cpfpo {Cprob} R Documentation

## Proportional-odds Model for the Conditional Probability Function

### Description

This function uses the Temporal Process Regression framework to fit a proportional-odds model to the conditional probability function.

### Usage

```cpfpo(formula, data, subset, na.action, failcode, tis, w, ...)
```

### Arguments

 `formula` A formula object whose response, on the left of a ~ operator, is a `Hist` object, and the terms on the right of ~ `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 `options()\$na.action` `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 `tis`. Default is `rep(1, length(tis))` `...` Further arguments for `tpr`

### Details

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 proportional-odds 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 time-dependent 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.

### Value

`cpfpo` returns an object of class `cpfpo` and `tpr`. See `tpr` for further details.

### Note

As the returned value is also a `tpr` object, all the methods defined in the tpr package are available.

### Author(s)

Arthur Allignol, arthur.allignol@gmail.com

### References

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

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.

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`

### Examples

```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 odds-ratios
if(require("lattice")) {
xyplot(fit.cpfpo, scales = list(relation = "free"), layout = c(3, 1))
}
```

[Package Cprob version 1.4.1 Index]