| Title: | Simulation Extrapolation Inverse Probability Weighted Generalized Estimating Equations |
|---|---|
| Description: | Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0). |
| Authors: | Juan Xiong <[email protected]>, Grace Y. Yi <[email protected]> |
| Maintainer: | Juan Xiong <[email protected]> |
| License: | GPL-3 |
| Version: | 1.4 |
| Built: | 2024-12-11 07:03:00 UTC |
| Source: | CRAN |
Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).
The DESCRIPTION file:
| Package: | swgee |
| Type: | Package |
| Title: | Simulation Extrapolation Inverse Probability Weighted Generalized Estimating Equations |
| Version: | 1.4 |
| Date: | 2019-03-20 |
| Imports: | stats, graphics, gee, geepack, mvtnorm, |
| LazyLoad: | yes |
| Author: | Juan Xiong <[email protected]>, Grace Y. Yi <[email protected]> |
| Maintainer: | Juan Xiong <[email protected]> |
| Description: | Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0). |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Packaged: | 2019-03-20 07:20:42 UTC; apple |
| Repository: | CRAN |
| Date/Publication: | 2019-03-20 07:40:02 UTC |
| Config/pak/sysreqs: | libicu-dev |
Index of help topics:
BMI BMI dataset
getsimexest getsimexest
plot.swgee plot.swgee
print.summary.swgee print.summary.swgee
print.swgee print.swgee
summary.swgee summary.swgee
swgee Simulation Extrapolation Inverse Probability
Weighted Generalized Estimating Equations
swgee-package Simulation Extrapolation Inverse Probability
Weighted Generalized Estimating Equations
Implementation of the SIMEX inverse probability weighted GEE method for longitudinal data with missing observations and measurement error in covariates
Juan Xiong <[email protected]>, Grace Y. Yi<[email protected]>
Maintainer: Juan Xiong <[email protected]>
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.
a subset of the the Framingham Heart Study Data. The data set consists of measurements of 100 patients from a series of exams with 5 assessments for each individual. Individual's obesity status, age, systolic blood pressure (SBP) and cholesterol level (CHOL) are collected at each assessment.
data("BMI")data("BMI")
A data frame with 500 observations on the following 7 variables.
ida numeric vector for subject id
visita numeric vector for assessment time
agea numeric vector of age
sbpa numeric vector of systolic blood pressure
chola numeric vector of cholesterol level
bbmian indicator of obesity status(1=yes, 0=no)
Oan indicator of observed measurement(1=yes, 0=no)
The author thanks Boston University and the National Heart, Lung, and Blood Institute (NHLBI) for providing the data set from the Framingham Heart Study (No. N01-HC-25195) in the illustration. The Framingham Heart Study is conducted and supported by the NHLBI in collaboration with Boston University. This package was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.
Strug, L., Sun, L. and Corey, M. (2003). The genetics of cross-sectional and longitudinal body mass index. BMC Genetics 4 (Suppl 1), S14
Yoo, Y. J., Huo, Y., Ning, Y., Gordon, D., Finch, S. and Mendell, N. R. (2003). Power of maximum HLOD tests to detect linkage to obesity genes. BMC Genetics 4 (Suppl 1), S16.
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.
data(BMI)data(BMI)
extract the estimates for every lambda
getsimexest(indata)getsimexest(indata)
indata |
swgee object from the function swgee |
internal function for the extrapolation step
Juan Xiong<[email protected]>, Grace Y. Yi<[email protected]>
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.
Produce the plot of the quadratic extrapolation curve for any covariables with measurement error in the swgee model
## S3 method for class 'swgee' ## S3 method for class 'swgee' plot(x, covariate, ...)## S3 method for class 'swgee' ## S3 method for class 'swgee' plot(x, covariate, ...)
x |
object of class 'swgee' |
covariate |
covariates specified in the formula |
... |
further arguments passed to or from other functions. |
Plot the simulation and extrapolation step
Juan Xiong<[email protected]>, Grace Y. Yi<[email protected]>
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.
require(gee) require(mvtnorm) 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) ##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) plot(output2,"sbp")require(gee) require(mvtnorm) 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) ##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) plot(output2,"sbp")
Summary method for class "swgee"
## S3 method for class 'swgee' ## S3 method for class 'summary.swgee' print(x, ...)## S3 method for class 'swgee' ## S3 method for class 'summary.swgee' print(x, ...)
x |
object of class 'swgee' |
... |
further arguments passed to or from other functions. |
Print summary nicely
Juan Xiong<[email protected]>, Grace Y. Yi<[email protected]>
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.
Summary method for class "swgee"
## S3 method for class 'swgee' ## S3 method for class 'swgee' print(x, ...)## S3 method for class 'swgee' ## S3 method for class 'swgee' print(x, ...)
x |
object of class 'swgee' |
... |
further arguments passed to or from other functions. |
Print swgee object nicely
Juan Xiong<[email protected]>, Grace Y. Yi<[email protected]>
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.
Summary method for class "swgee"
## S3 method for class 'swgee' summary(object, ...)## S3 method for class 'swgee' summary(object, ...)
object |
object of class 'swgee' |
... |
further arguments passed to or from other functions. |
The function summary.swgee computes and returns a list of summary statistics of the response process and missing process
summary estimates for parameters associated with response process and missing process
Juan Xiong<[email protected]>, Grace Y. Yi<[email protected]>
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.
Implementation of the SIMEX inverse probability weighted GEE method for longitudinal data with missing observations and measurement error in covariates
swgee(formula, data = parent.frame(), id, family = family, corstr = "independence", missingmodel, SIMEXvariable, SIMEX.err, repeated = FALSE, repind = NULL, B = 50, lambda = seq(0, 2, 0.5))swgee(formula, data = parent.frame(), id, family = family, corstr = "independence", missingmodel, SIMEXvariable, SIMEX.err, repeated = FALSE, repind = NULL, B = 50, lambda = seq(0, 2, 0.5))
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<[email protected]>, Grace Y. Yi<[email protected]>
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.
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)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)