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Optimal Design Strategies for Sibling Studies with Binary Exposures

Author

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  • Li Zhigang

    (Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, One Medical Center Drive, 7927 Rubin Building, Hanover, NH 03755, USA)

  • McKeague Ian W.

    (Department of Biostatistics, Columbia University, 722 West 168th Street, New York, NY 10032, USA)

  • Lumey Lambert H.

    (Department of Epidemiology, Columbia University, 722 West 168th Street, New York, NY 10032, USA)

Abstract

Sibling studies have become increasingly popular because they provide better control over confounding by unmeasured family-level risk factors than can be obtained in standard cohort studies. However, little attention has been devoted to the development of efficient design strategies for sibling studies in terms of optimizing power. We here address this issue in commonly encountered types of sibling studies, allowing for continuous and binary outcomes and varying numbers of exposed and unexposed siblings. For continuous outcomes, we show that in families with sibling pairs, optimal study power is obtained by recruiting discordant (exposed–control) pairs of siblings. More generally, balancing the exposure status within each family as evenly as possible is shown to be optimal. For binary outcomes, we elucidate how the optimal strategy depends on the variation of the binary response; as the within-family correlation increases, the optimal strategy tends toward only recruiting discordant sibling pairs (as in the case of continuous outcomes). R code for obtaining the optimal strategies is included.

Suggested Citation

  • Li Zhigang & McKeague Ian W. & Lumey Lambert H., 2014. "Optimal Design Strategies for Sibling Studies with Binary Exposures," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 185-196, November.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:2:p:12:n:10
    DOI: 10.1515/ijb-2014-0015
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    References listed on IDEAS

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    1. John M. Neuhaus & Charles E. McCulloch, 2006. "Separating between‐ and within‐cluster covariate effects by using conditional and partitioning methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 859-872, November.
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