# Clinical Regression Analytics plus AI Reasoning

Regression analysis is a vital tool in data-driven healthcare, enabling clinicians and researchers to predict outcomes, adjust for confounders, model survival, and assess diagnostics. The increasing variety of regression techniques, from basic linear to advanced models like Cox and functional data analysis, necessitates clarity on their relationships and specific applications for continuous, categorical, repeated, or latent variables. This post categorizes regression models by clinical use, outcome type, and complexity to guide tool selection for analyzing trials, EHR data, or diagnostic studies. Organizing these methods conceptually enhances the alignment of statistical choices with research aims, improving the interpretability and impact of clinical findings towards more transparent and precise healthcare research.

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