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Stata Textbook Examples
Practical Multivariate Analysis, Fifth Edition, by Afifi, May and Clark
Chapter 13: Regression Analysis using Survival Data
Table 13.2, page 330.
clear input id years dead 1 2 1 2 6 1 3 2 0 4 3 0 5 1 1 end
ltable years dead, noadj intervals(0,1,2,6)
Beg. Std.
Interval Total Deaths Lost Survival Error [95% Conf. Int.]
-------------------------------------------------------------------------------
1 2 5 1 0 0.8000 0.1789 0.2038 0.9692
2 6 4 1 2 0.6000 0.2191 0.1257 0.8818
6 . 1 1 0 0.0000 . . .
-------------------------------------------------------------------------------
Figure 13.6, page 331.
use http://www.ats.ucla.edu/stat/stata/examples/pma5/surv, clear stset days, failure(death) sts graph, by(staget)
Table 13.3, page 336.
foreach i of varlist staget perfbl treat poinf {
tab death `i', col
}
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
status at end of | tumor size
observation time | small large | Total
-----------------+----------------------+----------
alive (censored) | 122 75 | 197
| 57.28 39.89 | 49.13
-----------------+----------------------+----------
dead | 91 113 | 204
| 42.72 60.11 | 50.87
-----------------+----------------------+----------
Total | 213 188 | 401
| 100.00 100.00 | 100.00
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
| perfomance status at
status at end of | baseline
observation time | good poor | Total
-----------------+----------------------+----------
alive (censored) | 174 22 | 196
| 51.63 35.48 | 49.12
-----------------+----------------------+----------
dead | 163 40 | 203
| 48.37 64.52 | 50.88
-----------------+----------------------+----------
Total | 337 62 | 399
| 100.00 100.00 | 100.00
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
status at end of | treatment
observation time | saline (c bcg (expe | Total
-----------------+----------------------+----------
alive (censored) | 99 98 | 197
| 50.77 47.57 | 49.13
-----------------+----------------------+----------
dead | 96 108 | 204
| 49.23 52.43 | 50.87
-----------------+----------------------+----------
Total | 195 206 | 401
| 100.00 100.00 | 100.00
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
| post-operative
status at end of | infection
observation time | no yes | Total
-----------------+----------------------+----------
alive (censored) | 191 5 | 196
| 50.26 25.00 | 49.00
-----------------+----------------------+----------
dead | 189 15 | 204
| 49.74 75.00 | 51.00
-----------------+----------------------+----------
Total | 380 20 | 400
| 100.00 100.00 | 100.00
Table 13.4, page 337.
stset days, failure(death)
streg staget perfbl poinf treat, dist(weibull) nohr time
failure _d: death
analysis time _t: days
Fitting constant-only model:
Iteration 0: log likelihood = -528.69151
Iteration 1: log likelihood = -527.0816
Iteration 2: log likelihood = -527.0782
Iteration 3: log likelihood = -527.0782
Fitting full model:
Iteration 0: log likelihood = -527.0782
Iteration 1: log likelihood = -515.54616
Iteration 2: log likelihood = -512.77675
Iteration 3: log likelihood = -512.74783
Iteration 4: log likelihood = -512.74781
Weibull regression -- accelerated failure-time form
No. of subjects = 398 Number of obs = 398
No. of failures = 203
Time at risk = 693517
LR chi2(4) = 28.66
Log likelihood = -512.74781 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
staget | -.5874447 .1573837 -3.73 0.000 -.8959111 -.2789782
perfbl | -.5986494 .2028811 -2.95 0.003 -.9962891 -.2010098
poinf | -.7124162 .3088923 -2.31 0.021 -1.317834 -.1069984
treat | -.0831065 .1545574 -0.54 0.591 -.3860334 .2198205
_cons | 8.642305 .1583925 54.56 0.000 8.331862 8.952749
-------------+----------------------------------------------------------------
/ln_p | -.0855928 .0623351 -1.37 0.170 -.2077675 .0365818
-------------+----------------------------------------------------------------
p | .9179679 .0572217 .8123959 1.037259
1/p | 1.089363 .0679056 .9640792 1.230927
------------------------------------------------------------------------------
Table 13.5, page 339.
cox days staget perfbl poinf treat, dead(death)
Iteration 0: log likelihood = -1131.8837
Iteration 1: log likelihood = -1119.5607
Iteration 2: log likelihood = -1117.9168
Iteration 3: log likelihood = -1117.8838
Iteration 4: log likelihood = -1117.8838
Refining estimates:
Iteration 0: log likelihood = -1117.8838
Cox regression -- Breslow method for ties
Entry time 0 Number of obs = 398
LR chi2(4) = 28.00
Prob > chi2 = 0.0000
Log likelihood = -1117.8838 Pseudo R2 = 0.0124
------------------------------------------------------------------------------
days |
death | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
staget | .5365369 .1421111 3.78 0.000 .2580043 .8150695
perfbl | .5308045 .1852409 2.87 0.004 .167739 .89387
poinf | .6668952 .2804013 2.38 0.017 .1173187 1.216472
treat | .0703558 .1418181 0.50 0.620 -.2076026 .3483142
------------------------------------------------------------------------------
We have skipped the examples on pages 340-344 because they use a simulated data set.
Figure 13.9, page 347.
sts gen s=s, by(staget) gen lls = ln(-ln(s)) graph twoway scatter lls days, connect(L) msymbol(none) sort(staget days) ylabel( , angle(0) nogrid)
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