pseudomean: Pseudo-observations for the restricted mean

In pseudo: Computes Pseudo-Observations for Modeling

Description

Computes pseudo-observations for modeling survival function based on the restricted mean.

Usage

pseudomean(time,event, tmax)

Arguments

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.

Details

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.

Value

A vector of pseudo-observations for each individual.

References

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

See Also

pseudosurv, pseudoci

Examples

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)

Example output

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

pseudo documentation built on May 1, 2019, 6:35 p.m.