Optimal Design Strategies for Sibling Studies with Binary Exposures

Li Zhigang (), McKeague Ian W. () and Lumey Lambert H. ()
Additional contact information
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

The International Journal of Biostatistics, 2014, vol. 10, issue 2, 185-196

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.

Keywords: design; power; sample size; sibling studies (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2014-0015 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:10:y:2014:i:2:p:12:n:10

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2014-0015

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().