title: Epigenesis theory: a mathematical model relating causal concepts of pathogenesis in individuals to disease patterns in populations.
abstract: A mathematical modeling approach called epigenesis theory is presented which relates three aspects of pathogenesis to the population distribution of disease. The three aspects of pathogenesis involve how two or more measured variables interact. They are 1) whether the measured variables are related to the same causal action, 2) whether there is only one pathogenic process leading to disease, and 3) whether the measured variables contribute to the same pathogenic process. Epigenesis theory defines the following multivariable relations between two disease causes: 1) "Complementary" causes contribute different causal actions to the sole pathogenic process leading to disease. They have multiplicative relations. 2) "Separate process" causes contribute different causal actions to different pathogenic processes. They have the relations of simple independent action which are slightly less than additive. 3) "Intermediate" causes contribute different causal actions to the same pathogenic process in the presence of additional pathogenic processes where at most one of them may also participate. They have relations somewhere between multiplicative and simple independent actions. 4) "Cooperative-competitive" causes share the same causal action and act within the same pathogenic process. Their relations can change from greater than multiplicative to less than simple independent action at increasing dichotomization points of the measured variables. Epigenesis theory unifies the sufficient-component causes model and the simple independent action model and exceeds either model in the range of observations it can explain. It is most useful given directly causal measured variables and specific disease outcomes, but it will assist in etiologic investigations of nonspecific outcomes in which new disease classifications are proposed. While it is less useful given surveillance-type variables such as age or sex or outcomes resulting from numerous pathogenic processes such as death, it gains utility as more causal variables are entered into an analysis and as more cut points of continuous complementary, independent, or intermediate variables are distinguished.
authors.affiliation: Dept. of Epidemiology, U. of Michigan, Ann Arbor 48109.
authors.forename: J S
authors.initials: JS
authors.lastname: Koopman
authors.forename: D L
authors.initials: DL
authors.lastname: Weed
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000453:epidemiology; D015984:Causality; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D015994:Incidence; D008875:Middle Aged; D015233:Models, Statistical; D011336:Probability; D012307:Risk Factors; D013598:Systems Theory