[R] Model Comparision for case control studies in R

[Ebert,Timothy Aaron]

Disease status is missing from the sample data.
Are age, disease, smoking, and/or hypertension correlated in any way or are they independent (correlation=0)?
Are the correlations large enough to adversely influence your model?
Tim

-----Original Message-----
From: R-help <r-help-bounces using r-project.org> On Behalf Of anteneh asmare
Sent: Wednesday, June 15, 2022 7:29 AM
To: r-help using r-project.org
Subject: [R] Model Comparision for case control studies in R

[External Email]

y<-c(0,1,1,0,0,1,0,0,1,1,1,0,1,1,1,0,0,0,0,1)
age<-c(45,23,56,67,23,23,28,56,45,47,36,37,33,35,38,39,43,28,39,41)
smoking<-c(0,1,1,1,0,0,0,0,0,1,1,0,0,1,0,1,1,1,0,1)
hypertension<-c(1,1,0,1,0,1,0,1,1,0,1,1,1,1,1,1,0,0,1,0)
data<-data.frame(y,age,smoking,hypertension)
data
model<-glm(y~age+factor(smoking)+factor(hypertension), data, family = binomial(link = "logit"),na.action = na.omit)
summary(model)
from above sample data I want to study a case-control study on male individuals with my response variable y, disease status (1=Case,
0=Control) with covariates age, smoking status(1=Yes, 0=No)  and hypertension, hypertensive (1=Yes, 0=No). I want to fit the model to predict the disease status using at least two different methods. And to make model comparisons. I think logistic regression will be the best fit for this case control study. Do we have other options in addition to logistic regression? My objective is to fit the model to predict the disease status using at least two different methods.
Kind regards,
Hana

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