chest - Change-in-Estimate Approach to Assess Confounding Effects
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.
