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* Program to run the parametric models *;
options ls=80;
data indat;
infile 'd:\survival\mrfit.dat';
input age 1-2 dbp 3-5 sbp 6-8 race 9 diab 10 chol 11-13 Gigs
exyr 16-17 exmo 18-19 exday 20-21 fyrs 22-23 icd9 24-27
dyr 28-29 dmo 30-31 dday 32-33 died 34;
exdate = mdy(exmo,exday,exyr);
ddate = mdy(dmo,dday,dyr);
fdate = mdy(12,31,90);
time = min(fdate,ddate) - exdate;
time = time / 365.25;
14-15
~~~tJ A'tI'tJ 11i"d
proc lifereg;
model time*died(O) = / dist=exponential;
proc lifereg;
model time*died(O) = / dist = weibull;
proc lifereg;
model time*died(O) = / dist = lognormal;
run;
-~- ~-
,-
The SAS System
The LIFEREG Procedure
Model Information
Data Set
Dependent Variable
Censoring Variable
Censoring Value(s)Number of Observations
Noncensored Values
Right Censored ValuesLeft Censored Values
Interval Censored Values
Name of Distribution
Log Likelihood
Algorithm converged.
Variable EstimateDF
InterceptScale
10
3.592021 .00000
WORK.INDAT
Log(time)died
0
5440
1989
3451
0
0
EXPONENT
-5285.705847
Analysis of Parameter Estimates
Standard
Error Chi-Square Pr > ChiSq Label
0.02242 25663.2812
0
<.0001 Intercept
Extreme value scale
Lagrange Multiplier StatisticsVariable Chi-Square Pr > ChiSq
Scale 280.8776 <.0001
~.
~~-----
The SAS System
The LIFEREG Procedure
Model Information
Data Set
Dependent Variable
Censoring Variable
Censoring Value(s)
Number of Observations
Noncensored Values
Right Censored Values
Left Censored Values
Interval Censored Values
Name of Distribution
Log Likelihood
Algorithm converged.
Variable EstimateOF
Intercept
Scale
11
3.361310.74919
WORK.INDAT
Log(time)
died0
544019893451
00
WEIBULL-5202.499725
Analysis of Parameter Estimates
Standard
Error Chi-Square Pr > ChiSq Label
0.02214 23043.9817
0.01595<.0001 Intercept
Extreme value scale
~---
The SAS System
The LIFEREG Procedure
Model Information
Data Set
Dependent Variable
Censoring Variable
Censoring Value(s)Number of Observations
Noncensored Values
Right Censored ValuesLeft Censored Values
Interval Censored Values
Name of Distribution
Log Likelihood
Algorithm converged.
r-
WORK.INDAT
Log(time)died
0
5440
1989
3451
0
0
LNORMAL
-5296.048383
Analysis of Parameter Estimates
Variable EstimateDF
InterceptScale
11
3.27958
1.35152
Standard
Error Chi-Square Pr > ChiSq Label
0.02746 14265.7131
0.02417<.0001 Intercept
Normal scale
* Program to take the results of the parametricanalysis and plot the parametric survival curvesvs. the KMsurvival curve. The relevant parametervalues are copied off of the printout from theparametric model analyses. *;
options ls=80;
filename gsasfile 'd:\survival\scurv.ps';goptions
device=psgsfname=gsasfilegsfmode=replacegsflen=80colors=(black);
symbo11 i=j line=1;symbo12 i=j line=2;axis1 length=5.5in label=('Kaplan-Meier');axis2 length=5.5in label=('Parametric Model');
data indat;infile 'd:\survival\mrfit.dat';input age 1-2 dbp 3-5 sbp 6-8 race 9 diab 10 chol 11-13 Gigs
exyr 16-17 exmo 18-19 exday 20-21 fyrs 22-23 icd9 24-27dyr 28-29 dmo 30-31 dday 32-33 died 34;
