Comparison to other software

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

R code and results

library(eventglm)

colon.cifit <- cumincglm(Surv(time, status) ~ rx, time = 2500, data = colon)
summary(colon.cifit)
#> 
#> Call:
#> cumincglm(formula = Surv(time, status) ~ rx, time = 2500, data = colon)
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  0.54345    0.02946  18.449  < 2e-16 ***
#> rxLev       -0.02907    0.04173  -0.697  0.48596    
#> rxLev+5FU   -0.13176    0.04186  -3.148  0.00165 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for quasi family taken to be 1)
#> 
#>     Null deviance: 253.10  on 928  degrees of freedom
#> Residual deviance: 250.15  on 926  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 2
confint(colon.cifit)
#>                  2.5 %      97.5 %
#> (Intercept)  0.4857153  0.60118745
#> rxLev       -0.1108637  0.05271372
#> rxLev+5FU   -0.2137964 -0.04971921

Stata code and results

This uses the st0202_1 package, available from here: https://www.stata-journal.com/article.html?article=st0202_1

. import delimited "colon.csv", clear
(18 vars, 929 obs)

. 
. stset time, failure(status==1)

     failure event:  status == 1
obs. time interval:  (0, time]
 exit on or before:  failure

------------------------------------------------------------------------------
        929  total observations
          0  exclusions
------------------------------------------------------------------------------
        929  observations remaining, representing
        452  failures in single-record/single-failure data
  1,551,389  total analysis time at risk and under observation
                                                at risk from t =         0
                                     earliest observed entry t =         0
                                          last observed exit t =     3,329

. 
. // requires st0202_1 install (search stpci)
. stpci, at(2500)
Pseudo-observations for the cumulative incidence function.
Competing risks: (none).
Computing pseudo-observations (progress dots indicate percent completed).
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
Generated variable: pseudo.

. tabulate rx, gen(rxdum)

         rx |      Freq.     Percent        Cum.
------------+-----------------------------------
        Lev |        310       33.37       33.37
    Lev+5FU |        304       32.72       66.09
        Obs |        315       33.91      100.00
------------+-----------------------------------
      Total |        929      100.00

. glm pseudo rxdum1 rxdum2, vce(robust)

Iteration 0:   log pseudolikelihood =  -708.7556  

Generalized linear models                         Number of obs   =        929
Optimization     : ML                             Residual df     =        926
                                                  Scale parameter =    .270145
Deviance         =   250.154308                   (1/df) Deviance =    .270145
Pearson          =   250.154308                   (1/df) Pearson  =    .270145

Variance function: V(u) = 1                       [Gaussian]
Link function    : g(u) = u                       [Identity]

                                                  AIC             =   1.532305
Log pseudolikelihood = -708.7556003               BIC             =   -6078.23

------------------------------------------------------------------------------
             |               Robust
      pseudo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      rxdum1 |   -.029075   .0416186    -0.70   0.485    -.1106459    .0524959
      rxdum2 |  -.1317578   .0417443    -3.16   0.002    -.2135752   -.0499404
       _cons |   .5434514     .02938    18.50   0.000     .4858676    .6010352
------------------------------------------------------------------------------

SAS code and results

Assuming you have loaded the colon data in your workspace.

proc lifetest data=colon noprint plots=none timelist=2500 reduceout outsurv=sall;
time time*status(0);
run;

data sall;
set sall;
theta = survival;
keep theta;
run;

data sout;
set colon;
keep id;
run;

%macro pseudosurv;

%do ip=1 %to 929;
data coloni;
set colon;
where id ^= &ip;
run;

proc lifetest data=coloni noprint plots=none timelist=2500 reduceout outsurv=salli;
time time*status(0);
run;

data salli;
set salli;
thetamini = survival;
id = &ip;
keep id thetamini;
run;

data souti;
merge salli sall;
run;

data sout; 
merge sout souti;
by id;
run;
%end;
%mend pseudosurv;

%pseudosurv;


data sout2;
set sout;
pseudoci = 1 - (929 * theta - (929 - 1) * thetamini);
run;

data colon2;
merge colon sout2;
by id;
if rx='Lev' then rxlev = 1;
else rxlev = 0;
if rx='Lev+5FU' then rxlevplus = 1;
else rxlevplus = 0;
run;

proc reg data = colon2;
model pseudoci = rxlev rxlevplus / white;
run;

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > \|t\|	Heteroscedasticity Consistent
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > \|t\|	Standard Error	t Value	Pr > \|t\|
Intercept	1	0.54345	0.02928	18.56	<.0001	0.02936	18.51	<.0001
rxlev	1	-0.02907	0.04158	-0.70	0.4846	0.04160	-0.70	0.4847
rxlevplus	1	-0.13176	0.04179	-3.15	0.0017	0.04172	-3.16	0.0016

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.