ABSTRACT
BACKGROUND/AIMS: Sibling recurrence risk (SRR) is a measure of familial aggregation of a disease and is often used in family-based studies in genetic epidemiology to indicate the existence of possible genes conferring susceptibility of disease. Estimating SRR requires information about the disease status of sibships of families with two or more siblings where at least one is affected. Since family-based studies are not usually random samples, estimates of SRR derived from these studies may be biased. Network sampling used in survey research offers a way to ascertain the disease status of sibships from interviewed individuals in household survey samples, in order to obtain (approximately) unbiased estimators of SRR and its related SRR ratio (SRR divided by the prevalence of disease). METHODS: Two methods of ascertaining sibships of affected families are considered: in one method the siblings' affected status is reported by an individual, regardless of the individual's affected status, and in the other method only affected individuals can report their siblings' affected status. Network estimators of SRR and SRR ratio and estimators of their standard errors are provided. RESULTS: Reported diabetes for siblings from the 1976 National Health Interview is used to illustrate the estimation methods. The SRR ratio for diabetes among living siblings was 5.79% [relative standard error (RSE) = 5.12%], and for living or deceased siblings, it was 7.66% (RSE = 3.76%). CONCLUSIONS: Network sampling estimators can provide population estimates of SRR and SRR ratio for diseases such as diabetes.
Subject(s)
Biostatistics/methods , Genetic Predisposition to Disease , Sampling Studies , Adolescent , Adult , Aged , Child , Data Collection/methods , Diabetes Mellitus/epidemiology , Diabetes Mellitus/genetics , Female , Genetic Predisposition to Disease/epidemiology , Genetic Predisposition to Disease/genetics , Humans , Male , Middle Aged , Risk Factors , Self Report , Young AdultABSTRACT
The linked population/establishment survey (LS) of health services utilization is a two-phase sample survey that links the sample designs of the population sample survey (PS) and the health-care provider establishment sample survey (ES) of health services utilization. In Phase I, household respondents in the PS identify their health-care providers during a specified calendar period. In Phase II, health-care providers identified in Phase I report the variables of interest for all or a sample of their transactions with all households during the same calendar period. The LS has been proposed as a potential design alternative to the PS whenever the health-care transactions of interest are hard to find or enumerate in household surveys and as a potential design alternative to the ES whenever it is infeasible or expensive to construct or maintain complete sampling provider frames that list all health-care providers with good measures of provider size. Suppose that the non-sampling errors are ignorable, how do the LS, PS and ES sampling errors compare? This paper addresses that question by summarizing and extending recent research findings that compare expressions of the sampling variance of (1) the LS and PS of equivalent household sample size and (2) the LS and the ES of equivalent expected health-care provider and transaction sample sizes. The paper identifies the parameters contributing to the precision differences and assesses the conditions that favour the LS or one or the other surveys. Published in 2007 by John Wiley & Sons, Ltd.
