Derivation of the linear-logistic model and Cox's proportional hazard model from a canonical system description.
The linear-logistic regression model and Cox's proportional hazard model are widely used in epidemiology. Their successful application leaves no doubt that they are accurate reflections of observed disease processes and their associated risks or incidence rates. In spite of their prominence, it is not a priori evident why these models work. This article presents a derivation of the two models from the framework of canonical modeling. It begins with a general description of the dynamics between risk sources and disease development, formulates this description in the canonical representation of an S-system, and shows how the linear-logistic model and Cox's proportional hazard model follow naturally from this representation. The article interprets the model parameters in terms of epidemiological concepts as well as in terms of general systems theory and explains the assumptions and limitations generally accepted in the application of these epidemiological models.
Voit
E O
EO
Department of Biometry and Epidemiology, Medical University of South Carolina, Charleston 29425-2503, USA.
Knapp
R G
RG
D003141:Communicable Diseases / Q000453:epidemiology; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D006801:Humans; D007938:Leukemia / Q000453:epidemiology; D016014:Linear Models; D016015:Logistic Models; D016017:Odds Ratio; D016016:Proportional Hazards Models; D012307:Risk Factors