IJE Advance Access published online on October 13, 2009
International Journal of Epidemiology, doi:10.1093/ije/dyp304
Bias correction of estimates of familial risk from population-based cohort studies
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
* Corresponding author. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 17177, Sweden. E-mail: marie.reilly{at}ki.se
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Abstract |
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Background In addition to guiding molecular epidemiology investigations, estimates of the increased risk of disease in relatives of affected persons are also important for screening and counselling decisions. Since precise estimation of such familial risks (FRs) requires large sample sizes, many of the estimates in common use have been obtained from historical electronic records of disease in entire populations, where the relatives of affected and unaffected persons are compared. These estimates may be biased due to failure to identify relatives as affected if they are diagnosed before the start-up date of disease registration.
Methods This article presents a method for correcting the bias in FR estimates from such misclassification of family history, using a simple formula that depends on the prevalence and sensitivity of the observed family history. The sensitivity is estimated by using the R package poplab to create realistic populations of related individuals and then imposing the start-up effect of disease registration.
Results For a range of FRs, the truncation of family history is demonstrated to result in non-differential misclassification, and sensitivity that has little or no dependence on the FR. The bias is most pronounced for high FRs and for registers with a short life span, and increases with the age of the study cohort. In all the situations studied, the bias-corrected estimates are in excellent agreement with the true values.
Conclusions In summary, our method can correct the inevitable bias in FRs induced by using electronic population data, and is a feasible alternative to the use of validation samples.
Keywords Family history, misclassification, left truncation, sensitivity of exposure, simulation, poplab
Accepted 20 August 2009