README.md

In vanessaxiaofan/merc: Regression calibration methods for validation study and reliability study

merc

The goal of merc is to implement regression calibration methods for validation study and reliability study.

Installation

Install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("vanessaxiaofan/merc")

Example

This is a basic example which shows you how to solve a common problem:

library(merc)
## basic example code
# # Only one mismeasured covariate, logistic model
y <- c(1,1,2,3,2,3)
x <- c(2,2.1,3,4,2.9,4.1)
s <- c(2,5,4,3,3,5)
case <- c(0,1,0,1,0,0)
age <- c(10,10,11,12,13,13)
test <- data.frame(y,x,s,case,age)

x <- c(1.1,1.2,0.8)
x2 <- c(1.2,1.2,0.9)
x3 <- c(0.9,1.0,1.3)
relib <- data.frame(x,x2,x3)

wts <- data.frame(x=1,s=1)


### Fit main study logistic model
outModel <- glm(case ~ x + s , family = binomial(link="logit"), data = test)
outcomeParam=coef(outModel)
outcomeParamVCOV=vcov(outModel)
outcomeModelResults<-(list(outcomeParam,outcomeParamVCOV))
Bstar<-outcomeParam[2:length(outcomeParam)] #p' x 1
VBstar<-outcomeParamVCOV[2:length(outcomeParam),2:length(outcomeParam)] # p' x p'

# Provide point estimate
fit1 <- mercRel(supplyEstimates=TRUE, relib=relib, pointEstimates = Bstar, vcovEstimates = VBstar, sur = c("x"), woe = c("s"), weri = c("x","x2","x3"), rr=3, ms=test, weights=wts,link = "logit",method = "glm" )
fit1
#> 
#> Call:
#> mercRel(supplyEstimates = TRUE, relib = relib, pointEstimates = Bstar, 
#>     vcovEstimates = VBstar, sur = c("x"), woe = c("s"), weri = c("x", 
#>         "x2", "x3"), rr = 3, ms = test, weights = wts, method = "glm", 
#>     link = "logit")
#> 
#> Coefficients Uncorrected Model:
#>   Weights           B   SE(B)   OR(B)     Z Value  Pr(>|Z|) lower 95%CI
#> x       1 -0.04791056 1.10300 0.95322 -0.04343659 0.9653535     0.10972
#> s       1  0.42994470 0.83453 1.53717  0.51519382 0.6064176     0.29947
#>   upper 95%CI
#> x     8.28105
#> s     7.89022
#> 
#> Coefficients Corrected Model:
#>   Weights        B   SE(B)   OR(B)     Z Value  Pr(>|Z|) lower 95%CI(OR)
#> x       1 -0.05027 1.15735 0.95097 -0.04343543 0.9653544         0.09840
#> s       1  0.43037 0.83639 1.53783  0.51455661 0.6068629         0.29851
#>   lower 95%CI(OR)
#> x         9.19018
#> s         7.92240

# Without point estimate
fit2 <- mercRel(supplyEstimates=FALSE, relib = relib, sur = c("x"), woe = c("s"), weri = c("x","x2","x3"), outcome = c("case"), rr=3, ms=test,method = "glm", family = binomial, link = "logit", weights=wts)
fit2
#> 
#> Call:
#> mercRel(supplyEstimates = FALSE, relib = relib, sur = c("x"), 
#>     woe = c("s"), outcome = c("case"), weri = c("x", "x2", "x3"), 
#>     rr = 3, ms = test, weights = wts, method = "glm", family = binomial, 
#>     link = "logit")
#> 
#> Coefficients Uncorrected Model:
#>   Weights           B   SE(B)   OR(B)     Z Value  Pr(>|Z|) lower 95%CI
#> x       1 -0.04791056 1.10300 0.95322 -0.04343659 0.9653535     0.10972
#> s       1  0.42994470 0.83453 1.53717  0.51519382 0.6064176     0.29947
#>   upper 95%CI
#> x     8.28105
#> s     7.89022
#> 
#> Coefficients Corrected Model:
#>   Weights        B   SE(B)   OR(B)     Z Value  Pr(>|Z|) lower 95%CI(OR)
#> x       1 -0.05027 1.15735 0.95097 -0.04343543 0.9653544         0.09840
#> s       1  0.43037 0.83639 1.53783  0.51455661 0.6068629         0.29851
#>   lower 95%CI(OR)
#> x         9.19018
#> s         7.92240

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.

vanessaxiaofan/merc documentation built on May 7, 2023, 6:17 p.m.