get_quality_legend

<p>Returns a legend explaining the quality indicators used in formatted results.</p>

Calculates risk differences (or prevalence differences for
cross-sectional data) using generalized linear models with automatic
link function selection. Provides robust model fitting with fallback
methods, support for stratification and adjustment variables, inverse
probability of treatment weighting (IPTW) for causal inference, and
publication-ready output formatting. Handles model convergence issues
gracefully and provides confidence intervals using multiple approaches.
Methods are based on approaches described in Mark W. Donoghoe and
Ian C. Marschner (2018) "logbin: An R Package for Relative Risk
Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09>
for robust GLM fitting, Peter C. Austin (2011) "An Introduction to
Propensity Score Methods for Reducing the Effects of Confounding in
Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods,
and standard epidemiological methods for risk difference estimation as
described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash
(2008, ISBN:9780781755641) "Modern Epidemiology".

John D. Murphy

get_quality_legend: Get Quality Legend for Risk Difference Results

Description

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Examples