NC631

Introduction to Survival Analysis Using Stata
Learn how to effectively analyse survival data using Stata. We cover censoring, truncation, hazard rates, and survival functions. Topics include data preparation, descriptive statistics, life tables, Kaplan–Meier curves, and semiparametric (Cox) regression and parametric regression. Discover how to set the survivaltime characteristics of your dataset just once then apply any of Stata's many estimators and statistics to that data.
Written for everyone who uses Stata, whether health researchers or social scientists. We provide lesson material, detailed answers to the questions posted at the end of each lesson, and access to a discussion board on which you can post questions for other students and the course leader to answer.
Next class:
16 Jan 2020
to
5 Mar 2020
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Lesson 1: Introduction to survival analysis

Introduction

The problem of survival analysis

The need for specific distributions

Answering specific kinds of questions

Censoring

Rightcensoring (withdrawal from study)

Leftcensoring


Truncation

Lefttruncation (delayed entry)

Righttruncation


Gaps


Survival analysis

The survivor and hazard functions

Hazard models

Parametric models

Semiparametric models

Nonparametric estimators


Analysis time (time at risk)


Summary
Lesson 2: Setting and summarizing survival data

Introduction

The problem of survival analysis

The need for specific distributions

Answering specific kinds of questions

Censoring

Rightcensoring (withdrawal from study)

Leftcensoring


Truncation

Lefttruncation (delayed entry)

Righttruncation


Gaps


Survival analysis

The survivor and hazard functions

Hazard models

Parametric models

Semiparametric models

Nonparametric estimators


Analysis time (time at risk)


Summary
Lesson 3: Setting and summarizing survival data

Nonparametric estimation

The Kaplan–Meier productlimit estimator of the survivor curve

Calculation of the Kaplan–Meier survivor curve

Censored observations

Delayed entry

Gaps

Properties of the Kaplan–Meier estimator

The sts graph command

The sts list command

The stsum command


The Nelson–Aalen estimator of the cumulative hazard

Alternative estimators of the survivor and cumulative hazard functions

Comparing survival experience

The logrank test

The Wilcoxon test

The Tarone–Ware test

The Peto–Peto–Prentice test

The Fleming–Harrington test

Test for trend across ordered groups

The Cox test

Lesson 5: Regression models — Parametric survival models
Lesson 4: Regression models — Cox proportional hazards

Introduction

The Cox model has no intercept

Interpreting coefficients

The effect of units on coefficients

The baseline hazard and related functions

The effect of units on the baseline functions

Summary of stcox command


The calculation of results

No tied failures

Tied failures

The marginal calculation

The partial calculation

The Breslow approximation

The Efron approximation

Summary



Stratified analysis

Obtaining coefficient estimates

Obtaining the baseline functions


Modeling

Indicator variables

Categorical variables

Continuous variables

Interactions

Timevarying variables

Using stcox with option tvc()

Using stsplit


Testing the proportionalhazards assumption

Tests based on reestimation

Test based on Schoenfeld residuals

Graphical methods


Residuals

Determining functional form

Assessing goodness of fit

Finding outliers and influential points


Course prerequisites

Stata 15 installed and working

Course content of NetCourse 101 or equivalent knowledge

Internet web browser, installed and working
(course is platform independent)

Introduction

Classes of parametric models

Parametric proportionalhazards models

Accelerated failuretime models


Maximum likelihood estimation for parametric models

A survey of parametric regression models in Stata

Exponential regression

Exponential regression in the PH formulation

Exponential regression in the AFT formulation


Weibull regression

Weibull regression in the PH formulation

Weibull regression in the AFT formulation


Gompertz regression (PH formulation)

Lognormal regression (AFT formulation)

Loglogistic regression (AFT formulation)

Generalized loggamma regression (AFT formulation)


Choosing among parametric models

Nested models

Nonnested models


Stratified models

Use of predict after streg

Predicting time of failure

Predicting the hazard and related functions

Calculating residuals


Use of stcurve after streg