hdnom (version 5.0)

smart: Imputed SMART Study Data

Description

Imputed SMART study data (no missing values).

Usage

data(smart)

Format

A numeric matrix with 3873 samples, and 29 variables (27 variables + time variable + event variable):

• Demographics

  • SEX - gender

  • AGE - age in years

• Classical risk factors

  • SMOKING - smoking (never, former, current)

  • PACKYRS - in years

  • ALCOHOL - alcohol use (never, former, current)

  • BMI - Body mass index, in kg/m^2

  • DIABETES

• Blood pressure

  • SYSTH - Systolic, by hand, in mm Hg

  • SYSTBP - Systolic, automatic, in mm Hg

  • DIASTH - Diastolic, by hand, in mm Hg

  • DIASTBP - Diastolic, automatic, in mm Hg

• Lipid levels

  • CHOL - Total cholesterol, in mmol/L

  • HDL - High-density lipoprotein cholesterol, in mmol/L

  • LDL - Low-density lipoprotein cholesterol, in mmol/L

  • TRIG - Triglycerides, in mmol/L

• Previous symptomatic atherosclerosis

  • CEREBRAL - Cerebral

  • CARDIAC - Coronary

  • PERIPH - Peripheral

  • AAA - Abdominal aortic aneurysm

• Markers of atherosclerosis

  • HOMOC - Homocysteine, in \(\mu\)mol/L

  • GLUT - Glutamine, in \(\mu\)mol/L

  • CREAT - Creatinine clearance, in mL/min

  • ALBUMIN - Albumin (no, micro, macro)

  • IMT - Intima media thickness, in mm

  • STENOSIS - Carotid artery stenosis > 50%

References

Steyerberg, E. W. (2008). Clinical prediction models: a practical approach to development, validation, and updating. Springer Science & Business Media.

Examples

# NOT RUN {
data(smart)
dim(smart)

# # Code for generating the smart dataset by imputing the smarto data
# library("mice")
# library("Hmisc")
# data(smarto)
# set.seed(1000)
# smarti = aregImpute(~ I(TEVENT) + EVENT + SEX + I(AGE) +
#   DIABETES + CEREBRAL + CARDIAC + AAA + PERIPH +
#   STENOSIS + SYSTBP + DIASTBP + SYSTH + DIASTH +
#   I(LENGTH) + I(WEIGHT) + I(BMI) +
#   I(CHOL) + I(HDL) + I(LDL) + I(TRIG) + I(HOMOC) + I(GLUT) + I(CREAT) + I(IMT) +
#   as.factor(ALBUMIN) + as.factor(SMOKING) + I(PACKYRS) + as.factor(ALCOHOL),
#   n.impute = 10, data = smarto)
# imputed = impute.transcan(smarti, imputation = 1, data = smarto,
#   list.out = TRUE, pr = FALSE, check = FALSE)
# smart = smarto
# smart[names(imputed)] = imputed
# str(smart)
# }