A bayesian network for generating categorical synthetic data for assessing cardiovascular risk. Variable types are as follows:
data(cvd_bayes_net)
A Bayesian Network of class CPTgrain using the gRain package for representing the data.
age Patient Age Category. Age category of patient. string of age ranges.
htn Does patient have hypertension? Threshold systolic blood pressure is 150. Y/N
treat Is patient receiving hypertension treatment? Y/N
smoking Y/N based on threshold of pack years: 10
race Race based on self-defined question in survey. AmInd (american indian),
Asian/PI (asian/pacific islander), Black/AfAm (Black/African American), White
gender Gender of patient. Male, Female, NA means that patient did not want gender recorded.
t2d Whether patient has Type 2 diabetes. Y/N.
bmi Body Mass Index of Patient. kg/m^2
sbp Systolic Blood Pressure in mm/Hg
rs10757278 SNP data. Associated with race and total cholesterol.
rs1333049 SNP data. Associated with race and total cholesterol. Always co-occurs with rs10757278.
rs4665058 SNP data. Associated with race and total cholesterol.
rs8055236 SNP data. Variant is associated with increased risk.
Note that not all covariates (including cardiovascular risk) were generated by the dataset.
Further details about how to generate the entire dataset from this network can be found from
generate_data_from_network in the vignettes folder.
library(gRain) data(cvd_bayes_net) #generate categorical data for 1000 patients testData <- simulate(cvd_bayes_net, nsim =1000) summary(testData)#> age htn treat smoking htn race bmi #> 0-20 : 77 N:655 N:797 N:855 N : 0 AmInd : 4 15-18:163 #> 20-40:319 Y:345 Y:203 Y:145 Y : 0 Asian/PI :169 18-25:693 #> 40-55:246 NA's:1000 Black/AfAm: 54 25-31: 92 #> 55-70:231 White :773 31+ : 52 #> 70-90:127 #> #> t2d genotype tchol gender #> N:926 1111: 13 <160 :278 M:451 #> Y: 74 1110: 16 160-199:459 F:549 #> 1100:189 200-239:147 #> 0010: 48 240+ :116 #> 0001:349 #> 0000:385