Factor Variables
stpm3 fully supports factor variables
In stpm3 there is now full support for factor variables including for time-dependent effects.
This makes predictions much easier. In addition, standsurv has been updated to be compatible
with stpm3 making marginal predictions also easier.
We first load the Rotterdam 2 breast cancer data and then use stset to declare the survival time and event indicator.
. use https://www.pclambert.net/data/rott2b, clear
(Rotterdam breast cancer data (augmented with cause of death))
. stset os, f(osi==1) scale(12) exit(time 120)
Survival-time data settings
Failure event: osi==1
Observed time interval: (0, os]
Exit on or before: time 120
Time for analysis: time/12
--------------------------------------------------------------------------
2,982 total observations
0 exclusions
--------------------------------------------------------------------------
2,982 observations remaining, representing
1,171 failures in single-record/single-failure data
20,002.424 total analysis time at risk and under observation
At risk from t = 0
Earliest observed entry t = 0
Last observed exit t = 10
The scale(12) option converts the times recorded in months to years.
To fit an stpm3 model with a binary covariate we can use,
. stpm3 hormon, scale(lncumhazard) df(5)
Iteration 0: log likelihood = -2929.2941
Iteration 1: log likelihood = -2928.2998
Iteration 2: log likelihood = -2928.2966
Iteration 3: log likelihood = -2928.2966
Number of obs = 2,982
Wald chi2(1) = 25.19
Log likelihood = -2928.2966 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
xb |
hormon | .4321954 .0861189 5.02 0.000 .2634054 .6009854
-------------+----------------------------------------------------------------
time |
_ns1 | -23.9834 1.92113 -12.48 0.000 -27.74874 -20.21805
_ns2 | 6.695765 1.027919 6.51 0.000 4.681082 8.710449
_ns3 | -1.214676 .0497438 -24.42 0.000 -1.312172 -1.11718
_ns4 | -.8095755 .0387379 -20.90 0.000 -.8855004 -.7336505
_ns5 | -.4994385 .0418591 -11.93 0.000 -.5814808 -.4173963
_cons | -.5713643 .0332128 -17.20 0.000 -.6364603 -.5062684
------------------------------------------------------------------------------
Warning: This is a test version of stpm3
The equivalent model using factor variables is,
. stpm3 i.hormon, scale(lncumhazard) df(5)
Iteration 0: log likelihood = -2929.2941
Iteration 1: log likelihood = -2928.2998
Iteration 2: log likelihood = -2928.2966
Iteration 3: log likelihood = -2928.2966
Number of obs = 2,982
Wald chi2(1) = 25.19
Log likelihood = -2928.2966 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
xb |
hormon |
yes | .4321954 .0861189 5.02 0.000 .2634054 .6009854
-------------+----------------------------------------------------------------
time |
_ns1 | -23.9834 1.92113 -12.48 0.000 -27.74874 -20.21805
_ns2 | 6.695765 1.027919 6.51 0.000 4.681082 8.710449
_ns3 | -1.214676 .0497438 -24.42 0.000 -1.312172 -1.11718
_ns4 | -.8095755 .0387379 -20.90 0.000 -.8855004 -.7336505
_ns5 | -.4994385 .0418591 -11.93 0.000 -.5814808 -.4173963
_cons | -.5713643 .0332128 -17.20 0.000 -.6364603 -.5062684
------------------------------------------------------------------------------
Warning: This is a test version of stpm3
You can also include factor variables as a time-dependent effect.
