README.md

In michellezhou2009/NCCIPW: NCCIPW

NCCIPW

This R package is to implement the proposed method in the manuscript titled "New weighting methods when cases are only a subset of events in a nested case-control study."

Instrall NCCIPW

To install this R package, you need to first install the devtool package via

install.packages("devtools")

To install the NCCIPW package,

devtools::install_github("michellezhou2009/NCCIPW")
library(NCCIPW)

Example

An example data myexample is included in the package, and it is a simulated data from the simulation setting in the manuscript. The following gives the R code to fit a time-dependent generalized linear model, and the output includes the IPW estimates using the Horvitz-Thompson's weight for the model parameters (i.e., regression coefficients) and accuracy parameters (including AUC and others) as well as their perturbed counterparts.

data("myexample")
GLM.IPW(formula=Surv(time,status)~marker1+marker2,
      data=myexample$data,
      id="id",
      case="case",
      control="control",
      m0=3,t0=1, 
      weight.type = "HT",      
      yes.match=T,control.matching=list(Mdat=myexample$Mdat,aM=myexample$aM),
      yes.ptb=TRUE,control.ptb=list(n.ptb=10,CaseID="CaseID"))

The following gives the R code to fit a Cox proportional hazards model, and the output includes the IPW estimates using the Horvitz-Thompson's weight for the model parameters (i.e., regression coefficients) and accuracy parameters (including AUC and others) as well as their perturbed counterparts.

PH.IPW(formula=Surv(time,status)~marker1+marker2,
      data=myexample$data,
      id="id",
      case="case",
      control="control",
      m0=3,t0=1, 
      weight.type = "HT",      
      yes.match=T,control.matching=list(Mdat=myexample$Mdat,aM=myexample$aM),
      yes.ptb=TRUE,control.ptb=list(n.ptb=10,CaseID="CaseID"))

michellezhou2009/NCCIPW documentation built on April 25, 2022, 3:39 p.m.