# Survival Analysis Stata Commands

*The revised third edition has been updated for Stata 14, and it includes a new section on predictive margins and marginal effects, which demonstrates how to obtain and visualize marginal predictions and marginal effects using the margins and marginsplot commands after survival regression models. (Change ec968 to some other name of your choosing, if you prefer.). Please email your comments and suggestions. Hshaz This is a program for discrete time proportional hazards regression but, unlike pgmhaz8, hshaz assumes that the mixture distribution summarizing frailty is a discrete one, following Heckman and Singer (1984). There is material to read followed by exercises. *

Chapters 911 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, model diagnostics, and regression with survey data. This text also serves as survival a valuable reference to those readers who already have experience using Statas survival analysis routines. To check the assumption of proportionality of hazards one may introduce interactions with duration.

Chapter 4 deals with censoring and truncation. The book presents the essential models, formulas, background, and relevant references in a games compact and adequate manner, and then continues to present the relevant tools, their implementation, and explanation of outputs. Gen S1 S0exp btreated) / treated. 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. Survival analysis is a field of its own that requires specialized data management and analysis procedures.

### A brief introduction to survival analysis using Stata An Introduction to Survival Analysis Using Stata, Third

Chapter 16 is devoted to power and sample-size calculations for survival studies.

Likelihood ratio test16.4 on 1 df,.26e-05 n 42, number of events 30 exp(coef(cm) - 1 treated -0.7923965, the results show that at any given duration since remission, the risk of relapse.6 lower in the treated group.

Sts gen KM s, by(treated) / two Kaplan-Meiers.

The next four chapters cover parametric models, which are fit using Stata's streg command.

An Introduction to Survival Analysis Using Stata, Revised Third 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 are not as dexterous in using Stata. Of subjects 42 Number of obs. Interval treated.2076035.085615 -3.81.000.0925128.4658729 gehan - mutate(gehan, treated meric(group "treated cm - coxph(Surv(weeks, relapse) treated, data gehan) cm, call: coxph(formula Surv(weeks, relapse) treated, data gehan) coef exp(coef) se(coef) z p treated -1.572.208.412 -3.81.00014.

S original paper, in a twogroup analysis like this it is also possible to plot the KaplanMeier estimates.

### Introduction to the stset command - Paul Dickman

The American Statistician, November 2010, Vol. This book is also an excellent supplement for a graduate-level survival analysis course as well as a reference book for a data analyst dealing with survival data. Twoway (scatter S0 _t, c(J) ms(none) sort) / baseline (scatter S1 _t, c(J) ms(none) sort) / treated (scatter KM _t if treated, msymbol(circle_hollow) / KM treated (scatter KM _t if!treated, msymbol(X) / KM base, legend(off) title(Kaplan-Meier and Proportional Hazards Estimates).