Developing the statistical concepts unique to survival data, An Introduction to Survival Analysis Using Stata, Revised Edition includes statistical theory, step-by-step procedures for analyzing survival data, a detailed usage guide for Stata's most widely used st commands, and pointers for using Stata to analyze survival data and present the results. The first several chapters cover basic theoretical concepts as well as censoring and truncation. The next few chapters describe the formatting, manipulation, stsetting, and error-checking involved in preparing survival data for analysis using Stata's st analysis commands. The book also discusses Cox regression and includes various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, and modeling diagnostics. The final chapters cover parametric models, which are fitted using Stata's streg command.

Contenuti

Preface to the Revised Edition
Preface
Notation and Typography
THE PROBLEM OF SURVIVAL ANALYSIS
Parametric modeling
Semiparametric modeling
Nonparametric analysis
Linking the three approaches
DESCRIBING THE DISTRIBUTION OF FAILURE TIMES
The survivor and hazard functions
The quantile function
Interpreting the hazard and cumulative hazard
Means and medians
HAZARD MODELS
Parametric models
Semiparametric models
Analysis time (time at risk)
CENSORING AND TRUNCATION
Censoring
Truncation
RECORDING SURVIVAL DATA
The desired format
Other formats
Example
USING stset
A short lesson on dates
The purpose of the stset command
The syntax of the stset command
AFTER stset
Look at stset's output
List some of your data
Use stdes
Use stvary
Perhaps use stfill
Example: Hip fracture data
NONPARAMETRIC ANALYSIS
Inadequacies of standard univariate methods
The Kaplan-Meier estimator
The Nelson-Aalen estimator
Estimating the hazard function
Tests of hypothesis
THE COX PROPORTIONAL HAZARDS MODEL
Using stcox
Likelihood calculations
Stratified analysis
Cox models with shared frailty
MODEL BUILDING USING stcox
Indicator variables
Categorical variables
Continuous variables
Interactions
Time-varying variables
THE COX MODEL: DIAGNOSTICS
Testing the proportional hazards assumption
Residuals
PARAMETRIC MODELS
Motivation
Classes of parametric models
A SURVEY OF PARAMETRIC REGRESSION MODELS IN STATA
The exponential model
Weibull regression
Gompertz regression (PH metric)
Log-normal regression (AFT metric)
Log-logistic regression (AFT metric)
Generalized gamma regression (AFT metric)
Choosing among parametric models
POST-ESTIMATION COMMANDS FOR PARAMETRIC MODELS
Use of predict after streg
Using stcurve
GENERALIZING THE PARAMETRIC REGRESSION MODEL
Using the ancillary() option
Stratified models
Frailty models
References
Author Index
Subject Index

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