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DESIGN CONSIDERATIONS FOR ESTIMATION OF EXPOSURE EFFECTS ON DISEASE RISK, USING AGGREGATE DATA STUDIES
Author(s) -
SHEPPARD LIANNE,
PRENTICE ROSS L.,
ROSSING MARY ANNE
Publication year - 1996
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19960915)15:17<1849::aid-sim396>3.0.co;2-4
Subject(s) - covariate , aggregate (composite) , statistics , econometrics , aggregate data , population , sample size determination , estimation , random effects model , relative risk , demography , computer science , mathematics , medicine , meta analysis , environmental health , economics , confidence interval , materials science , management , sociology , composite material
Previously we proposed an aggregate data study design for estimation of exposure effects from population‐based disease rates and covariate data from risk factor surveys in each population group. A basic relative rate model specified for individuals is aggregated to produce a random effects relative rate model for the disease rates. Relative rate parameter estimates from aggregate data studies target the same parameters as individual‐level studies but use between‐group information in the data. We distinguish aggregate data studies from ecologic studies. Considerations in the design of aggregate studies are motivated by the need to gain clearer understanding of the role of diet in cancer aetiology. Simulation studies show that increasing the number of populations included in an aggregate data study from about 20 to 30–40 gives greater improvement in power than corresponding increases in the size of the survey sample in each population over an initial size of 100 individuals.