EstimateHR: Classification, Survival Estimation and Visualization

Description

The Function classifies subjects into low and high risk group using the risk scores based on the cut-off percentile specified. It also visualize survival fit along with HR estimates.

Usage

EstimateHR(
  Risk.Scores,
  Data.Survival,
  Prognostic = NULL,
  Plots = FALSE,
  Quantile = 0.5
)

Arguments

Risk.Scores

A vector of risk scores with size equals to number of subjects

Data.Survival

A dataframe in which the first column is the survival time and the second column is the Censoring indicator for each subject.

Prognostic

A dataframe containing possible prognostic(s) factor and/or treatment effect

Plots

A boolean parameter indicating if plots should be shown. Default is FALSE

Quantile

The cut off value for the classifier, default is the median cutoff

Value

An object of is returned, which is a list with the results of the cox regression and some informative plot concerning survival of the risk group.

SurvResult

The cox proportional regression result

Riskgroup

The riskgroup based on the riskscore and the cut off value and length is equal to number of subjects

KMplot

The Kaplan-Meier survival plot of the riskgroup

SurvBPlot

The distribution of the survival in the riskgroup

Details

The risk scores obtained using the signature is used to generate the risk group by dividing subjects into low and high risk group. A Cox model is then fitted with the risk group as covariate in the presence or absence of prognostic factors and or treatment effect. The extent of survival in the risk groups is known.

Examples

Run this code

# NOT RUN {
### Classification and estimating with prognostic factors
data(DataHR)
Result = EstimateHR(Risk.Scores=DataHR[,1],Data.Survival=DataHR[,2:3]
,Prognostic=DataHR[,4:5],Plots=FALSE,Quantile=0.50)

### Classification and estimating without prognostic factors
data(DataHR)
Result = EstimateHR(Risk.Scores=DataHR[,1],Data.Survival=DataHR[,2:3]
,Prognostic=NULL,Plots=FALSE,Quantile=0.50)
# }