title: Regression methods for assessing familial aggregation of disease.
abstract: This paper reviews methods for assessing familial aggregation of disease based on simple logistic regression models. Studies are based on a case-control sampling design, where the disease status of the first degree relatives of both cases and controls are obtained. Both 'proband predictive' and 'family predictive' models are discussed, and an example is given using a case-control sample from a lung cancer study in non-smokers. The methods are extended to characterize co-aggregation of two disorders, that is, presence of one disorder in the proband increases the risk of a second disorder in the relative. An example involving eating disorders and depression is given.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. laird@hsph.harvard.edu
authors.forename: Nan M
authors.initials: NM
authors.lastname: Laird
authors.forename: Karen T
authors.initials: KT
authors.lastname: Cuenco
mesh_terms: D016022:Case-Control Studies; D015897:Comorbidity; D016001:Confidence Intervals; D001068:Feeding and Eating Disorders / Q000235:genetics; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000209:etiology / Q000235:genetics; D008297:Male; D019964:Mood Disorders / Q000235:genetics; D016017:Odds Ratio; D014028:Tobacco Smoke Pollution / Q000009:adverse effects
chemical_list: D014028:Tobacco Smoke Pollution