Hello,
I am using Stata 14.1. I have a survival analysis and I am trying to use a cox proportional hazards model to estimate the time to death after transplant for rld_tx_type = 1, 2 and 3, in a model with multiple covariates. Using stcox I can obtain hazard ratios, but I would like to obtain adjusted point estimates for survival at specific time points like 365 days, 1095 days, 1825 days.
Using streg and weibull distribution parametric survival model followed by the margins command, I can obtain point estimates for survival at 365 days.
I have read numerous similar entries on this topic, that discuss the inability to estimate baseline hazards using stcox b/c cox regression doesnt estimate the baseline hazard. Is this why I am unable to obtain point estimates using stcox?
*Code
stset gtime, failure(outcome)
streg i.rld_tx_type gender_group ecmo_trr ventilator_trr i.blood_type i.year i.Ethnic bmi_analysis pTLC_ratio_copd end_match_las age_don gender_mismatch ischtime end_o2 i.lung_preference, vce(robust) dist(weibull)
streg, coeflegend
margins, expression(exp(-exp(predict(xb))*1825^exp(_b[ln_p:_cons]))) at(rld_tx_type=(1 2 3))
*Output
. stset gtime, failure(outcome)
failure event: outcome != 0 & outcome < .
obs. time interval: (0, gtime]
exit on or before: failure
------------------------------------------------------------------------------
5055 total observations
13 observations end on or before enter()
------------------------------------------------------------------------------
5042 observations remaining, representing
2212 failures in single-record/single-failure data
6507620 total analysis time at risk and under observation
at risk from t = 0
earliest observed entry t = 0
last observed exit t = 4412
.
. streg i.rld_tx_type gender_group ecmo_trr ventilator_trr i.blood_type i.year i.Ethnic bmi_analysis pTLC_ratio_copd end_match_las age_don
> gender_mismatch ischtime end_o2 i.lung_preference, vce(robust) dist(weibull)
failure _d: outcome
analysis time _t: gtime
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -5568.0139
Iteration 1: log pseudolikelihood = -5556.817
Iteration 2: log pseudolikelihood = -5556.8074
Iteration 3: log pseudolikelihood = -5556.8074
Fitting full model:
Iteration 0: log pseudolikelihood = -5556.8074
Iteration 1: log pseudolikelihood = -5507.4874
Iteration 2: log pseudolikelihood = -5506.2359
Iteration 3: log pseudolikelihood = -5506.2337
Iteration 4: log pseudolikelihood = -5506.2337
Weibull regression -- log relative-hazard form
No. of subjects = 4,866 Number of obs = 4,866
No. of failures = 2,117
Time at risk = 6193954
Wald chi2(36) = 105.86
Log pseudolikelihood = -5506.2337 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
| Robust
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
rld_tx_type |
2 | .8249158 .0641938 -2.47 0.013 .7082234 .9608354
3 | .7857402 .062952 -3.01 0.003 .6715562 .9193388
|
gender_group | 1.094763 .0534039 1.86 0.063 .9949418 1.2046
ecmo_trr | 1.587534 .9326214 0.79 0.431 .501963 5.020819
ventilator_trr | 1.325516 .1665057 2.24 0.025 1.036241 1.695545
|
blood_type |
2 | .9575172 .0441988 -0.94 0.347 .8746923 1.048185
3 | .9455973 .0703752 -0.75 0.452 .8172527 1.094098
4 | .9209833 .0991832 -0.76 0.445 .7457333 1.137418
