In an era where healthcare decisions are increasingly guided by data, regression analysis has emerged as one of the most powerful tools for clinicians and researchers alike. Whether it’s predicting outcomes, adjusting for confounders, modeling survival time, or understanding diagnostic performance, regression models provide the statistical backbone for translating raw data into clinical insight.
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Yet, with the proliferation of techniques—from simple linear regression to more advanced models like Cox regression, Poisson models, or functional data analysis—it’s easy to lose sight of how these tools relate, differ, and complement one another. Each model serves a unique purpose, whether dealing with continuous measurements, categorical outcomes, repeated observations, or latent variables.
This post presents a structured approach to categorizing regression models based on clinical use cases, outcome types, and methodological complexity. The aim is to offer a practical reference point for selecting appropriate tools, whether you're analyzing a randomized trial, exploring large-scale EHR data, or designing a diagnostic accuracy study.
By organizing these methods into conceptual groups—such as continuous outcome regressions, time-to-event models, confirmatory analyses, and latent variable approaches—we can better align statistical choices with research objectives and improve the interpretability and impact of clinical findings.
Understanding the landscape of regression models isn’t just a statistical exercise—it’s a step toward more transparent, precise, and actionable healthcare research.
Primary Biliary Cirrhosis Data on Cox Proportional Hazard Model