Upcoming SlideShare
×

# Handout week12tutorial1 survival.8622bf57af99

173 views

Published on

0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total views
173
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
4
0
Likes
0
Embeds 0
No embeds

No notes for slide

### Handout week12tutorial1 survival.8622bf57af99

1. 1. Tutorial: Survival Analysis in StataIn this tutorial, we use data from the Digitalis Investigation Group (DIG). Recall that the DIG trialwas a was a randomized, double-blind, multicenter trial designed to examine the safety andefficacy of Digoxin in treating patients with congestive heart failure. In this trial, patients wererandomized to either Digoxin or placebo. The log-rank test was used to compare overallmortality between the two groups.To begin, open the dig.dta data set. Before we can do any analyses, we must first tell Stata thatwe are working with survival data (analogous to how we had to svyset our data and tell Statathat we were working with survey data). You can do this using the stset command. Thecommand for this dataset is stset deathday, failure(death==1). This command tells Stata thatour time-to-death variable is deathday; and a value of 1 for the death variable means thatperson died while any other value (in this case 0) means that person was censored. For survivaldata, we need at least two variables: 1) a variable for the time to the event and 2) a variable toindicate if the observation is censored or not. 1. Graph the Kaplan-Meier estimates of the survival curves for each treatment group. .sts graph, by(trtmt) 1
2. 2. Kaplan-Meier survival estimates 1.00 0.75 0.50 0.25 0.00 0 500 1000 1500 2000 analysis time trtmt = 0 trtmt = 1 Note: you can also list the values of the survival function using the sts list, by(trtmt) command.2. In the New England of Journal paper (see handout NEJM_DIG), the authors plotted 1 – S(t) in Figure 1. Graph 1 – S(t) for each treatment group. sts graph, failure by(trtmt) 2
3. 3. Kaplan-Meier failure estimates 1.00 0.75 0.50 0.25 0.00 0 500 1000 1500 2000 analysis time trtmt = 0 trtmt = 13. Conduct a log-rank test at the 0.05 level of significance to test the hypothesis that the survival distribution is the same in the two treatment groups. Use the following command: sts test trtmt, logrank a. What are your null and alternative hypotheses? The null hypothesis is that the two groups have same distribution of survival times. The alternative is that they do not. b. What is the value of your test statistic? 0.00 c. What distribution does your test statistic have under the null hypothesis? Chi-squared distribution with 1 degree of freedom d. What is your p-value? 0.9616. Note: using all 6800 observation yields a p-value of 0.8013. This is the p- value the authors reported in Figure 1. 3
4. 4. e. What is your conclusion? Since our p-value is greater than 0.05, we fail to reject the null hypothesis. Thus, we conclude that we do not have evidence that the distribution of survival times is different between the Digoxin group and the placebo group. 4