Description Usage Arguments Details Value Author(s) References See Also Examples
Implementation of the SIMEX inverse probability weighted GEE method for longitudinal data with missing observations and measurement error in covariates
1 2 3 |
formula |
specifies the model to be fitted, with the variables coming with data. This argument has the same format as the formula argument in the function |
data |
an optional data frame in which to interpret the variables occurring in the formula, along with the id variable. |
id |
a vector which identifies the clusters. The length of id should be the same as the number of observations. Data are assumed to be sorted so that observations on a cluster are contiguous rows for all entities in the formula. |
family |
a family object as the family argument in the function |
corstr |
a character string specifying the correlation structure. The following are permitted: |
missingmodel |
specifies the misisng model to be fitted, of the form |
SIMEXvariable |
a vector of characters containing the name of the covariates subject to measurement error. |
SIMEX.err |
specifies the covariance matrix of measurement errors in error model. |
repeated |
This is the indicator if there are repeated measurements for the covariates with measurement error. The default value is FALSE. |
repind |
This is the index of the repeated measurement variables for each covariate with measurement error. It has an R list form. If repeated = TRUE, repind must be specified. |
B |
the number of simulated samples for the simulation step. The default is set to be 50. |
lambda |
a vector of lambdas for which the simulation step should be done. |
The quadratic extrapolation method is implemented as described in Cook and Stefanski
call |
the function call |
family |
family |
corstr |
correlation structure |
SIMEXvariable |
a vector of characters containing the name of the covariates subject to measurement error |
B |
the number of iterations |
beta |
the coefficients associated with the response process |
alpha |
the coefficients associated with the missing process |
simex.plot |
the estimates for every B and lambda |
Juan Xiong<jxiong@szu.edu.cn>, Grace Y. Yi<yyi@uwaterloo.ca>
Cook, J.R. and Stefanski, L.A. (1994) Simulation-extrapolation estimation in parametric measurement error models. Journal of the American Statistical Association, 89, 1314-1328.
Carrol, R.J., Ruppert, D., Stefanski, L.A. and Crainiceanu, C. (2006) Measurement error in nonlinear models: A modern perspective., Second Edition. London: Chapman and Hall.
Yi, G. Y. (2008) A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates. Biostatistics, 9, 501-512.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | require(gee)
data(BMI)
bmidata <- BMI
rho <- 0
sigma1 <- 0.5
sigma2 <- 0.5
sigma <- matrix(0,2,2)
sigma[1,1] <- sigma1*sigma1
sigma[1,2] <- rho*sigma1*sigma2
sigma[2,1] <- sigma[1,2]
sigma[2,2] <- sigma2*sigma2
set.seed(1000)
##naive method, ignore missingness and measurement error
output1 <- gee(bbmi~sbp+chol+age, id = id, data = bmidata,
family = binomial(link="logit"), corstr = "independence")
##swgee method ##########
output2 <- swgee(bbmi~sbp+chol+age, data = bmidata, id = id,
family = binomial(link="logit"),corstr = "independence",
missingmodel = O~bbmi+sbp+chol+age, SIMEXvariable = c("sbp","chol"),
SIMEX.err = sigma, repeated = FALSE, B = 20, lambda = seq(0, 2, 0.5))
summary(output2)
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