UCLA Academic Technology Services HomeServicesClassesContactJobs
Search

Stata Textbook Examples
Computer-Aided Multivariate Analysis by Afifi and Clark
Chapter 13: Regression Analysis using Survival Data

Table 13.2, page 318.
use http://www.ats.ucla.edu/stat/stata/examples/cama3/surv, clear

tab death staget, col

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 


tab death perfbl, col

                 | 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 


tab death treat, col

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 


tab death poinf, col

                 |    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 13.3, page 318.
NOTE: The intercept given in the output is slightly different from that shown in the text.  We suspect that the difference is caused by a change in the algorithms used by the different statistical packages.
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 13.4, page 320.
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
------------------------------------------------------------------------------
Figure 13.8, page 321.
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)

How to cite this page

Report an error on this page

UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services


The content of this web site should not be construed as an endorsement of any particular web site, book, or software product by the University of California.