|
year_tx |
2 | .8792394 .1061766 -1.07 0.287 .6939312 1.114032
3 | .9375805 .1130025 -0.53 0.593 .7403157 1.187409
4 | .8853886 .1082717 -1.00 0.320 .6966953 1.125188
5 | .817168 .0988432 -1.67 0.095 .6446908 1.035789
6 | .7776473 .0963679 -2.03 0.042 .6099578 .991438
7 | .8771987 .1082622 -1.06 0.288 .6887226 1.117253
8 | .7459922 .0964708 -2.27 0.023 .5789726 .961193
9 | .7436866 .1000223 -2.20 0.028 .5713568 .9679937
10 | .6911968 .1026914 -2.49 0.013 .5165809 .9248367
11 | .944747 .1380387 -0.39 0.697 .7094878 1.258016
12 | .7908836 .1392182 -1.33 0.183 .5601143 1.116731
13 | .4194482 .2500005 -1.46 0.145 .1304197 1.349004
|
Ethnic |
2 | 1.044576 .0912809 0.50 0.618 .8801515 1.239718
4 | 1.11143 .2007295 0.58 0.559 .7801019 1.583482
5 | .9110327 .2200833 -0.39 0.700 .5674197 1.462728
|
bmi_analysis | 1.010092 .0055142 1.84 0.066 .9993424 1.020958
pTLC_ratio_copd | .8998795 .0441061 -2.15 0.031 .8174554 .9906143
end_match_las | .9960659 .0054117 -0.73 0.468 .9855155 1.006729
age_don | 1.000985 .0015336 0.64 0.521 .9979834 1.003995
gender_mismatch | 1.16013 .0621598 2.77 0.006 1.044477 1.288588
ischtime | .9901301 .0149002 -0.66 0.510 .9613527 1.019769
end_o2 | 1.025314 .0115466 2.22 0.026 1.002931 1.048197
|
lung_preference |
2 | .910501 .1291987 -0.66 0.509 .6894396 1.202443
3 | .9074591 .0921592 -0.96 0.339 .7436717 1.107319
4 | .847192 .0941981 -1.49 0.136 .6812992 1.053479
5 | 1.095501 .109549 0.91 0.362 .9005204 1.332699
6 | .9505574 .1139962 -0.42 0.672 .7514455 1.202428
7 | .9216207 .1356492 -0.55 0.579 .6906654 1.229806
|
_cons | .0008421 .0002809 -21.23 0.000 .000438 .001619
----------------+----------------------------------------------------------------
/ln_p | -.0939497 .0231057 -4.07 0.000 -.1392361 -.0486633
----------------+----------------------------------------------------------------
p | .9103286 .0210338 .8700226 .9525018
1/p | 1.098504 .0253817 1.049867 1.149395
---------------------------------------------------------------------------------
.
. streg, coeflegend
Weibull regression -- log relative-hazard form
No. of subjects = 4,866 Number of obs = 4,866
No. of failures = 2,117
Time at risk = 6193954
Wald chi2(36) = 105.86
Log pseudolikelihood = -5506.2337 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
_t | Coef. Legend
----------------+----------------------------------------------------------------
rld_tx_type |
2 | -.1924739 _b[_t:2.rld_tx_type]
3 | -.2411291 _b[_t:3.rld_tx_type]
|
gender_group | .0905383 _b[_t:gender_group]
ecmo_trr | .4621821 _b[_t:ecmo_trr]
ventilator_trr | .2818021 _b[_t:ventilator_trr]
|
blood_type |
2 | -.0434116 _b[_t:2.blood_type]
3 | -.0559385 _b[_t:3.blood_type]
4 | -.0823133 _b[_t:4.blood_type]
|
year_tx |
2 | -.1286981 _b[_t:2.year_tx]
3 | -.0644526 _b[_t:3.year_tx]
4 | -.1217286 _b[_t:4.year_tx]
5 | -.2019106 _b[_t:5.year_tx]
6 | -.2514822 _b[_t:6.year_tx]
7 | -.1310217 _b[_t:7.year_tx]
8 | -.2930401 _b[_t:8.year_tx]
9 | -.2961355 _b[_t:9.year_tx]
10 | -.3693308 _b[_t:10.year_tx]
11 | -.0568382 _b[_t:11.year_tx]
12 | -.2346045 _b[_t:12.year_tx]
13 | -.8688152 _b[_t:13.year_tx]
|
Ethnic |
2 | .0436113 _b[_t:2.Ethnic]
4 | .1056479 _b[_t:4.Ethnic]
5 | -.0931765 _b[_t:5.Ethnic]
