Literature DB >> 17878173

The impact of truncation and missing family links in population-based registers on familial risk estimates.

Monica Leu1, Kamila Czene, Marie Reilly.   

Abstract

Family history information is often incomplete in population-based disease registers because of truncation and/or missing family links. In this study, the authors simulated complete populations of related individuals with realistic age, family structure, and incidence rates. After mimicking the realities of register-based data, such as left truncation of family history and missing family links due to death, the authors explored recovery of familial association parameters from standard epidemiologic models. Truncation of family history produced almost no bias for a familial risk of 2 and 50 years of follow-up, but it had a dramatic impact when the familial risk was 10. The age distribution of disease and the magnitude of background incidence rates also affected family history loss and thus the magnitude of bias. One can safeguard against bias by starting follow-up later, with the number of registration years to be ignored in the analysis depending on the value of familial risk. The missing familial links due to death had no effect, except when there was differential mortality for cases with and without a family history of disease. In summary, truncation, and to a lesser extent missing family links, induces bias in familial risk estimates from population-based registers.

Mesh:

Year:  2007        PMID: 17878173     DOI: 10.1093/aje/kwm234

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  3 in total

Review 1.  Familial aggregation of glioma: a pooled analysis.

Authors:  Michael E Scheurer; Carol J Etzel; Mei Liu; Jill Barnholtz-Sloan; Fredrik Wiklund; Björn Tavelin; Margaret R Wrensch; Beatrice S Melin; Melissa L Bondy
Journal:  Am J Epidemiol       Date:  2010-09-21       Impact factor: 4.897

2.  Family history of colorectal cancer and survival: a Swedish population-based study.

Authors:  F Pesola; S Eloranta; A Martling; D Saraste; K E Smedby
Journal:  J Intern Med       Date:  2020-03-03       Impact factor: 8.989

3.  A constant risk for familial breast cancer? A population-based family study.

Authors:  Kamila Czene; Marie Reilly; Per Hall; Mikael Hartman
Journal:  Breast Cancer Res       Date:  2009-05-20       Impact factor: 6.466

  3 in total

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