Sibling comparison designs: bias from non-shared confounders and measurement error.
Twins, full siblings, and half-siblings are increasingly used as comparison groups in matched cohort and matched case-control studies. The "within-pair" estimates acquired through these comparisons are free from confounding from all factors that are shared by the siblings. This has made sibling comparisons popular in studying associations thought likely to suffer confounding from socioeconomic or genetic factors. Despite the wide application of these designs in epidemiology, they have received little scrutiny from a statistical or methodological standpoint. In this paper we show, analytically and through a series of simulations, that the standard interpretation of the models is subject to several limitations that are rarely acknowledged.Although within-pair estimates will not be confounded by factors shared by the siblings, such estimates are more severely biased by non-shared confounders than the unpaired estimate. If siblings are less similar with regard to confounders than to the exposure under study, the within-pair estimate will always be more biased than the ordinary unpaired estimate. Attenuation of associations due to random measurement error in exposure will also be higher in the within-pair estimate, leading within-pair associations to be weaker than corresponding unpaired associations, even in the absence of confounding. Implications for the interpretation of sibling comparison results are discussed.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Thomas.Frisell@ki.se
Thomas
T
Frisell
Sara
S
Öberg
Ralf
R
Kuja-Halkola
Arvid
A
Sjölander
D015982:Bias; D016022:Case-Control Studies; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D016555:Matched-Pair Analysis; D035781:Siblings; D018433:Twin Studies as Topic / Q000379:methods* / Q000706:statistics & numerical data