mgee2v {mgee2}

mgee2v

Description

Corrected GEE2 for ordinal data, with validation subsample

Usage

mgee2v(
  formula,
  id,
  data,
  corstr = "exchangeable",
  misvariable = "W",
  valid.sample.ind = "delta",
  y.mcformula,
  x.mcformula,
  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.

valid.sample.ind

a string object which names the indicator variable delta. When a data point belongs to the validation set, delta = 1; otherwise 0.

y.mcformula

a string object which indicates the misclassification formula between true response Y and surrogate(observed) response S.

x.mcformula

a string object which indicates the misclassification formula between true error-prone covariate X and surrogate W.

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

The function mgee2v does not require the misclassification parameters to be known, but require the availability of validation data. Similar to mgee2k, the function mgee2v needs the data set to be structured by individual id, i=1,...,n, and visit time, j_i=1,...,m_i. The data set should contain the observed response and covariates S and W. To indicate whether or not a subject is in the validation set, an indicator variable delta should be added in the data set, and we use a column named valid.sample.ind for this purpose. The column name of the error-prone covariate W should also be specified in misvariable.

Value

A list with component

beta

the coefficients in the order of 1) all non-baseline levels for response, 2) covariates - same order as specified in the formula

alpha

the coefficients for paired responses global odds ratios. Number of alpha coefficients corresponds to the paired responses odds ratio structure selected in "corstr"; when corstr="exchangeable", only one baseline alpha is fitted.

variance

variance-covariance matrix of all fitted parameters

convergence

a logical variable, TRUE if the model converges

iteration

number of iterations for the model to converge

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$Y <- as.factor(obs1$Y)
  obs1$X <- as.factor(obs1$X)
  obs1$visit <- as.factor(obs1$visit)
  obs1$treatment <- as.factor(obs1$treatment)
  obs1$S <- as.factor(obs1$S)
  obs1$W <- as.factor(obs1$W)
  mgee2v.fit = mgee2v(formula = S~W+treatment+visit, id = "ID", data = obs1,
                      y.mcformula = "S~1", x.mcformula = "W~1", misvariable = "W",
                      valid.sample.ind = "delta",
                      corstr = "exchangeable")
  }