diab_df

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

datasets

Applies the change-in-effect estimate method to assess confounding
effects in medical and epidemiological research (Greenland & Pearce (2016)
<doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model
including only the outcome and exposure variables. At each of the subsequent
steps, one variable which creates the largest change among the remaining variables
is selected. This process is repeated until all variables have been entered into
the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest'
package has functions for linear regression, logistic regression, negative
binomial regression, Cox proportional hazards model and conditional logistic
regression.

Zhiqiang Wang

chest

Change-in-Estimate Approach to Assess Confounding Effects

diab_df function

A data frame with 2372 rows and 14 variables:<dl>
 <dt>Diabetes</dt>
<dd>diabetes status 1: with diabetes 0: without diabetes</dd> <dt>Endpoint</dt>
<dd>mortality status 1: reached end point, and 0: survived</dd> <dt>Age</dt>
<dd>Age, in years</dd> <dt>Sex</dt>
<dd>sex, 1: male, 2: Female</dd> <dt>BMI</dt>
<dd>Body mass index</dd> <dt>Married</dt>
<dd>marital status 1: married, 0: not</dd> <dt>Smoke</dt>
<dd>smoking status 1: smoker, 0: non-smoker</dd> <dt>CVD</dt>
<dd>cardiovascular disease 1: yes 0: no</dd> <dt>Cancer</dt>
<dd>cancer 1: yes, 0: no</dd> <dt>Education</dt>
<dd>education 1: high, 0: low</dd> <dt>Income</dt>
<dd>income 1: high, 0: low</dd> <dt>t0</dt>
<dd>time (age) at the start of the follow-up</dd> <dt>t1</dt>
<dd>time (age) at the end of the follow-up</dd> <dt>mid</dt>
<dd>matched set id, for conditional logistic regression</dd> 
</dl>

Format

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

A data frame with 2372 rows and 14 variables:<dl>
 <dt>Diabetes</dt>
<dd>diabetes status 1: with diabetes 0: without diabetes</dd>

 <dt>Endpoint</dt>
<dd>mortality status 1: reached end point, and 0: survived</dd>

 <dt>Age</dt>
<dd>Age, in years</dd>

 <dt>Sex</dt>
<dd>sex, 1: male, 2: Female</dd>

 <dt>BMI</dt>
<dd>Body mass index</dd>

 <dt>Married</dt>
<dd>marital status 1: married, 0: not</dd>

 <dt>Smoke</dt>
<dd>smoking status 1: smoker, 0: non-smoker</dd>

 <dt>CVD</dt>
<dd>cardiovascular disease 1: yes 0: no</dd>

 <dt>Cancer</dt>
<dd>cancer 1: yes, 0: no</dd>

 <dt>Education</dt>
<dd>education 1: high, 0: low</dd>

 <dt>Income</dt>
<dd>income 1: high, 0: low</dd>

 <dt>t0</dt>
<dd>time (age) at the start of the follow-up</dd>

 <dt>t1</dt>
<dd>time (age) at the end of the follow-up</dd>

 <dt>mid</dt>
<dd>matched set id, for conditional logistic regression</dd>

 
</dl>

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

Description

Usage

Arguments

Format