|
bmi_analysis | .0100419 _b[_t:bmi_analysis]
pTLC_ratio_copd | -.1054945 _b[_t:pTLC_ratio_copd]
end_match_las | -.0039418 _b[_t:end_match_las]
age_don | .0009842 _b[_t:age_don]
gender_mismatch | .1485317 _b[_t:gender_mismatch]
ischtime | -.0099189 _b[_t:ischtime]
end_o2 | .0249992 _b[_t:end_o2]
|
lung_preference |
2 | -.0937603 _b[_t:2.lung_preference]
3 | -.0971068 _b[_t:3.lung_preference]
4 | -.165828 _b[_t:4.lung_preference]
5 | .091212 _b[_t:5.lung_preference]
6 | -.0507067 _b[_t:6.lung_preference]
7 | -.0816215 _b[_t:7.lung_preference]
|
_cons | -7.079553 _b[_t:_cons]
----------------+----------------------------------------------------------------
/ln_p | -.0939497 _b[ln_p:_cons]
----------------+----------------------------------------------------------------
p | .9103286
1/p | 1.098504
---------------------------------------------------------------------------------
.
. margins, expression(exp(-exp(predict(xb))*1825^exp(_b[ln_p:_cons]))) at(rld_tx_type=(1 2 3))
Predictive margins Number of obs = 4,866
Model VCE : Robust
Expression : exp(-exp(predict(xb))*1825^exp(_b[ln_p:_cons]))
1._at : rld_tx_type = 1
2._at : rld_tx_type = 2
3._at : rld_tx_type = 3
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at |
1 | .4857455 .0222814 21.80 0.000 .4420748 .5294162
2 | .5503463 .0220442 24.97 0.000 .5071405 .593552
3 | .5659892 .0123369 45.88 0.000 .5418094 .590169
------------------------------------------------------------------------------
Thanks,
Luke
I am using Stata 14.1. I have a survival analysis and I am trying to use a cox proportional hazards model to estimate the time to death after transplant for rld_tx_type = 1, 2 and 3, in a model with multiple covariates. Using stcox I can obtain hazard ratios, but I would like to obtain adjusted point estimates for survival at specific time points like 365 days, 1095 days, 1825 days.
Using streg and weibull distribution parametric survival model followed by the margins command, I can obtain point estimates for survival at 365 days.
I have read numerous similar entries on this topic, that discuss the inability to estimate baseline hazards using stcox b/c cox regression doesnt estimate the baseline hazard. Is this why I am unable to obtain point estimates using stcox?
*Code
stset gtime, failure(outcome)
streg i.rld_tx_type gender_group ecmo_trr ventilator_trr i.blood_type i.year i.Ethnic bmi_analysis pTLC_ratio_copd end_match_las age_don gender_mismatch ischtime end_o2 i.lung_preference, vce(robust) dist(weibull)
streg, coeflegend
margins, expression(exp(-exp(predict(xb))*1825^exp(_b[ln_p:_cons]))) at(rld_tx_type=(1 2 3))
*Output
. stset gtime, failure(outcome)
failure event: outcome != 0 & outcome < .
obs. time interval: (0, gtime]
exit on or before: failure
------------------------------------------------------------------------------
5055 total observations
13 observations end on or before enter()
------------------------------------------------------------------------------
5042 observations remaining, representing
2212 failures in single-record/single-failure data
6507620 total analysis time at risk and under observation
at risk from t = 0
earliest observed entry t = 0
last observed exit t = 4412
.
