1. Introduction

remss from github is a package for TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of HPV+ and HPV- diseases.We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance.

2. Installation

You can install remss from [github] (https://github.com/qiuanzhu/remss):

library("devtools")
install_github("qiuanzhu/remss")

3. Examples

In the following examples, a simulated dataset with 10 variables and 504 observations.

library("remss")
head(Rdata)

#> 
#> Attaching package: 'remss'
#> The following object is masked from 'package:base':
#> 
#>     rank

There are three grouping schemes (Scheme.1,Scheme.2,Scheme.3) base on the classification (Basic_group). Five measures of grouping scheme and weight for each measurement are introduced.

data(Rdata)
Scheme=c('Scheme.1','Scheme.2','Scheme.3')
Covar=c('Age','Treatment')
weight=c(1,1,0.5,0.5,1)
Order=list(c('I','II','III'),c('I','II','III','IV'),c('I','II','III','IV'))
table<-rank(os='OS',ostime='survmonth',groupvar='Basic_group', scheme=Scheme, order=Order, covariate=Covar,weight=weight,data=Rdata)

#> Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
#> Loglik converged before variable 16,19 ; coefficient may be infinite.

Variable Information:

Observation number:

Hazard Consistency Measurement:

Hazard Discrimination Measurement:

Likelihood Difference Measurement:

Explained Variance Measurement:

Balance Measurement:

Overall Rank: