Survival Model Family

A Survival Model Family is a statistical model family that defines a set of survival functions (to represent a survival process).

• Context:
  • It can be an input to a Survival Analysis Algorithm.
  • It can include a Hazard Function, [math]\displaystyle{ \lambda_0(t) }[/math] (describing how the hazard/risk changes over time at baseline levels of covariates).
  • It can include an Effects Parameter (escribing how the hazard varies in response to explanatory covariates).
• See: Failure Model, Proportional Hazards Model.

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References

2013

• http://en.wikipedia.org/wiki/Survival_analysis#Distributions_used_in_survival_analysis
  • Exponential distribution.
  • Weibull distribution.
  • Exponential-logarithmic distribution.
• http://en.wikipedia.org/wiki/Proportional_hazards_models#Introduction
  • Survival models can be viewed as consisting of two parts: the underlying hazard function, often denoted [math]\displaystyle{ \lambda_0(t) }[/math], describing how the hazard (risk) changes over time at baseline levels of covariates; and the effect parameters, describing how the hazard varies in response to explanatory covariates. A typical medical example would include covariates such as treatment assignment, as well as patient characteristics such as age, gender, and the presence of other diseases in order to reduce variability and/or control for confounding.

2011

• (Hosmer Jr. et al., 2011) ⇒ David W. Hosmer Jr, Stanley Lemeshow, and Susanne May. (2011). “Applied Survival Analysis: regression modeling of time to event data, 2nd edition." Wiley-Interscience, ISBN 1118211588

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