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Measuring familial aggregation by using odds-ratio regression models. 1991

K Y Liang, and T H Beaty
Department of Biostatistics, Johns Hopkins University, Baltimore 21205.

Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.

UI MeSH Term Description Entries
D007223 Infant A child between 1 and 23 months of age. Infants
D008113 Liver Neoplasms Tumors or cancer of the LIVER. Cancer of Liver,Hepatic Cancer,Liver Cancer,Cancer of the Liver,Cancer, Hepatocellular,Hepatic Neoplasms,Hepatocellular Cancer,Neoplasms, Hepatic,Neoplasms, Liver,Cancer, Hepatic,Cancer, Liver,Cancers, Hepatic,Cancers, Hepatocellular,Cancers, Liver,Hepatic Cancers,Hepatic Neoplasm,Hepatocellular Cancers,Liver Cancers,Liver Neoplasm,Neoplasm, Hepatic,Neoplasm, Liver
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D008957 Models, Genetic Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment. Genetic Models,Genetic Model,Model, Genetic
D012044 Regression Analysis Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable. Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D002277 Carcinoma A malignant neoplasm made up of epithelial cells tending to infiltrate the surrounding tissues and give rise to metastases. It is a histological type of neoplasm and not a synonym for "cancer." Carcinoma, Anaplastic,Carcinoma, Spindle-Cell,Carcinoma, Undifferentiated,Carcinomatosis,Epithelial Neoplasms, Malignant,Epithelioma,Epithelial Tumors, Malignant,Malignant Epithelial Neoplasms,Neoplasms, Malignant Epithelial,Anaplastic Carcinoma,Anaplastic Carcinomas,Carcinoma, Spindle Cell,Carcinomas,Carcinomatoses,Epithelial Neoplasm, Malignant,Epithelial Tumor, Malignant,Epitheliomas,Malignant Epithelial Neoplasm,Malignant Epithelial Tumor,Malignant Epithelial Tumors,Neoplasm, Malignant Epithelial,Spindle-Cell Carcinoma,Spindle-Cell Carcinomas,Tumor, Malignant Epithelial,Undifferentiated Carcinoma,Undifferentiated Carcinomas
D002648 Child A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL. Children
D002675 Child, Preschool A child between the ages of 2 and 5. Children, Preschool,Preschool Child,Preschool Children
D002681 China A country spanning from central Asia to the Pacific Ocean. Inner Mongolia,Manchuria,People's Republic of China,Sinkiang,Mainland China

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