ADONGO'S CENTRAL COUNT THEORY

CENTRAL COUNT THEORY

My Central Point Theory has great influenced in almost all area of human endeavor. One of the aspect I am much Concern today is to estimate a rate ratio associated with a predictor or exposures x₁.

The dependent variables x₂, x₃, x₄, …., x_r are count of occurrence of interest. The relationship between the single predictor or exposure x₁ and response variable x₂, x₃, x₄, …., x_r is;

X₂=e^α_*e^β1x1

X₃=e^α_*e^β1x1_*e^β2x2

X₄=e^α_*e^β1x1_*eβ^2x2_*e^β3x3

…………………………………………………….

X_r= e^α_*e^β1x1_*e^β2x2_*e^β3x3_*…_*e^βi-1xi-1_*…_*e^βr-1xr-1

Where α and β_i-1 are constants. In this theory we should be able to hypothesize the exact count of occurrence of interests x₂, x₃, x₄,……, x_r, provided the single predictor x1 is known.

Considered a cohort of subjects, some non-smokers was observed for several years. The numbers of cases of cancer of the lung diagnosed among smoking were also obtained from each individual, for each category the person-years of observation. Using the Central Count Theory, we can investigate to address the question of the relative risk of smoking.

Cigarette Per Day	No. Years Smoking	Person Years	Cases
20	15	5683	0
20	25	5483	1
27	15	3042	0
27	25	4290	4
40	15	670	0
40	25	1482	6

EFERENCE

*Adongo Ayine William(Me), Diary(or Weblog), "2012/2013 Academic Year Project"