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.
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.
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