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Adjustment for misclassification in studies of familial aggregation of disease using routine register data
Author(s) -
Andersen Elisabeth Wreford,
Andersen Per Kragh
Publication year - 2002
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/sim.1319
Subject(s) - disease , register (sociolinguistics) , family aggregation , computer science , regression , cluster analysis , statistics , calibration , medicine , artificial intelligence , mathematics , philosophy , linguistics
This paper discusses the misclassification that occurs when relying solely on routine register data in family studies of disease clustering. A register study of familial aggregation of schizophrenia is used as an example. The familial aggregation is studied using a regression model for the disease in the child including the disease status of the parents as a risk factor. If all the information is found in the routine registers then the disease status of the parents is only known from the time when the register started and if this information is used unquestioningly the parents who have had the disease before this time are misclassified as disease‐free. Two methods are presented to adjust for this misclassification: regression calibration and an EM‐type algorithm. These methods are used in the schizophrenia example where the large effect of having a schizophrenic mother hardly shows any signs of bias due to misclassification. The methods are also studied in simulations showing that the misclassification problem increases with the disease frequency. Copyright © 2002 John Wiley & Sons, Ltd.