Description References Examples

In clinical trials and epidemiological studies, the association
between an exposure and the outcome of interest in a study can be estimated by
*regression coefficients*, *odds ratios* or *hazard ratios* depending
on the nature of study designs and outcome measurements. We use a general term
*effect estimate* here for any of those measurements in this document.
Based on those measurements,
we determine if a treatment is effective (or detrimental) or a factor is a risk factor.
Imbalanced distributions of other factors could bias the effect estimates, called
*confounding*. One way to assess the
confounding effect of a factor is to examine the difference in effect
estimates between models with and without a specific factor. `'chest'`

allows
users quickly calculate the changes when potential confounding factors
are sequentially added to the model in a stepwise fashion. At each step, one
variable which creates the largest change (%) of the effect estimate among the remaining
variables is added to the model. `'chest'`

returns a graph and a data frame (table) with
effect estimates (95% CI) and change (%) values. The package currently has the following main
functions: `'chest_lm'`

for linear regression, `'chest_glm'`

for logistic
regression and Poisson regression, `'chest_speedglm'`

using `'speedlm'`

as
a faster alternative of `'chest_glm'`

, `'chest_clogit'`

for matched logistic
regression, `'chest_nb'`

for negative binomial regression and `'chest_cox'`

for
Cox proportional hazards models.

Zhiqiang Wang (2007) <https://doi.org/10.1177/1536867X0700700203>

1 2 3 4 5 6 7 8 | ```
? chest_speedglm
? chest_glm
? chest_cox
? chest_clogit
? chest_lm
? chest_nb
? chest_plot
? chest_forest
``` |

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