Adjustment for misclassi cation in studies of familial aggregation of disease using routine register data

Elisabeth Wreford Andersen, Per Kragh Andersen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This paper discusses the misclassication 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 misclassied as disease-free. Two methods are presented to adjust for this misclassication: regression calibration and an EM-type algorithm. These methods are used in the schizophrenia example where the large eect of having a schizophrenic mother hardly shows any signs of bias due to misclassication. The methods are also studied in simulations showing that the misclassication problem increases with the disease frequency. Copyright © 2002 John Wiley & Sons, Ltd.
Original languageEnglish
JournalStatistics in Medicine
Volume21
Issue number23
Pages (from-to)3596-3607
Number of pages12
ISSN0277-6715
DOIs
Publication statusPublished - 2002
Externally publishedYes

Keywords

  • disease register
  • EM-algorithm
  • familial aggregation
  • misclassification
  • nested case-control study
  • regression calibration

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