. streg i.rld_tx_type gender_group ecmo_trr ventilator_trr i.blood_type i.year i.Ethnic bmi_analysis pTLC_ratio_copd end_match_las age_don
> gender_mismatch ischtime end_o2 i.lung_preference, vce(robust) dist(weibull)
failure _d: outcome
analysis time _t: gtime
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -5568.0139
Iteration 1: log pseudolikelihood = -5556.817
Iteration 2: log pseudolikelihood = -5556.8074
Iteration 3: log pseudolikelihood = -5556.8074
Fitting full model:
Iteration 0: log pseudolikelihood = -5556.8074
Iteration 1: log pseudolikelihood = -5507.4874
Iteration 2: log pseudolikelihood = -5506.2359
Iteration 3: log pseudolikelihood = -5506.2337
Iteration 4: log pseudolikelihood = -5506.2337
Weibull regression -- log relative-hazard form
No. of subjects = 4,866 Number of obs = 4,866
No. of failures = 2,117
Time at risk = 6193954
Wald chi2(36) = 105.86
Log pseudolikelihood = -5506.2337 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
| Robust
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
rld_tx_type |
2 | .8249158 .0641938 -2.47 0.013 .7082234 .9608354
3 | .7857402 .062952 -3.01 0.003 .6715562 .9193388
|
gender_group | 1.094763 .0534039 1.86 0.063 .9949418 1.2046
ecmo_trr | 1.587534 .9326214 0.79 0.431 .501963 5.020819
ventilator_trr | 1.325516 .1665057 2.24 0.025 1.036241 1.695545
|
blood_type |
2 | .9575172 .0441988 -0.94 0.347 .8746923 1.048185
3 | .9455973 .0703752 -0.75 0.452 .8172527 1.094098
4 | .9209833 .0991832 -0.76 0.445 .7457333 1.137418
|
year_tx |
2 | .8792394 .1061766 -1.07 0.287 .6939312 1.114032
3 | .9375805 .1130025 -0.53 0.593 .7403157 1.187409
4 | .8853886 .1082717 -1.00 0.320 .6966953 1.125188
5 | .817168 .0988432 -1.67 0.095 .6446908 1.035789
6 | .7776473 .0963679 -2.03 0.042 .6099578 .991438
7 | .8771987 .1082622 -1.06 0.288 .6887226 1.117253
8 | .7459922 .0964708 -2.27 0.023 .5789726 .961193
9 | .7436866 .1000223 -2.20 0.028 .5713568 .9679937
10 | .6911968 .1026914 -2.49 0.013 .5165809 .9248367
11 | .944747 .1380387 -0.39 0.697 .7094878 1.258016
12 | .7908836 .1392182 -1.33 0.183 .5601143 1.116731
13 | .4194482 .2500005 -1.46 0.145 .1304197 1.349004
|
Ethnic |
2 | 1.044576 .0912809 0.50 0.618 .8801515 1.239718
4 | 1.11143 .2007295 0.58 0.559 .7801019 1.583482
5 | .9110327 .2200833 -0.39 0.700 .5674197 1.462728
|
bmi_analysis | 1.010092 .0055142 1.84 0.066 .9993424 1.020958
pTLC_ratio_copd | .8998795 .0441061 -2.15 0.031 .8174554 .9906143
end_match_las | .9960659 .0054117 -0.73 0.468 .9855155 1.006729
age_don | 1.000985 .0015336 0.64 0.521 .9979834 1.003995
gender_mismatch | 1.16013 .0621598 2.77 0.006 1.044477 1.288588
ischtime | .9901301 .0149002 -0.66 0.510 .9613527 1.019769
end_o2 | 1.025314 .0115466 2.22 0.026 1.002931 1.048197
|
lung_preference |
2 | .910501 .1291987 -0.66 0.509 .6894396 1.202443
3 | .9074591 .0921592 -0.96 0.339 .7436717 1.107319
4 | .847192 .0941981 -1.49 0.136 .6812992 1.053479
5 | 1.095501 .109549 0.91 0.362 .9005204 1.332699
6 | .9505574 .1139962 -0.42 0.672 .7514455 1.202428
7 | .9216207 .1356492 -0.55 0.579 .6906654 1.229806
|
_cons | .0008421 .0002809 -21.23 0.000 .000438 .001619
----------------+----------------------------------------------------------------
/ln_p | -.0939497 .0231057 -4.07 0.000 -.1392361 -.0486633
----------------+----------------------------------------------------------------
p | .9103286 .0210338 .8700226 .9525018
1/p | 1.098504 .0253817 1.049867 1.149395
---------------------------------------------------------------------------------
.
