Cprob-package: Conditional probability function of a competing event
In aallignol/Cprob: The Conditional Probability Function of a Competing Event

Description Details Author(s) References See Also

Estimates the conditional probability function of a competing event, and fits, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function

Package:	Cprob
Version:	1.0
Depends:	prodlim, tpr, lattice, geepack
License:	GPL (>=2)

Index:

cpf                     Conditional Probability Function of a Competing
                        Event
cpfpo                   Proportional-odds Model for the Conditional
                        Probability Function
lines.cpf               Lines method for 'cpf' objects
mgus                    Monoclonal Gammopathy of Undetermined
                        Significance
plot.cpf                Plot method for cpf objects
predict.cpf             Conditional Probability Estimates at chosen
                        timepoints
print.cpf               Print a cpf object
print.cpfpo             Print Method for cpfpo objects
pseudocpf               Pseudo values for the conditional probability
                        function
summary.cpf             Summary method for cpf
summary.pseudocpf       Summary method for pseudocpf objects
xyplot.cpfpo            'xyplot' method for object of class 'cpfpo'

The cpf function computes the conditional probability function of a competing event and can test equality of (only) two conditional probability curves.

A proportional-odds model for the conditional probability function can be fitted using either cpfpo or pseudocpf. The former function uses the temporal process regression methodology while the latter uses the pseudo value technique.

Arthur Allignol

Maintainer: Arthur Allignol <arthur.allignol@gmail.com>

M.S. Pepe and M. Mori, Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine, 12(8):737–751.

J.P. Fine, J. Yan and M.R. Kosorok (2004). Temporal Process Regression, Biometrika, 91(3):683-703.

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.

P.K. Andersen, J.P. Klein and S. Rosthoj (2003). Generalised Linear Models for Correlated Pseudo-Observations, with Applications to Multi-State Models. Biometrika, 90, 15-27.

J.P. Klein and P.K. Andersen (2005). Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function. Biometrics, 61, 223-229.

tpr

aallignol/Cprob documentation built on May 10, 2019, 3:09 a.m.