exdate = mdy(exmo,exday,exyr);ddate = mdy(dmo,dday,dyr);fdate = mdy(12,31,90);time = min(fdate,ddate) - exdate;time = time /365.25;
proc lifetest noprint notable outsurv=new method=km;time time*died(O);
data new2; set new;if _censor_=1 then delete;if time=O then delete;exppar = 3.59202;exp_lam = exp(-exppar);weipar1 = 3.36131;weipar2 = 0.74919;wei_lam = exp(-weipar1);wei_p = 1/weipar2;exp_fail = 1 - exp(-exp_lam*time);wei_fail = 1 - exp(-(wei_lam*time)**wei_p);km_fail = 1 - survival;Inmu = 3.27958;Insig = 1.35152;In_fail = probnorm((log(time)-lnmu)/lnsig);
keep time exp_fail wei_fail In_fail km_fail;
proc plot;
14-15
~- -------
plot exp_fail*km_fail='*';
plot wei_fail*km_fail='*';
plot In_fail*km_fail='*';
data pdat;
set;
model = 'Exponential';
legend= 'Graph';par_fail = exp_fail;
output;
legend = 'Reference';
par_fail = km_fail;
output;
model = 'Weibull';
legend= 'Graph';par_fail = wei_fail;
output;legend = 'Reference';
par_fail = km_fail;
output;
model = 'Lognormal';
legend= 'Graph';par_fail = In_fail;
output;legend = 'Reference';
par_fail = km_fail;
output;
proc sort; by model time;
proc gplot; by model;
plot par_fail*km_fail=legend / haxis=axis1 vaxis=axis2 frame;
run;
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exp_fail0.4
0.3
0.2
0.1
0.0
0.0000
**
~
Plot of exp_fail*km_fail. Symbol u~ed i$ '*',
*****
*****
****
***
****
****
****
****
***
***
****
***
**
**
0.0647 O. 1294 0 . 1941
km fail
NOTE: 1606 obs hidden.
*****
*****
****
****
0.2588
*****
0.3235
*****
****
*****
0.3882
7'
The SAS System
Plot of wei fail*km fail. Symbol used is '*'
wei fail0.4
*******
********
****
0.3 ********
****
****
***
****
0.2 ****
****
****
***
****
***
0.1 *****
*******
********
***0.0 ***
0.0000 0.0647 O. 1294 0 . 1941 0.2588 0.3235 0.3882
km fail
NOTE: 1605 obs hidden.
-~ --
The SAS System
Plot of In fail*km fail.- - Symbol used 1S '*'
In fail0.4
********
***********
0.3 **********
*********
*********
0.2 ********
*******
*******
0.1 ******
*******
*******
0.0 ***
0.0000 0.0551 0.1103 O.1654 0.2206 0.2757 0.3309 0.3860
km fail
NOTE: 1603 obs hidden.
-- -- -- --- ----
Parametric
model=Exponential
Model0.390.380.370.360.350.340.330.320.310.300.290.280.270.260.250.240.230.220.210.200.190.180.170.160.150.140.130.120.110.100.090.080.070.060.050.040.030.020.010.00
I .
0.00
....,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
",
0.20 0 .25 0.300 .05 0.10 0.15
Kaplan-Meier
legend Graph nn_n. Reference
",,,,,,,,,,,.'.'
0.35 0.40
Parametric Model0.390.380.370.360.350.340.330.320.310.300.290.280 . 270.260 .250.240.230.220.210.200.190.180.170.160.150.140.130.120.110.100.090.080.070.060.050 . 040.030.020.010 .00
I .0.00
model=Weibull
0.05
legend
0.10 0.40
Graph
0.15 0.20 0.25 0.30
Kaplan-Meier
nnn_. Reference
0.35
Parametric Model0.390.380.370 . 360.350.340.330.320.310.300.290.280.270.260.250.240.230.220.210.200.190.180.170.160.150.140 . 130.120.110.100.090.080.070.060.050.040.030.020.010.00
I .0.00
legend
model=Lognormal
.,-,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"",
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0.05 0.10 0.15 0.20 0.25 0.30
Kaplan-Meier
Graph Reference
,-,,,,,,,,,,,, .'.
0 . 35 0.40