. streg, coeflegend
Weibull regression -- log relative-hazard form
No. of subjects = 4,866 Number of obs = 4,866
No. of failures = 2,117
Time at risk = 6193954
Wald chi2(36) = 105.86
Log pseudolikelihood = -5506.2337 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
_t | Coef. Legend
----------------+----------------------------------------------------------------
rld_tx_type |
2 | -.1924739 _b[_t:2.rld_tx_type]
3 | -.2411291 _b[_t:3.rld_tx_type]
|
gender_group | .0905383 _b[_t:gender_group]
ecmo_trr | .4621821 _b[_t:ecmo_trr]
ventilator_trr | .2818021 _b[_t:ventilator_trr]
|
blood_type |
2 | -.0434116 _b[_t:2.blood_type]
3 | -.0559385 _b[_t:3.blood_type]
4 | -.0823133 _b[_t:4.blood_type]
|
year_tx |
2 | -.1286981 _b[_t:2.year_tx]
3 | -.0644526 _b[_t:3.year_tx]
4 | -.1217286 _b[_t:4.year_tx]
5 | -.2019106 _b[_t:5.year_tx]
6 | -.2514822 _b[_t:6.year_tx]
7 | -.1310217 _b[_t:7.year_tx]
8 | -.2930401 _b[_t:8.year_tx]
9 | -.2961355 _b[_t:9.year_tx]
10 | -.3693308 _b[_t:10.year_tx]
11 | -.0568382 _b[_t:11.year_tx]
12 | -.2346045 _b[_t:12.year_tx]
13 | -.8688152 _b[_t:13.year_tx]
|
Ethnic |
2 | .0436113 _b[_t:2.Ethnic]
4 | .1056479 _b[_t:4.Ethnic]
5 | -.0931765 _b[_t:5.Ethnic]
|
bmi_analysis | .0100419 _b[_t:bmi_analysis]
pTLC_ratio_copd | -.1054945 _b[_t:pTLC_ratio_copd]
end_match_las | -.0039418 _b[_t:end_match_las]
age_don | .0009842 _b[_t:age_don]
gender_mismatch | .1485317 _b[_t:gender_mismatch]
ischtime | -.0099189 _b[_t:ischtime]
end_o2 | .0249992 _b[_t:end_o2]
|
lung_preference |
2 | -.0937603 _b[_t:2.lung_preference]
3 | -.0971068 _b[_t:3.lung_preference]
4 | -.165828 _b[_t:4.lung_preference]
5 | .091212 _b[_t:5.lung_preference]
6 | -.0507067 _b[_t:6.lung_preference]
7 | -.0816215 _b[_t:7.lung_preference]
|
_cons | -7.079553 _b[_t:_cons]
----------------+----------------------------------------------------------------
/ln_p | -.0939497 _b[ln_p:_cons]
----------------+----------------------------------------------------------------
p | .9103286
1/p | 1.098504
---------------------------------------------------------------------------------
.
. margins, expression(exp(-exp(predict(xb))*1825^exp(_b[ln_p:_cons]))) at(rld_tx_type=(1 2 3))
Predictive margins Number of obs = 4,866
Model VCE : Robust
Expression : exp(-exp(predict(xb))*1825^exp(_b[ln_p:_cons]))
1._at : rld_tx_type = 1
2._at : rld_tx_type = 2
3._at : rld_tx_type = 3
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at |
1 | .4857455 .0222814 21.80 0.000 .4420748 .5294162
2 | .5503463 .0220442 24.97 0.000 .5071405 .593552
3 | .5659892 .0123369 45.88 0.000 .5418094 .590169
------------------------------------------------------------------------------
Thanks,
Luke
Comment