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What is another word for Proportional Hazard Models?

Pronunciation: [pɹəpˈɔːʃənə͡l hˈazəd mˈɒdə͡lz] (IPA)

Proportional Hazard Models, also known as Cox proportional hazards models, are statistical models frequently used in survival analysis. These models allow researchers to analyze the time-to-event data, such as the time until death, occurrence of disease, or any other significant event. While the term "Proportional Hazard Models" predominantly describes this specific type of model, there are alternative terms that can be used synonymously. These include Cox models, proportional hazards regression models, or even simply survival analysis models. Despite the multiple terms, they all encompass the same statistical methodology designed to understand the relationship between predictors and the time until an event occurs, making them invaluable tools in numerous fields of research.
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What are the opposite words for Proportional Hazard Models?

The antonyms for Proportional Hazard Models may include Non-Proportional Hazard Models or Nonlinear Hazard Models. The Proportional Hazard Models are statistical models that study the relationship between time and the likelihood of events occurring. In contrast, Non-Proportional Hazard Models do not hold constant the hazard rates of different groups being compared. Instead, they allow the hazard rates to vary over time or based on covariates. Nonlinear Hazard Models, on the other hand, assume that the relationship between time and the hazard is not linear. Instead, the hazard may increase or decrease at different rates over time. These antonyms represent different approaches to studying the factors that affect the likelihood of events occurring over time.

What are the antonyms for Proportional hazard models?

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