Description Usage Arguments Details Value References See Also Examples
Computes pseudo-observations for modeling survival function based on the restricted mean.
1 | pseudomean(time,event, tmax)
|
time |
the follow up time. |
event |
the status indicator: 0=alive, 1=dead. |
tmax |
the maximum cut-off point for the restricted mean. If missing or larger than the maximum follow up time, it is replaced by the maximum follow up time. |
The function calculates the pseudo-observations for the restricted mean survival for each individual at prespecified time-points.
The pseudo-observations can be used for fitting a regression model with a generalized estimating equation.
No missing values in either time
or event
vector are allowed.
Please note that the output of the function has changed and the usage is thus no longer the same as in the reference paper - the new usage is described in the example below.
A vector of pseudo-observations for each individual.
Klein J.P., Gerster M., Andersen P.K., Tarima S., POHAR PERME, M.: "SAS and R Functions to Compute Pseudo-values for Censored Data Regression." Comput. methods programs biomed., 2008, 89 (3): 289-300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | library(KMsurv)
data(bmt)
#compute the pseudo-observations:
pseudo = pseudomean(time=bmt$t2, event=bmt$d3,tmax=2000)
#arrange the data
a <- cbind(bmt,pseudo = pseudo,id=1:nrow(bmt))
#fit a regression model for the mean time
library(geepack)
summary(fit <- geese(pseudo ~ z1 + as.factor(z8) + as.factor(group),
data = a, id=id, jack = TRUE, family=gaussian,
corstr="independence", scale.fix=FALSE))
#rearrange the output
round(cbind(mean = fit$beta,SD = sqrt(diag(fit$vbeta.ajs)),
Z = fit$beta/sqrt(diag(fit$vbeta.ajs)), PVal =
2-2*pnorm(abs(fit$beta/sqrt(diag(fit$vbeta.ajs))))),4)
|
Loading required package: KMsurv
Loading required package: geepack
Call:
geese(formula = pseudo ~ z1 + as.factor(z8) + as.factor(group),
id = id, data = a, family = gaussian, scale.fix = FALSE,
corstr = "independence", jack = TRUE)
Mean Model:
Mean Link: identity
Variance to Mean Relation: gaussian
Coefficients:
estimate san.se ajs.se wald p
(Intercept) 1154.99718 219.26127 223.114730 27.7484043 1.381621e-07
z1 -11.55564 6.88759 7.067215 2.8148364 9.339642e-02
as.factor(z8)1 -518.60039 169.54383 172.840899 9.3562473 2.222267e-03
as.factor(group)2 630.54074 185.49115 187.292713 11.5552657 6.755765e-04
as.factor(group)3 143.50411 216.88341 220.748022 0.4378002 5.081861e-01
Scale Model:
Scale Link: identity
Estimated Scale Parameters:
estimate san.se ajs.se wald p
(Intercept) 636446.7 49205.89 52042.62 167.2977 0
Correlation Model:
Correlation Structure: independence
Returned Error Value: 0
Number of clusters: 137 Maximum cluster size: 1
mean SD Z PVal
(Intercept) 1154.9972 223.1147 5.1767 0.0000
z1 -11.5556 7.0672 -1.6351 0.1020
as.factor(z8)1 -518.6004 172.8409 -3.0004 0.0027
as.factor(group)2 630.5407 187.2927 3.3666 0.0008
as.factor(group)3 143.5041 220.7480 0.6501 0.5156
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