An Introduction to Survival Analysis Using Stata, Second Edition

An Introduction to Survival Analysis Using Stata, Second 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 not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those who already have experience using Stata’s survival analysis routines. The second edition has been updated for Stata 10, containing a new chapter on power and sample-size calculations for survival studies and sections that describe how to fit regression models (stcox and streg) in the presence of complex survey data. Other enhancements include discussions about nonparametric estimation of mean/median survival, survival graphs with embedded at-risk tables, better hazard graphs through the use of boundary kernels, and concordance measures for assessing the predictive accuracy of the Cox model, as well as an expanded discussion of model building strategies including the use of fractional polynomials. Survival analysis is a field of its own requiring specialized data management and analysis procedures. Toward this end, Stata provides the st family of commands for organizing and summarizing survival data. The authors of this text are also the authors of Stata’s st commands. This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth usage guide for Stata’s most widely used st commands, and a collection of tips for using Stata to analyze survival data and 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. hapters 9-11 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, and model diagnostics. 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 obtaining predictions, stratification, and advanced topics such as frailty models. The final chapter is devoted to power and sample-size calculations for survival studies.