| Title: | Bias-Corrected Estimates for Generalized Linear Models for Dependent Data |
|---|---|
| Description: | Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>. |
| Authors: | Nicola Lunardon <[email protected]>, Daniel Scharfstein <[email protected]> |
| Maintainer: | Nicola Lunardon <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.1 |
| Built: | 2024-11-09 03:06:01 UTC |
| Source: | https://github.com/cran/BCgee |
The function takes as an input an object of class gee and produces an object of class BCgee which contains the bias-corrected estimates of regression coefficients and further quantities; see details.
BCgee(fit)BCgee(fit)
fit |
A fitted model from |
The function computes bias-corrected estimates of regression coefficients by using quantities in the supplied object of class gee. The output is an object of class BCgee which has the same structure of an object of class gee. The output of the two classes differ because quantities included in the object of class BCgee are computed with the bias-corrected estimates.
Methods print and summary are available for objects of class BCgee.
Lunardon, N. [email protected]
Lunardon, N., Scharfstein, D. (2017). Comment on "Small sample GEE estimation of regression parameters for longitudinal data". Statistics in Medicine, <doi:10.1002/sim.7366>.
##Cerebrovascular deficiency example ##see page 153 of Diggle, P., Liang, K.-Y., Zeger, S. (1994). Analysis of longitudinal data. data(cereb) if(require(gee)){ fit <- gee(y ~ Period+Drug, id = id, data = cereb, family = binomial(logit), corstr = "exchangeable") fitbc <- BCgee(fit) ##compare coefficients, standard errors, and Wald statistics summary(fit)$coefficients summary(fitbc)$coefficients ##compare residuals fit$residuals fitbc$residuals } ##Seizure example from geepack ##see page 166 of Diggle, P., Liang, K.-Y., Zeger, S. (1994). Analysis of longitudinal data. data(seizure) seiz.l <- reshape(seizure, varying=list(c("base","y1", "y2", "y3", "y4")), v.names="y", times=0:4, direction="long") seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),] seiz.l$t <- ifelse(seiz.l$time == 0, 8, 2) seiz.l$x <- ifelse(seiz.l$time == 0, 0, 1) if(require(gee)){ fit <- gee(y ~ offset(log(t)) + x + trt + x:trt, id = id, data=seiz.l, corstr="exchangeable", family=poisson(log)) fitbc <- BCgee(fit) ##compare coefficients, standard errors, and Wald statistics summary(fit)$coefficients summary(fitbc)$coefficients ##compare residuals fit$residuals fitbc$residuals }##Cerebrovascular deficiency example ##see page 153 of Diggle, P., Liang, K.-Y., Zeger, S. (1994). Analysis of longitudinal data. data(cereb) if(require(gee)){ fit <- gee(y ~ Period+Drug, id = id, data = cereb, family = binomial(logit), corstr = "exchangeable") fitbc <- BCgee(fit) ##compare coefficients, standard errors, and Wald statistics summary(fit)$coefficients summary(fitbc)$coefficients ##compare residuals fit$residuals fitbc$residuals } ##Seizure example from geepack ##see page 166 of Diggle, P., Liang, K.-Y., Zeger, S. (1994). Analysis of longitudinal data. data(seizure) seiz.l <- reshape(seizure, varying=list(c("base","y1", "y2", "y3", "y4")), v.names="y", times=0:4, direction="long") seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),] seiz.l$t <- ifelse(seiz.l$time == 0, 8, 2) seiz.l$x <- ifelse(seiz.l$time == 0, 0, 1) if(require(gee)){ fit <- gee(y ~ offset(log(t)) + x + trt + x:trt, id = id, data=seiz.l, corstr="exchangeable", family=poisson(log)) fitbc <- BCgee(fit) ##compare coefficients, standard errors, and Wald statistics summary(fit)$coefficients summary(fitbc)$coefficients ##compare residuals fit$residuals fitbc$residuals }
The cereb data frame has 134 rows and 4 columns. The dataset consists of safety data from a crossover trial on the disease cerebrovascular deficiency. In this two-period crossover trial, comparing the effects of active drug to placebo, 67 patients were randomly allocated to the two treatment sequences, with 34 patients receiving placebo followed by active treatment, and 33 patients receiving active treatment followed by placebo. The response variable is binary, indicating whether an electrocardiogram (ECG) was abnormal (Y=1) or normal (Y=0). Each patient has a bivariate binary response vector.
data(cereb)data(cereb)
This data frame contains the following columns:
patient's unique id number
Period in the trial, 0=Period 1, 1=Period 2
Treatment, 0=Placebo, 1=Active Drug
ECG Response, 0=Normal, 1=Abnormal
Jones, B. and Kenward, M.G. (1989). Design and Analysis of Cross-over Trials. London: Chapman and Hall/CRC Press.
Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994). Analysis of Longitudinal Data. Clarendon Press.
##see help page of function BCgee##see help page of function BCgee
The seizure data frame has 59 rows and 7 columns. The dataset
has the number of epiliptic seizures in each of four two-week intervals,
and in a baseline eight-week inverval, for treatment and control groups
with a total of 59 individuals.
data(seizure)data(seizure)
This data frame contains the following columns:
the number of epiliptic seizures in the 1st 2-week interval
the number of epiliptic seizures in the 2nd 2-week interval
the number of epiliptic seizures in the 3rd 2-week interval
the number of epiliptic seizures in the 4th 2-week interval
an indicator of treatment
the number of epilitic seizures in a baseline 8-week interval
a numeric vector of subject age
Thall, P.F. and Vail S.C. (1990). Some covariance models for longitudinal count data with overdispersion, Biometrics, 46, 657–671.
Højsgaard, S., Halekoh, U. & Yan J. (2006). The R Package geepack for Generalized Estimating Equations, Journal of Statistical Software, 15, 1–11
Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994). Analysis of Longitudinal Data. Clarendon Press.
##see help page of function BCgee##see help page of function BCgee