Demography and epidemiology: Practical use of the Lexis diagram ...
Demography and epidemiology: Practical use of the Lexis diagram ...
Demography and epidemiology: Practical use of the Lexis diagram ...
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18 9 Programs <strong>use</strong>d<br />
9 Programs <strong>use</strong>d<br />
lung-ex.R<br />
Program that takes <strong>the</strong> Mayo clinic lung cancer survival data, <strong>and</strong> analyses it both by<br />
<strong>the</strong> Cox-model <strong>and</strong> by <strong>the</strong> Poisson-model for split follow-up data.<br />
Illustrates that <strong>the</strong> Cox-likelihood <strong>and</strong> <strong>the</strong> Poisson-approach gives exactly <strong>the</strong> same<br />
results whereas <strong>the</strong> approaches smoothing <strong>the</strong> underlying hazard gives almost <strong>the</strong> same.<br />
Also illustrates how to compute <strong>the</strong> survival function with confidence limits using<br />
<strong>the</strong> Poisson-approach with smooth underlying hazard.<br />
R 1.9.0<br />
---------------------------------------------<br />
Program: lung-ex.R<br />
Folder: C:\Bendix\Artikler\WntCma\R<br />
Started: tirsdag 10. august 2004, 14:29:38<br />
---------------------------------------------<br />
> library( splines )<br />
> library( <strong>Lexis</strong> )<br />
Attaching package '<strong>Lexis</strong>':<br />
The following object(s) are masked from package:Useful :<br />
ci.lin steplines<br />
The following object(s) are masked from package:Epi :<br />
interp lines.est nice plot.est points.est print.floated ROC ROC.tic steplines tabplot twoby2 weeks<br />
> library( survival )<br />
> data( lung )<br />
> str( lung )<br />
`data.frame': 228 obs. <strong>of</strong> 10 variables:<br />
$ inst : num 3 3 3 5 1 12 7 11 1 7 ...<br />
$ time : num 306 455 1010 210 883 ...<br />
$ status : num 2 2 1 2 2 1 2 2 2 2 ...<br />
$ age : num 74 68 56 57 60 74 68 71 53 61 ...<br />
$ sex : num 1 1 1 1 1 1 2 2 1 1 ...<br />
$ ph.ecog : num 1 0 0 1 0 1 2 2 1 2 ...<br />
$ ph.karno : num 90 90 90 90 100 50 70 60 70 70 ...<br />
$ pat.karno: num 100 90 90 60 90 80 60 80 80 70 ...<br />
$ meal.cal : num 1175 1225 NA 1150 NA ...<br />
$ wt.loss : num NA 15 15 11 0 0 10 1 16 34 ...<br />
><br />
> table( lung$status )<br />
1 2<br />
63 165<br />
> table( table( lung$time ) )<br />
1 2 3<br />
146 38 2<br />
><br />
> system.time(<br />
+ c.res summary( c.res )<br />
Call:<br />
coxph(formula = Surv(time, status == 2) ~ age + factor(sex),<br />
data = lung, method = "breslow", eps = 10^-8, iter.max = 25)<br />
n= 228<br />
coef exp(coef) se(coef) z p<br />
age 0.017 1.017 0.00922 1.84 0.0650<br />
factor(sex)2 -0.513 0.599 0.16746 -3.06 0.0022<br />
exp(coef) exp(-coef) lower .95 upper .95<br />
age 1.017 0.983 0.999 1.036<br />
factor(sex)2 0.599 1.670 0.431 0.832<br />
Rsquare= 0.06 (max possible= 0.999 )
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