CrossGEEKron {CrossCarry}

Run a GEE model for data from a crossover experiment with repeated measures

Description

Provides a GEE model for the data of a crossover design with S sequences of T periods. There must be at least two observations of each experimental unit in each period.

Usage

CrossGEEKron(
  response,
  period,
  treatment,
  id,
  time,
  carry,
  covar = NULL,
  data,
  family = gaussian(),
  correlation = "independence",
  formula = NULL,
  tol = 1e-04,
  niter = 100
)

Arguments

response

A character string specifying the name of the response variable of the crossover experimental design

period

A character string specifying the name of vector with the observation period of the responses of the crossover experimental design

treatment

A character string specifying the name of vector with the treatment applied at each observation of the crossover experimental design

id

A character string specifying the name of vector which identifies the experimental units. The length of ‘id’ should be the same as the number of observations. Data are assumed to be sorted so that observations on each cluster appear as contiguous rows in data. If data is not sorted this way, the function will not identify the clusters correctly. If data is not sorted this way, a warning will be issued.

time

A character string specifying the name of the vector with the measurement time within each period

carry

A vector of character string specifying the name set of dummy variables that indicates the treatment applied in the previous period of each experimental unit. They must be 0 in period 1

covar

A vector of character string specifying the name of possible covariates of the crossover experimental design

data

A data frame with all the variables of the crossover experimental design

family

See corresponding documentation to glm.

correlation

character string specifying the correlation within periods structure. The following are permitted: "independence", "exchangeable", "ar1" and "unstructured".

formula

A formula related the response variable with the explanatory variables. If it is NULL the formula, response~period+treatment+carry+time+covar will be evaluated

tol

the tolerance used in the fitting algorithm.

niter

the maximum number of iterations. response~period+treatment+carry+time+covar will be evaluated

Value

QIC The QIC of the model: The model are fitted by geeglm

model The model fitted by geeglm.

Within The estimated correlation matrix within the period with the structure determined by correlation.

Between The estimated correlation matrix between periods

Source

https://doi.org/10.1007/s00362-022-01391-z

References

Cruz, N.A., Melo, O.O. & Martinez, C.A. A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures. Statistical Papers 65, 263–290 (2024)

Examples

data(Arterial)

carrydata <- createCarry(data=Arterial, treatment = "Treatment",
 period = "Period",id="Subject")

data <- carrydata$data
carry <- carrydata$carryover
model <- CrossGEEKron(response = "Pressure", treatment = "Treatment",
period = "Period", id="Subject", time="Time",
 carry=c("Carry_B","Carry_C"),data=data, correlation = "ar1")

model$QIC
model$Within
model$Between
summary(model$model)
model2 <- CrossGEEKron(response = "Pressure", treatment = "Treatment",
 period = "Period", id="Subject", time="Time",
 carry=c("Carry_B","Carry_C"), data=data,
 correlation = "ar1",formula=Pressure ~ Treatment+
 Period+ Carry_B+Carry_C)

model2$QIC
model2$Within
model2$Between
summary(model2$model)