chest_cox

<code>'chest_cox'</code> is used to assess confounding effects
using Proportional Hazards Regression Model (<code>'coxph'</code> from 'survival' package).
It presents the effect estimates (such as hazard ratios) for the association between exposure and outcome variables by adding other variables (potential confounders) to the model sequentially.
The order of variables to be added is based on the magnitudes of the changes in effect estimates.

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

chest_cox function

<dl><dt>crude</dt>
<dd>An object of formula for initial model, generally crude model.
However, any other variables can also be included here as the initial model.</dd>
<dt>xlist</dt>
<dd>A vector of characters with variable names of potential confounders.</dd>
<dt>data</dt>
<dd>Data frame.</dd>
<dt>na_omit</dt>
<dd>Remove all missing values, default: 'na_omit = TRUE'.</dd>
<dt>plus</dt>
<dd>Change the <code>+</code> sign before variable names.</dd>
<dt>indicate</dt>
<dd>indicate the progress.</dd>
<dt>...</dt>
<dd>Further optional arguments for forestplot.</dd></dl>

Arguments

Assessing confounding effects using Cox Proportional Hazards regression models — chest_cox

<dl>

<dt>crude</dt>
<dd>An object of formula for initial model, generally crude model.
However, any other variables can also be included here as the initial model.</dd>


<dt>xlist</dt>
<dd>A vector of characters with variable names of potential confounders.</dd>


<dt>data</dt>
<dd>Data frame.</dd>


<dt>na_omit</dt>
<dd>Remove all missing values, default: 'na_omit = TRUE'.</dd>


<dt>plus</dt>
<dd>Change the <code>+</code> sign before variable names.</dd>


<dt>indicate</dt>
<dd>indicate the progress.</dd>


<dt>...</dt>
<dd>Further optional arguments for forestplot.</dd>

</dl>

chest_cox: Assessing confounding effects using Cox Proportional Hazards regression models

Description

Usage

Value

Arguments

See Also

Examples