mgee2 (version 0.5)

heart: heart: preprocessed Framingham Heart Study Teaching data

Description

heart: preprocessed Framingham Heart Study Teaching data

Usage

heart

Format

a dataframe with 1830 rows and 42 variables, a total of 915 participants.

RANDID

individual id number

HBP

a factor variable derived from SYSBP. HBP=0 indicates SBP below 140 mmHg, HBP=1 indicates SBP between 140 mmHg and 159 mmHg, and HBP=2 indicates SBP larger than 160 mmHg

chol

a factor variable derived from TOTCHOL. 0=normal (less than 200 mg/dL), 1=borderline high (200-239mg/dL), 2=hypercholesterolemia (greater than 240 mg/dL)

exam3

a factor variable. 1 if the observation belongs to exam 3, 0 otherwise.

For all other variables, please refer to https://biolincc.nhlbi.nih.gov/media/teachingstudies/FHS_Teaching_Longitudinal_Data_Documentation.pdf?link_time=2021-03-17_16:09:25.977880, The full teaching data set can be requested from https://biolincc.nhlbi.nih.gov/teaching/

Details

The authors thank 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.

References

Z. Chen, G. Y. YI, and C. WU. (2011) Marginal methods for correlated binary data with misclassified responses. Biometrika 98(3):647-662, 2011 Z. Chen, G. Y. Yi, and C. Wu. (2014) Marginal analysis of longitudinal ordinal data with misclassification inboth response and covariates. Biometrical Journal, 56(1):69-85, Oct. 2014 Carroll, 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.

Examples

{
    data(heart)
    #descriptive plots:
    if(0){
      library(mgee2)
      library(ggplot2)
      # covariates
      heart$chol = as.factor(heart$chol)
      heart$CURSMOKE = as.factor(heart$CURSMOKE)
      heart$exam3 = as.factor(heart$exam3)
      levels(heart$exam3) = c("exam2","exam3")
      ggplot(heart, aes(x=AGE, y=SYSBP)) +
        geom_line(aes(group=RANDID), alpha=0.5) +
        geom_smooth(se=FALSE, size=2) +
        ylab("SBP")+
        facet_grid(chol~CURSMOKE, labeller = label_both)
     # trend
     ggplot(heart, aes(x=AGE, y=SYSBP,
                       colour = chol,linetype = CURSMOKE)) +
                         geom_smooth(method="lm", se=FALSE) +
                         ylab("SBP")+facet_wrap(~exam3)+
                         scale_color_brewer(palette = "Dark2")
    }
    #Example 1:
    heart$chol = as.factor(heart$chol)
    heart$exam3 = as.factor(heart$exam3)
    ## set misclassification parameters to be known.
    varphiMat <- gamMat <- log( cbind(0.04/0.95, 0.01/0.95,
                                      0.95/0.03, 0.02/0.03,
                                      0.04/0.01, 0.95/0.01) )
    mgee2k.fit = mgee2k(formula = HBP~chol+AGE+CURSMOKE+exam3, id = "RANDID",
                        data = heart,
                        corstr = "exchangeable", misvariable = "chol", 
                        gamMat = gamMat,
                        varphiMat = varphiMat)
    summary(mgee2k.fit)
    
    #Example 2:
    naigee.fit = ordGEE2(formula = HBP~chol+AGE+CURSMOKE+exam3, id = "RANDID",
    data = heart, corstr = "exchangeable")
    summary(naigee.fit)
    }