# Conditional probability function of a competing event

### Description

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

### Details

Package: | Cprob |

Version: | 1.0 |

Depends: | prodlim, tpr, lattice, geepack |

License: | GPL (>=2) |

Index:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
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.

### Author(s)

Arthur Allignol

Maintainer: Arthur Allignol <arthur.allignol@uni-ulm.de>

### References

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.

### See Also

`tpr`

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