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A method to detect excess risk of disease in structured data: Cancer in relatives of retinoblastoma patients

A method to detect excess risk of disease in structured data: Cancer in relatives of... It is often of interest to know whether there is increased occurrence of a trait in a pedigree or other structured set of epidemiological data. In answering such questions most current methods use aggregate measures, such as relative risk, that may not relate the outcome for each individual to that individual's risk. In this paper we present a simple method, and its computational algorithm, to overcome this limitation. This new method also permits one to identify high‐risk families or subsets of a collection of data, which is not always possible using other approaches. In a study of cancer risk among relatives of retinoblastoma patients, by applying this new method it was found that 11 of 33 families each obtained through a unilateral retinoblastoma patient are at statistically high risk of cancer at all sites combined, while there are 15 of 47 such families obtained through a bilaterally affected proband. These results are unlikely to have occured by chance, indicating an overall excess risk in the ancestors of these retinoblastoma cases. The proposed test procedure does not specify the cause of elevated risk; however, a method is proposed that provides some indication regarding possible causal mechanisms under some circumstances. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Genetic Epidemiology Wiley

A method to detect excess risk of disease in structured data: Cancer in relatives of retinoblastoma patients

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References (13)

Publisher
Wiley
Copyright
Copyright © 1984 Wiley‐Liss, Inc., A Wiley Company
ISSN
0741-0395
eISSN
1098-2272
DOI
10.1002/gepi.1370010303
pmid
6544239
Publisher site
See Article on Publisher Site

Abstract

It is often of interest to know whether there is increased occurrence of a trait in a pedigree or other structured set of epidemiological data. In answering such questions most current methods use aggregate measures, such as relative risk, that may not relate the outcome for each individual to that individual's risk. In this paper we present a simple method, and its computational algorithm, to overcome this limitation. This new method also permits one to identify high‐risk families or subsets of a collection of data, which is not always possible using other approaches. In a study of cancer risk among relatives of retinoblastoma patients, by applying this new method it was found that 11 of 33 families each obtained through a unilateral retinoblastoma patient are at statistically high risk of cancer at all sites combined, while there are 15 of 47 such families obtained through a bilaterally affected proband. These results are unlikely to have occured by chance, indicating an overall excess risk in the ancestors of these retinoblastoma cases. The proposed test procedure does not specify the cause of elevated risk; however, a method is proposed that provides some indication regarding possible causal mechanisms under some circumstances.

Journal

Genetic Epidemiology – Wiley

Published: Jan 1, 1984

Keywords: familial aggregation; retinoblastoma; cancer risk; excess risk

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