NetCourseNow 631: Introduction to survival analysis using Stata

$495.00Self-paced

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Learn how to effectively analyze 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 survival-time characteristics of your dataset just once and then use many of Stata's survival-time estimators and summary statistics commands with those data.

Written for everyone who uses Stata, whether health researchers or social scientists.

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Discounts available for enrollments of two or more participants.

Prerequisites

Stata 18 or Stata 17, installed and working
Course content of NetCourse 101 or equivalent knowledge
Internet web browser, installed and working
(course is platform independent)

Course content

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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

Right-censoring (withdrawal from study)
Left-censoring

Truncation

Left-truncation (delayed entry)
Right-truncation

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

The purpose of the stset command
- The desired format—Introduction to stset
- (st) Setting your data
- The syntax of the stset command
  - Specifying analysis time
  - Specifying what constitutes failure
  - Specifying when subjects exit from the analysis
  - Specifying when subjects enter the analysis
  - Specifying the subject ID variable
  - Handling gaps
After (st) setting your data
- Look at stset's output
- Use stdescribe
- Use stvary
- Perhaps use stfill
Example: Hip fracture data
Appendices
- Dates
- Other formats
- Convenience options

Lesson 3: Nonparametric analysis

Nonparametric estimation
The Kaplan–Meier product-limit 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 log-rank 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 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
Time-varying variables
- Using stcox with option tvc()
- Using stsplit
Testing the proportional-hazards 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

Lesson 5: Regression models — Parametric survival models

Introduction
Classes of parametric models
- Parametric proportional-hazards models
- Accelerated failure-time 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 log-gamma 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

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