. stpm3 i.hormon, scale(lncumhazard) df(5) ///
> tvc(i.hormon) dftvc(3)
Iteration 0: log likelihood = -2928.8269
Iteration 1: log likelihood = -2926.8607
Iteration 2: log likelihood = -2926.8409
Iteration 3: log likelihood = -2926.8409
Number of obs = 2,982
Wald chi2(1) = 20.88
Log likelihood = -2926.8409 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
------------------+----------------------------------------------------------------
xb |
hormon |
yes | .4766542 .1043107 4.57 0.000 .2722089 .6810995
------------------+----------------------------------------------------------------
time |
_ns1 | -25.11479 2.156221 -11.65 0.000 -29.3409 -20.88867
_ns2 | 7.297905 1.13577 6.43 0.000 5.071836 9.523974
_ns3 | -1.195295 .0516705 -23.13 0.000 -1.296567 -1.094023
_ns4 | -.7997134 .0402802 -19.85 0.000 -.8786612 -.7207657
_ns5 | -.4954146 .0427173 -11.60 0.000 -.5791388 -.4116903
|
hormon#c._ns_tvc1 |
yes | 4.943174 3.778547 1.31 0.191 -2.462642 12.34899
|
hormon#c._ns_tvc2 |
yes | -2.952286 1.995084 -1.48 0.139 -6.862579 .9580062
|
hormon#c._ns_tvc3 |
yes | -.0773513 .1778044 -0.44 0.664 -.4258415 .2711389
|
_cons | -.5740705 .0333738 -17.20 0.000 -.6394819 -.5086591
-----------------------------------------------------------------------------------
Warning: This is a test version of stpm3
You can incorporate interactions into both the main effect and interactions with time using tvc().
. stpm3 i.hormon##i.grade, scale(lncumhazard) df(5) ///
> tvc(i.hormon##i.grade) dftvc(3) baselevels
Iteration 0: log likelihood = -2901.321
Iteration 1: log likelihood = -2895.1769
Iteration 2: log likelihood = -2895.0066
Iteration 3: log likelihood = -2895.0059
Iteration 4: log likelihood = -2895.0059
Number of obs = 2,982
Wald chi2(3) = 60.61
Log likelihood = -2895.0059 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
------------------------+----------------------------------------------------------------
xb |
hormon |
no | 0 (base)
yes | .4135919 .2951852 1.40 0.161 -.1649605 .9921443
|
grade |
2 | 0 (base)
3 | .483797 .0805037 6.01 0.000 .3260126 .6415814
|
hormon#grade |
yes#3 | .0284316 .315761 0.09 0.928 -.5904485 .6473117
------------------------+----------------------------------------------------------------
time |
_ns1 | -27.62422 5.730659 -4.82 0.000 -38.85611 -16.39234
_ns2 | 7.583979 2.721378 2.79 0.005 2.250176 12.91778
_ns3 | -1.385614 .1121938 -12.35 0.000 -1.60551 -1.165718
_ns4 | -.8489422 .0796837 -10.65 0.000 -1.005119 -.6927651
_ns5 | -.4786468 .0735986 -6.50 0.000 -.6228974 -.3343963
|
hormon#c._ns_tvc1 |
yes | -2.771489 16.45089 -0.17 0.866 -35.01463 29.47165
|
hormon#c._ns_tvc2 |
yes | 2.425308 8.628728 0.28 0.779 -14.48669 19.3373
|
hormon#c._ns_tvc3 |
yes | -.7069334 .573263 -1.23 0.218 -1.830508 .4166415
|
grade#c._ns_tvc1 |
3 | 2.757887 5.847325 0.47 0.637 -8.702659 14.21843
|
grade#c._ns_tvc2 |
3 | -.285027 3.030253 -0.09 0.925 -6.224213 5.654159
|
grade#c._ns_tvc3 |
3 | -.0467859 .1288623 -0.36 0.717 -.2993512 .2057795
|
hormon#grade#c._ns_tvc1 |
yes#3 | 8.162556 16.89039 0.48 0.629 -24.942 41.26711
|
hormon#grade#c._ns_tvc2 |
yes#3 | -5.885373 8.860398 -0.66 0.507 -23.25143 11.48069
|
hormon#grade#c._ns_tvc3 |
yes#3 | .7162528 .6030515 1.19 0.235 -.4657065 1.898212
|
_cons | -.9320134 .0710511 -13.12 0.000 -1.071271 -.7927559
-----------------------------------------------------------------------------------------
Warning: This is a test version of stpm3
