.6.4 Stratified tests

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

9.2.2 Tied failures

The partial calculation

The Breslow approximation

The Efron approximation

9.3.2 Obtaining estimates of baseline functions

9.4.2 Obtaining estimates of baseline functions

9.5.2 Fitting a Cox model with survey data

9.5.3 Some caveats of analyzing survival data from complex survey designs

9.6.2 Multiple-imputation inference

10.2 Categorical variables

10.3 Continuous variables

10.5 Time-varying variables

10.5.2 Using stsplit

11.1.2 Test based on Schoenfeld residuals

11.1.3 Graphical methods

11.2.1 Determining functional form

11.2.2 Goodness of fit

11.2.3 Outliers and influential points

12.2 Classes of parametric models

12.2.2 Accelerated failure-time models

12.2.3 Comparing the two parameterizations

13.1.2 Exponential regression in the AFT 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

13.7.2 Nonnested models

14.1.2 Predicting the hazard and related functions

14.1.3 Calculating residuals

14.3 Predictive margins and marginal effects

Marginal survival probabilities

Multiple-record data

15.0.4 Stratified models

15.1.2 Example: Kidney data

15.1.3 Testing for heterogeneity

15.1.4 Shared-frailty models

16.1.2 Comparing two survivor functions nonparametrically

16.1.3 Comparing two exponential survivor functions

16.1.4 Cox regression models

16.2.2 The effect of accrual

16.2.3 Examples

16.4 Tabulating or graphing results

17.2 Cumulative incidence functions

17.3 Nonparametric analysis

17.3.2 Cause-specific hazards

17.3.3 Cumulative incidence functions

Using stcox