A multiplicative statistical model predicts the size distribution of unruptured intracranial aneurysms.
A statistical model for characterizing the erratic nature of aneurysm evolution is developed and tested. This model is based upon a multiplicative hypothesis, whereby it is theorized that the progressive changes in the size of a given aneurysm are determined by random multipliers. Such a model would predict that within a large population of aneurysms, a lognormal histogram for aneurysm sizes would occur (i.e. the logarithms of aneurysm size would have a normal distribution). When applied to previously published clinical data of unruptured aneurysms by Crompton (1966) and McCormick et al. (1970), the model is found to adequately describe both sets of data. The methods introduced in this paper illustrate the utility of incorporating statistical and clinical insights with fundamental biometry for studying the complex phenomena of aneurysm growth and rupture.
Kailasnath
P
P
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520-8042, USA.
Chaloupka
J C
JC
Dickey
P S
PS
D017542:Aneurysm, Ruptured / Q000473:pathology; D006801:Humans; D002532:Intracranial Aneurysm / Q000473:pathology*; D016015:Logistic Models; D015233:Models, Statistical