WPC (version 1.0)

cox.wpc.est: Generate Weighted Predictiveness Curve Estimates Using Parametric Approach.

Description

This function generates weighted predictiveness curve estimates and/or confident bands using parametric approach.

Usage

cox.wpc.est(event, censor, marker, cutoff, quantile)

Arguments

event

This is the survival time. It is a positive numerical vector with no missing values.

censor

This specifies censor information. It is a vector, with 1 indicating an event and 0 indicating right censored. No missing values are allowed.

marker

This is the biomarker information (or other interesting variables). It is numerical with no missing values.

cutoff

This is to define the time cutoff.

quantile

This specifies the quantile of the confident band. Default is 0.95, 95% Confident band will be generated.

Value

A list with components: A list with components:

Details

The Cox proportional hazard model with a single biomarker will be used to derive and draw the predictiveness curve for parametric WPC. The relationship could be written in the form of the survival function as follows: $S(t)=[S_0(t)]^{exp{x\beta}}$, where S(t) is survival function, $S_0(t)$ is baseline survivor function, and x is the biomarker of interest. The effect of the biomarker is expressed by the $exp(x\beta)$ term and quantified as a shift from the baseline survival $S_0(t)$. Because $S_0(t)$ is always between 0 and 1, a positive coefficient $\beta$ will decrease the survival function with increasing biomarker values; a negative coefficient $\beta$ will increase the survival function with decreasing biomarker values. For any given time t, the baseline survival function $S_0(t)$ could be estimated. Therefore, with a fixed coefficient estimate and fixed time, we could do such prediction for a range of x values by fitting x values into the formula earlier and then connect the predictions derived from the smallest x value to the largest x value. That will form the predictiveness curve for that particular time point.

References

Yang H., Tang R., Hale M. and Huang J. (2016) A visualization method measuring the performance of biomarkers for guiding treatment decisions Pharmaceutical Statistics, 15(2), 1539-1612

See Also

npr.wpc.est

Examples

	## install packages "survival" and "msm"
	
	library("survival")
	library("msm")

	cox.object = cox.wpc.est(event=wpcdata$OSday, censor=wpcdata$OScensor, 
	marker=wpcdata$Biomarker1,cutoff=180,quantile=0.95)
	
	print(cox.object)