MMGOR: A Novel Minorize-Maximize Algorithm for the Generalized Odds Rate Model for Clustered Current Status Data

Overview

The MMGOR package implements a novel minorize-maximize algorithm to estimate parameters of generalized odds rate (GOR) model with clustered current status data. MMGOR allows any nonnegative r values of GOR model, which covers a wide range of commonly used survival models. The package takes advantage of C++ computational efficiency to reduce the computation time.

Installation

Downlowd the compressed file MMGOR_1.0.tar.gz to the working directory and use following command to install it

install.packages("MMGOR_1.0.tar.gz")

Example

There is an example to show how to use this package. First, use following command to simulate data. Data simulating has several dependencies gaussquad, numDeriv, extraDistr, fda, splines2 and nleqslv.

# Load package
library(MMGOR)

Scenario 1 (proportional hazard model): , , , ,

Simulate data:

set.seed(1)
H=function(t) log(1+t)+t^(3/2) 
data=data_for_est(r=0,beta=c(-1),gamma=c(-1),theta=1,n=300,H=H)

This function generates a cohort with 300 subjects, where each subject has up to 8 observations. Both the covariates of subject level and within-subject level are generated from uniform(-1,1) distribution. Users can use their own H function by changing the specific H function form.

Estimate parameters:

Regression parameters can be estimated using the command

result=MM_est(data$Delta,data$X,data$Z,data$n,data$ni,r=data$r,data$C,betadim=dim(data$X[[1]])[2],gammadim=dim(data$Z)[2],knotsnum = 2,order=2)

The output includes the parameters estimates and the corresponding standard errors.

Scenario 2 (proportional odds model): , , , ,

Simulate data:

set.seed(1)
H=function(t) log(1+t)+t^(3/2) 
data=data_for_est(r=1,beta=c(-1),gamma=c(-1),theta=1,n=300,H=H)

This function generates a cohort with 300 subjects, where each subject has up to 8 observations. Both the covariates of subject level and within-subject level are generated from uniform(-1,1) distribution. Users can use their own H function by changing the specific H function form.

Estimate parameters:

Regression parameters can be estimated using the command

result=MM_est(data$Delta,data$X,data$Z,data$n,data$ni,r=data$r,data$C,betadim=dim(data$X[[1]])[2],gammadim=dim(data$Z)[2],knotsnum = 2,order=2)

The output includes the parameters estimates and the corresponding standard errors.