The function computes pseudo values and then fit a proportionalodds model to the conditional probability function using GEE
1 2 
formula 
A formula object, whose terms are on the right of a ~
operator and the response, a 
data 
A data frame in which to interpret the formula 
id 
Individual patient id 
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

timepoints 
Time points at which to compute the pseudo values 
failcode 
Integer that specifies which event is of interest 
... 
Other arguments for the 
The regression model is fitted using a method based on the pseudovalues from a jackknife statistic constructed from the conditional probability curve. Then a GEE model is used on the pseudovalues to obtain the oddsratios.
Returns an object of class pseudocpf
containing the following
components:
fit 
A 
pseudo 
The pseudo values computed at the specified time points 
timepoints 
Same as in the function call 
call 
The matched call 
Besides the estimated regression coefficients, the function returns the computed pseudovalues, so that one can fit a different model, e.g., with a different link function.
Arthur Allignol, arthur.allignol@uniulm.de
P.K. Andersen, J.P. Klein and S. Rosthoj (2003). Generalised Linear Models for Correlated PseudoObservations, with Applications to MultiState Models. Biometrika, 90, 1527.
J.P. Klein and P.K. Andersen (2005). Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function. Biometrics, 61, 223229.
geese
, summary.pseudocpf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  data(mgus)
cutoffs < quantile(mgus$time, probs = seq(0, 1, 0.05))[1]
### with fancy variance estimation
fit1 < pseudocpf(Hist(time, ev) ~ age + creat, mgus, id = id,
timepoints = cutoffs, corstr = "independence",
scale.value = TRUE)
summary(fit1)
### with jackknife variance estimation
fit2 < pseudocpf(Hist(time, ev) ~ age + creat, mgus, id = id,
timepoints = cutoffs, corstr = "independence",
scale.value = TRUE, jack = TRUE)
summary(fit2)

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