diab_df: Example data: Health outcomes of 2372 adults with and without...

In allestimates: Effect Estimates from All Models

Example data: Health outcomes of 2372 adults with and without diabetes

Description

A data frame with 2372 rows and 14 variables with diabetes status diabetes and mortality status endpoint. For the purpose of demonstrate, assume that we are interested in the association between diabetes and endpoint. Other variables are considered as possible confounding factors. The purposes of this dataset is to illustrate those functions in chest and allestimates packages only. Therefore, we assume it is a cohort design for Cox Proportional Hazard regression, and a case-control design for logistic regression.

Usage

diab_df

Format

A data frame with 2372 rows and 14 variables:

Diabetes

diabetes status 1: with diabetes 0: without diabetes

Endpoint

mortality status 1: reached end point, and 0: survived

Age

Age, in years

Sex

sex, 1: male, 2: Female

BMI

Body mass index

Married

marital status 1: married, 0: not

Smoke

smoking status 1: smoker, 0: non-smoker

CVD

cardiovascular disease 1: yes 0: no

Cancer

cancer 1: yes, 0: no

Education

education 1: high, 0: low

Income

income 1: high, 0: low

t0

time (age) at the start of the follow-up

t1

time (age) at the end of the follow-up

mid

matched set id, for conditional logistic regression

allestimates documentation built on March 31, 2023, 5:28 p.m.