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.
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
'chest_lm' for linear regression,
'chest_glm' for logistic
regression and Poisson regression,
a faster alternative of
'chest_clogit' for matched logistic
'chest_nb' for negative binomial regression and
Cox proportional hazards models.
Zhiqiang Wang (2007) <https://doi.org/10.1177/1536867X0700700203>
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