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Modeling “Catalyst’s” Effect on (Health) Incidence Rate

Ramalingam Shanmugam

Abstract


A potentially interesting idea with respect to an identification of a latent factor influencing the occurrence of a disease is presented. The presence of a latent factor is then analyzed by means of count models which are generalized binomial, Poisson, or inverse binomial distributions. One parameter in these distributions portrays the incidence rate of a disease, and the other parameter signifies an impact of the latent factor on the incidence rate. This interesting and promising idea is illustrated with numerical data sets. The technique that is developed in this article could be an alternate to proportional odds model. Data on covariates are needed to practice proportional odds model whereas the technique of this article does not require any covariate data to identify and illustrate the impact of a latent factor.

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