This function uses the Temporal Process Regression framework to fit a proportionalodds model to the conditional probability function.
1 
formula 
A formula object whose response, on the left of a ~
operator, is a 
data 
A data.frame in which to interpret the variable names in the formula and subset 
subset 
Expression specifying that only a subset of the data set should be used 
na.action 
A missing data filter funtion applied to the
model.frame, after any subset argument has been used. Default is

failcode 
Integer specifying the code for the event of interest 
tis 
Vector of timepoints on which the model is fitted 
w 
Vector of weights. Should be of the same length as

... 
Further arguments for 
The conditional probability function of a competing event is the probability of having failed due to one risk (the event of interest) given that no other failure has previously occurred.
The cpfpo
function fits a proportionalodds model for the
conditional probability function within the Temporal Process
Regression framework, which is a marginal mean model, where the mean
of a response Y(t) at time t is specified
conditionally on a vector of covariates Z and a
timedependent stratification factor S(t)
E{Y(t)  Z, S(t) = 1} = g^(1){beta(t)'Z}
This approach enables the application of standard binary regression models in continuous time.
The regression model is fitted using the tpr package. See
tpr
for further details.
cpfpo
returns an object of class cpfpo
and
tpr
. See
tpr
for further details.
As the returned value is also a tpr
object, all the methods
defined in the tpr package are available.
Arthur Allignol, arthur.allignol@uniulm.de
J.P. Fine, J. Yan and M.R. Kosorok (2004). Temporal Process Regression, Biometrika, 91(3):683703.
M.S. Pepe and M. Mori, KaplanMeier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine, 12(8):737–751.
A. Allignol, A. Latouche, J. Yan and J.P. Fine (2011). A regression model for the conditional probability of a competing event: application to monoclonal gammopathy of unknown significance. Journal of the Royal Statistical Society: Series C, 60(1):135–142.
tpr
, print.cpfpo
,
xyplot.cpfpo
1 2 3 4 5 6 7 8 9 10 11  data(mgus)
mgus$A < ifelse(mgus$age < 64, 0, 1)
## fit the model for 2 covariates
fit.cpfpo < cpfpo(Hist(time, ev)~factor(A) + creat,
data = mgus, tis=seq(10, 30, 0.3),
w=rep(1,67))
## and plot the oddsratios
if(require("lattice")) {
xyplot(fit.cpfpo, scales = list(relation = "free"), layout = c(3, 1))
}

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