IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v10y2014i2p12n10.html
   My bibliography  Save this article

Optimal Design Strategies for Sibling Studies with Binary Exposures

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2014-0015
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/ijb-2014-0015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John M. Neuhaus & Alastair J. Scott & Christopher J. Wild & Yannan Jiang & Charles E. McCulloch & Ross Boylan, 2014. "Likelihood-based analysis of longitudinal data from outcome-related sampling designs," Biometrics, The International Biometric Society, vol. 70(1), pages 44-52, March.
    2. Quinn N. Lathrop & Ying Cheng, 2017. "Item Cloning Variation and the Impact on the Parameters of Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 245-263, March.
    3. Alfò, Marco & Carbonari, Lorenzo & Trovato, Giovanni, 2023. "On the effects of taxation on growth: an empirical assessment," Macroeconomic Dynamics, Cambridge University Press, vol. 27(5), pages 1289-1318, July.
    4. Ying Huang & Brian Leroux, 2011. "Informative Cluster Sizes for Subcluster-Level Covariates and Weighted Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 67(3), pages 843-851, September.
    5. Ullah, Inayat & Hussain, Saqib, 2023. "Impact of early access to land record information through digitization: Evidence from Alternate Dispute Resolution Data in Punjab, Pakistan," Land Use Policy, Elsevier, vol. 134(C).
    6. Brent A Coull, 2011. "A Random Intercepts–Functional Slopes Model for Flexible Assessment of Susceptibility in Longitudinal Designs," Biometrics, The International Biometric Society, vol. 67(2), pages 486-494, June.
    7. Seonho Shin, 2021. "Were they a shock or an opportunity?: The heterogeneous impacts of the 9/11 attacks on refugees as job seekers—a nonlinear multi-level approach," Empirical Economics, Springer, vol. 61(5), pages 2827-2864, November.
    8. Tanya P. Garcia & Yanyuan Ma, 2016. "Optimal Estimator for Logistic Model with Distribution-free Random Intercept," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 156-171, March.
    9. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    10. Krieg Sabine & Boonstra Harm Jan & Smeets Marc, 2016. "Small-Area Estimation with Zero-Inflated Data – a Simulation Study," Journal of Official Statistics, Sciendo, vol. 32(4), pages 963-986, December.
    11. Sylvie Goetgeluk & Stijn Vansteelandt, 2008. "Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data," Biometrics, The International Biometric Society, vol. 64(3), pages 772-780, September.
    12. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.
    13. Brumback, Babette A. & Dailey, Amy B. & Brumback, Lyndia C. & Livingston, Melvin D. & He, Zhulin, 2010. "Adjusting for confounding by cluster using generalized linear mixed models," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1650-1654, November.
    14. Li Liu & Liming Xiang, 2014. "Semiparametric estimation in generalized linear mixed models with auxiliary covariates: A pairwise likelihood approach," Biometrics, The International Biometric Society, vol. 70(4), pages 910-919, December.
    15. Brumback, Babette A. & He, Zhulin, 2011. "The Mantel-Haenszel estimator adapted for complex survey designs is not dually consistent," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1465-1470, September.
    16. Chenlu Li & Simon C Moore & Jesse Smith & Sarah Bauermeister & John Gallacher, 2019. "The costs of negative affect attributable to alcohol consumption in later life: A within-between random longitudinal econometric model using UK Biobank," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-15, February.
    17. Gerhard Tutz & Margret-Ruth Oelker, 2017. "Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures," International Statistical Review, International Statistical Institute, vol. 85(2), pages 204-227, August.
    18. Sartori, N. & Severini, T.A. & Marras, E., 2010. "An alternative specification of generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 575-584, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:10:y:2014:i:2:p:12:n:10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.