mgee2k {mgee2}

mgee2k

Description

Corrected GEE2 for ordinal data. This method yields unbiased estimators, but the misclassification parameters are required to known.

Usage

mgee2k(
  formula,
  id,
  data,
  corstr = "exchangeable",
  misvariable,
  gamMat,
  varphiMat,
  maxit = 50,
  tol = 0.001
)

Arguments

formula

a formula object which specifies the relationship between the response and covariates for the observed data.

id

a character object which records individual id in the data.

data

a dataframe or matrix object for the observed data set.

corstr

a character object. The default value is "exchangeable", corresponding to the structure where the association between two paired responses is considered to be a constant. The other option is "log-linear" which indicates the log-linear association between two paired responses.

misvariable

a character object which names the error-prone covariate W.

gamMat

a matrix object which records the misclassification parameter gamma for response Y.

varphiMat

a matrix object which records the misclassification parameter phi for covariate X.

maxit

an integer which specifies the maximum number of iterations. The default is 50.

tol

a numeric object which indicates the tolerance threshold. The default is 1e-3.

Details

mgee2k implements the misclassification adjustment method outlined in Chen et al.(2014) where the misclassification parameters are known. In this case, validation data are not required, and only the observed data of the outcome and covariates are needed for the implementation.

Value

A list with component

beta

the coefficients in the order as those specified in the formula for the response and covariates.

alpha

the oefficients for paired responses global odds ratios. The number of alpha coefficients corresponds to the paired responses odds ratio structure selected in corstr. When corstr="exchangeable", only one baseline alpha is fitted. When corstr="log-linear", baseline, first order, second order (interaction) terms are fitted.

variance

variance-covariance matrix of the estimator of all parameters.

convergence

a logical variable; TRUE if the model converges.

iteration

the number of iterations for the estimates of the model parameters to converge.

differ

a list of difference of estimation for convergence

call

Function called

References

Z. Chen, G. Y. Yi, and C. Wu. Marginal analysis of longitudinal ordinal data with misclassification inboth response and covariates. Biometrical Journal, 56(1):69-85, Oct. 2014

Xu, Yuliang, Shuo Shuo Liu, and Y. Yi Grace. 2021. “mgee2: An R Package for Marginal Analysis of Longitudinal Ordinal Data with Misclassified Responses and Covariates.” The R Journal 13 (2): 419.

Examples

  if(0){
  data(obs1)
  obs1$visit <- as.factor(obs1$visit)
  obs1$treatment <- as.factor(obs1$treatment)
  obs1$S <- as.factor(obs1$S)
  obs1$W <- as.factor(obs1$W)
  ## set misclassification parameters to be known.
  varphiMat <- gamMat <- log( cbind(0.04/0.95, 0.01/0.95,
                                    0.95/0.03, 0.02/0.03,
                                    0.04/0.01, 0.95/0.01) )
  mgee2k.fit = mgee2k(formula = S~W+treatment+visit, id = "ID", data = obs1,
                    corstr = "exchangeable", misvariable = "W", gamMat = gamMat, 
                    varphiMat = varphiMat)
  }