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.
| Version: |
0.3.6 |
| Depends: |
R (≥ 2.20) |
| Imports: |
broom, ggplot2, survival, grid, speedglm, forestplot, MASS, tibble, dplyr |
| Suggests: |
spelling, knitr, rmarkdown |
| Published: |
2022-03-01 |
| Author: |
Zhiqiang Wang [aut, cre] |
| Maintainer: |
Zhiqiang Wang <menzies.uq at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
chest results |
Documentation:
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