Table 3.1 on page 78 using the actg320 dataset.
use https://stats.idre.ucla.edu/stat/examples/asa2/actg320, clear
stset time, fail(censor)
stcox tx, nohr
failure _d: censor
analysis time _t: time
Iteration 0: log likelihood = -658.46549
Iteration 1: log likelihood = -653.12286
Iteration 2: log likelihood = -653.11789
Iteration 3: log likelihood = -653.11789
Refining estimates:
Iteration 0: log likelihood = -653.11789
Cox regression -- Breslow method for ties
No. of subjects = 1151 Number of obs = 1151
No. of failures = 96
Time at risk = 264941
LR chi2(1) = 10.70
Log likelihood = -653.11789 Prob > chi2 = 0.0011
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tx | -.6843186 .2149187 -3.18 0.001 -1.105551 -.2630857
------------------------------------------------------------------------------
Table 3.2 on page 83 continuing to use the actg320 dataset.
stcox tx age sex cd4 priorzdv, nohr
failure _d: censor
analysis time _t: time
Iteration 0: log likelihood = -658.46549
Iteration 1: log likelihood = -622.46228
Iteration 2: log likelihood = -618.94567
Iteration 3: log likelihood = -618.82581
Iteration 4: log likelihood = -618.8256
Refining estimates:
Iteration 0: log likelihood = -618.8256
Cox regression -- Breslow method for ties
No. of subjects = 1151 Number of obs = 1151
No. of failures = 96
Time at risk = 264941
LR chi2(5) = 79.28
Log likelihood = -618.8256 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tx | -.6589906 .2152889 -3.06 0.002 -1.080949 -.2370322
age | .0283562 .0112658 2.52 0.012 .0062756 .0504368
sex | .0972738 .2841181 0.34 0.732 -.4595874 .654135
cd4 | -.0165767 .0025453 -6.51 0.000 -.0215654 -.0115879
priorzdv | -.0002931 .0036913 -0.08 0.937 -.007528 .0069417
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Table 3.3 on page 84 continuing to use the actg320 dataset.
stcox tx age cd4, nohr
failure _d: censor
analysis time _t: time
Iteration 0: log likelihood = -658.46549
Iteration 1: log likelihood = -622.50294
Iteration 2: log likelihood = -619.00548
Iteration 3: log likelihood = -618.8856
Iteration 4: log likelihood = -618.88539
Refining estimates:
Iteration 0: log likelihood = -618.88539
Cox regression -- Breslow method for ties
No. of subjects = 1151 Number of obs = 1151
No. of failures = 96
Time at risk = 264941
LR chi2(3) = 79.16
Log likelihood = -618.88539 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tx | -.6587076 .2150446 -3.06 0.002 -1.080187 -.237228
age | .0277729 .0111474 2.49 0.013 .0059243 .0496215
cd4 | -.0165606 .0025391 -6.52 0.000 -.0215372 -.011584
------------------------------------------------------------------------------
Table 3.4 on page 87 using the whas100 dataset. We convert time to quarterly to increase the number of ties.
use https://stats.idre.ucla.edu/stat/examples/asa2/whas100, clear
generate time=foltime/30.44 /* divide by days per month */
replace time=round(time/3) /* divide by months per quarter */
replace time=.5 if time==0 /* event can't occur at time zero */
stset time, fail(folstatus)
/* exact ties */
stcox bmi gender, nohr exactp
failure _d: folstatus
analysis time _t: time
Iteration 0: log likelihood = -183.56906
Iteration 1: log likelihood = -177.82554
Iteration 2: log likelihood = -177.79731
Iteration 3: log likelihood = -177.79731
Refining estimates:
Iteration 0: log likelihood = -177.79731
Cox regression -- exact partial likelihood
No. of subjects = 100 Number of obs = 100
No. of failures = 51
Time at risk = 1648
LR chi2(2) = 11.54
Log likelihood = -177.79731 Prob > chi2 = 0.0031
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
bmi | -.0921293 .0337883 -2.73 0.006 -.1583532 -.0259055
gender | .5391896 .2875519 1.88 0.061 -.0244017 1.102781
------------------------------------------------------------------------------
/* Breslow ties */
stcox bmi gender, nohr
failure _d: folstatus
analysis time _t: time
Iteration 0: log likelihood = -210.71578
Iteration 1: log likelihood = -205.1773
Iteration 2: log likelihood = -205.14533
Iteration 3: log likelihood = -205.14533
Refining estimates:
Iteration 0: log likelihood = -205.14533
Cox regression -- Breslow method for ties
No. of subjects = 100 Number of obs = 100
No. of failures = 51
Time at risk = 1648
LR chi2(2) = 11.14
Log likelihood = -205.14533 Prob > chi2 = 0.0038
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
bmi | -.0885065 .0329861 -2.68 0.007 -.1531582 -.0238549
gender | .5181654 .2830296 1.83 0.067 -.0365625 1.072893
------------------------------------------------------------------------------
/* Efron ties */
stcox bmi gender, nohr efron
failure _d: folstatus
analysis time _t: time
Iteration 0: log likelihood = -210.06393
Iteration 1: log likelihood = -204.20163
Iteration 2: log likelihood = -204.16685
Iteration 3: log likelihood = -204.16685
Refining estimates:
Iteration 0: log likelihood = -204.16685
Cox regression -- Efron method for ties
No. of subjects = 100 Number of obs = 100
No. of failures = 51
Time at risk = 1648
LR chi2(2) = 11.79
Log likelihood = -204.16685 Prob > chi2 = 0.0027
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
bmi | -.0925082 .0334316 -2.77 0.006 -.1580329 -.0269835
gender | .5332732 .282779 1.89 0.059 -.0209634 1.08751
------------------------------------------------------------------------------