ROCit (version 2.1.1)

Diabetes: Diabetes Data

Description

These data are courtesy of Dr John Schorling, Department of Medicine, University of Virginia School of Medicine.

The data contains information on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. According to Dr John Hong, Diabetes Mellitus Type II (adult onset diabetes) is associated most strongly with obesity. The waist/hip ratio may be a predictor in diabetes and heart disease. DM II is also associated with hypertension - they may both be part of "Syndrome X". The 403 subjects were the ones who were actually screened for diabetes. Glycosylated hemoglobin > 7.0 is usually taken as a positive diagnosis of diabetes.

Usage

Diabetes

Format

A data frame with 403 rows and 22 variables (See "Note"):

id

Subject id

chol

Total cholesterol

stab.glu

Stabilized glucose

hdl

High density lipoprotein

ratio

Cholesterol/hdl ratio

glyhb

Glycosylated hemoglobin

location

A factor with levels Buckingham and Louisa

age

Age (years)

gender

Gender, male or female

height

Height (inches)

weight

Weight (pounds)

frame

A factor with levels small, medium and large

bp.1s

First systolic blood pressure

bp.1d

First diastolic blood pressure

bp.2s

Second systolic blood pressure

bp.2d

Second diastolic blood pressure

waist

Waist (inches)

hip

Hip (inches)

time.ppn

Postprandial time when labs were drawn in minutes

bmi

Body mass index

dtest

An indicator whether glyhb is greater than 7 or not

whr

Waist to hip ratio

References

Willems, James P., J. Terry Saunders, Dawn E. Hunt, and John B. Schorling. "Prevalence of coronary heart disease risk factors among rural blacks: a community-based study." Southern medical journal 90, no. 8 (1997): 814-820.

Schorling, John B., Julienne Roach, Marjorie Siegel, Natalie Baturka, Dawn E. Hunt, Thomas M. Guterbock, and Herbert L. Stewart. "A trial of church-based smoking cessation interventions for rural African Americans." Preventive Medicine 26, no. 1 (1997): 92-101.

Examples

data("Diabetes")
plot(Diabetes$hdl~Diabetes$weight, pch = 16,
       col =ifelse(Diabetes$gender=="male",1,2))
#------------------------------------------
## density plot
femaleBMI <- density(subset(Diabetes, gender == "female")$bmi, na.rm = TRUE)
maleBMI <- density(subset(Diabetes, gender == "male")$bmi, na.rm = TRUE)
## -------
plot(NULL, ylim = c(0,0.08), xlim = c(10,60),
     xlab = "BMI", ylab = "Density", main = "")
grid(col = 1)
polygon(maleBMI, col = rgb(0,0,1,0.2), border = 4)
polygon(femaleBMI, col = rgb(1,0,0,0.2), border = 2)
abline(h = 0)
legend("topright", c("Male", "Female"), pch = 15,
       col = c(rgb(0,0,1,0.2), rgb(1,0,0,0.2)), bty = "n")
#------------------------------------------
logistic.model <- glm(as.factor(dtest)~chol+age+bmi,
                      data = Diabetes,family = "binomial")
summary(logistic.model)
#------------------------------------------
class <- logistic.model$y
score <- logistic.model$fitted.values
rocit_object <- rocit(score = score, class = class)
summary(rocit_object)
plot(rocit_object)