ISBN-10:
1597180416
ISBN-13:
9781597180412
Pub. Date:
05/16/2008
Publisher:
Taylor & Francis
An Introduction to Survival Analysis Using Stata, 2nd Edition / Edition 2

An Introduction to Survival Analysis Using Stata, 2nd Edition / Edition 2

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Overview

An Introduction to Survival Analysis Using Stata, 2nd Edition / Edition 2

Written by the developers of Stata’s widely used survival analysis suite, this book provides the foundation to understand various approaches for analyzing time-to-event data. Taking a practical approach to the subject, the authors discuss how survival analysis estimators work and what information they exploit. They also present the syntax, features, and underpinnings of Stata’s survival analysis routines.

This edition highlights the new aspects of Stata 10, including its power and sample-size calculations for survival data. Other updates include 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.

Product Details

ISBN-13: 9781597180412
Publisher: Taylor & Francis
Publication date: 05/16/2008
Edition description: Edition
Pages: 372
Product dimensions: 7.30(w) x 9.30(h) x 1.00(d)

About the Author

Mario Cleves is a professor of pediatrics at the University of Arkansas for Medical Sciences and a senior biostatistician at the Arkansas Center for Birth Defects Research and Prevention.

William Gould is the president and head of development at StataCorp.

Roberto Gutierrez is the director of statistics at StataCorp.

Yulia Marchenko is a senior statistician at StataCorp.

All are authors of Stata statistical software, in particular, Stata’s widely used survival analysis suite.

Table of Contents

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 cumulative hazard and hazard rate
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: wide-form snapshot data
Using stset
A short lesson on dates
Purposes of the stset command
Syntax of the stset command
After stset
Look at stset’s output
List some of your data
Use stdescribe
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
Estimating mean and median survival times
Tests of hypothesis
The Cox Proportional Hazards Model
Using stcox
Likelihood calculations
Stratified analysis
Cox models with shared frailty
Cox models with survey data
Model Building Using stcox
Indicator variables
Categorical variables
Continuous variables
Interactions
Time-varying variables
Modeling group effects: fixed-effects, random-effects, stratification, and clustering
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 (PHmetric)
Lognormal regression (AFTmetric)
Loglogistic regression (AFTmetric)
Generalized gamma regression (AFTmetric)
Choosing among parametric models
Postestimation Commands for Parametric Models
Use of predict after streg
Using stcurve
Generalizing the Parametric Regression Model
Using the ancillary() option
Stratified models
Frailty models
Power and Sample-Size Determination for Survival Analysis
Estimating sample size
Accounting for withdrawal and accrual of subjects
Estimating power and effect size
Tabulating or graphing results
References
Author Index
Subject Index

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