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In this document, we illustrate the main features of the
ememax R package through examples. Additional information
on the statistical methodology and computational details are provided in
the accompanying documentation and research articles.
The package applies methods introduced in the paper:
Zhang J, Pradhan V, Zhao Y. Robust Emax model fitting: Addressing nonignorable missing binary outcome in dose–response analysis. Statistical Methods in Medical Research. 2026;0(0). doi:10.1177/09622802251403356
Open the R console and run the following command to install the package from source:
install.packages("devtools") # When you have not installed devtools package
devtools::install_github("Celaeno1017/ememax")First, load the R package.
library(ememax)To illustrate the main features of the R package ememax,
let’s first generate some data. We have built in a few functions
directly into the R package for this purpose.
theta_true=matrix(c(qlogis(0.1),qlogis(0.8)-qlogis(0.1),log(7.5)),1,3) #true parmaeter of emax model
colnames(theta_true)<- c('e_0','emax','led_50')
theta_true <- as.data.frame(theta_true)
dose_set <- c(0,7.5,22.5,75,225) #doseage definition
n=355 #total number of sample size. The sample will be evenly allocated.
alpha_true = c(0.5,1,-0.5,0,0) #mis 15 typical
data <-sim_data(theta_true,n,dose_set,alpha_true)To fit the emEmax model with Firth type correction, we use the fitEmaxEM_firth function which implements the proposed methodology.
res <- fitEmaxEM_firth(data=data$data,mis_form=as.formula(mis~y+dose) )Key parameters include: - mis_form: The pre-defined
logistic model for missingness. If y is included as a covariate, that
means one considers the missingness is non-ignorable.
The result will contain the following values: - theta:
the final fitted parameters of Emax model
alpha: the final fitted parameters of the logistic
missing model
weight: the final fitted weight for each observation
in EM
Q: the value of Q function for maximizationfor each
iteration of EM
K: the total number of iterations of EM to
converge
vcov_theta: the estimated variance covariance matrix
of theta
vcov_alpha: the estimated variance covariance matrix
of alpha
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.