Correcting for ascertainment bias in the COGA data set
作者： Anthony G. Comuzzie , Jeff T. Williams
关键词: Threshold model 、 Protocol (science) 、 Data set 、 Econometrics 、 Population 、 Statistics 、 Linkage (software) 、 Sampling bias 、 Computer science
摘要: The effects of several corrections for ascertainment bias on a linkage result in the COGA data are examined. A correction that models complete protocol markedly reduces evidence linkage. Different partial increase signal and recover good estimates population trait prevalence, but do not represent adequately complexity scheme. Evidently present size set cannot support bias, an effective can should be implemented.
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