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

In chest: Change-in-Estimate Approach to Assess Confounding Effects

Change-in-Estimate Approach to Assess Confounding Effects

Description

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_clogit' for matched logistic regression, 'chest_nb' for negative binomial regression and 'chest_cox' for Cox proportional hazards models.

References

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

Examples

? chest_glm
? chest_cox
? chest_clogit
? chest_lm
? chest_nb
? chest_plot
? chest_forest

chest documentation built on March 31, 2023, 10:02 p.m.