Data on Obesity from the Muscatine Coronary Risk Factor Study.

Description

The data are from the Muscatine Coronary Risk Factor (MCRF) study, a longitudinal survey of school-age children in Muscatine, Iowa. The MCRF study had the goal of examining the development and persistence of risk factors for coronary disease in children. In the MCRF study, weight and height measurements of five cohorts of children, initially aged 5-7, 7-9, 9-11, 11-13, and 13-15 years, were obtained biennially from 1977 to 1981. Data were collected on 4856 boys and girls. On the basis of a comparison of their weight to age-gender specific norms, children were classified as obese or not obese.

Usage

muscatine

Format

A dataframe with 14568 rows and 7 variables:

id

identifier of child.

gender

gender of child

base_age

baseline age

age

current age

occasion

identifier of occasion of recording

obese

'yes' or 'no'

numobese

obese in numerical form: 1 corresponds to 'yes' and 0 corresponds to 'no'.

Source

https://content.sph.harvard.edu/fitzmaur/ala2e/muscatine.txt

Woolson, R.F. and Clarke, W.R. (1984). Analysis of categorical incompletel longitudinal data. Journal of the Royal Statistical Society, Series A, 147, 87-99.

Examples

muscatine$cage <- muscatine$age - 12                                         
muscatine$cage2 <- muscatine$cage^2                                          
                                                                        
f1 <- numobese ~ gender                                                 
f2 <- numobese ~ gender + cage + cage2 +                                
    gender:cage + gender:cage2                                          
                                                                        
gee1 <- geeglm(formula = f1, id = id,                                   
               waves = occasion, data = muscatine, family = binomial(),      
               corstr = "independence")                                 
                                                                        
gee2 <- geeglm(formula = f2, id = id,                                   
               waves = occasion, data = muscatine, family = binomial(),      
               corstr = "independence")                                 
                                                                        
tidy(gee1)                                                              
tidy(gee2)                                                              
QIC(gee1)
QIC(gee2)