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What is another word for Proportional Hazards Models?

Pronunciation: [pɹəpˈɔːʃənə͡l hˈazədz mˈɒdə͡lz] (IPA)

Proportional hazards models, also known as Cox regression models, are statistical tools used to analyze survival data in research and clinical studies. These models allow researchers to examine the relationship between explanatory variables and the hazard rate, representing the risk of an event occurring over time. Although "proportional hazards models" is the commonly used term, there are several synonymous phrases used to describe these models. Some alternatives include Cox proportional hazards models and Cox models. These names all refer to the same statistical technique and are often used interchangeably. Researchers should be familiar with these synonyms to effectively navigate the literature and understand the methodology behind survival analysis.
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What are the opposite words for Proportional Hazards Models?

Antonyms for Proportional Hazards Models could include non-proportional hazards models, non-parametric survival models, and time-dependent hazards models. Non-proportional hazards models do not rely on the assumption that risk factors have a constant effect over time, and instead allow for changes in risk levels over time. Non-parametric survival models do not require any assumptions about the underlying distribution of hazards, and can be used when the shape of the hazard function is unknown. Time-dependent hazards models allow for the inclusion of time-varying covariates in the modeling process, and can be used to explore how different risk factors may impact survival over time.

What are the antonyms for Proportional hazards models?

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