Abstract
Power is a ubiquitous, though often overlooked, component of any statistical analyses. Almost every funding agency and institutional review board requires that some sort of power analysis is conducted prior to data collection. While there are several excellent on line power calculators for independent observations, twin studies pose unique challenges that are not incorporated into these algorithms. The goal of the current manuscript is to outline a general method for calculating power in twin studies, and to provide functions to allow researchers to easily conduct power analyses for a range of common twin models. Several scenarios are discussed to demonstrate the importance of various factors that influence the power within the classical twin design and to serve as examples for the provided functions.
References
Boker S, Neale M, Maes H, Wilde M, Spiegel M, Brick T, Fox J (2011) Openmx: an open source extended structural equation modeling framework. Psychometrika 76(2):306–317
Boker SM, Neale MC, Maes HH, Wilde MJ, Spiegel M, Brick TR, Driver C (2015) Openmx 2.3.1 user guide [Computer software manual]
Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Mahwah
Dominicus A, Skrondal A, Gjessing HK, Pedersen NL, Palmgren J (2006) Likelihood ratio tests in behavioral genetics: problems and solutions. Behav Genet 36(2):331–340. doi:10.1007/s10519-005-9034-7
Harris H (1948) On sex limitation in human genetics. Eugen Rev 40(2):70–76
Martin NG, Eaves LJ, Kearsey MJ, Davies P (1978) The power of the classical twin study. Heredity 40(1):97116
Medland SE (2004) Alternate parameterization for scalar and non-scalar sex-limitation models in Mx. Twin Res 7(3):299–305
Neale MC, Eaves LJ, Kendler KS (1994) The power of the classical twin method to resolve variation in threshold traits. Behav Genet 24:239–258
Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kickpatrick RM, Boker SM (2015). OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika. doi: 10.1007/s11336-014-9435-8
Neale MC, Rysamb E, Jacobson K (2006) Multivariate genetic analysis of sex limitation and g x e interaction. Twin Res Hum Genet 9(4):481–489. doi:10.1375/183242706778024937
Posthuma D, Boomsma DI (2000) A note on the statistical power in extended twin designs. Behav Genet 30(2):147–158
R Development Core Team (2008) R: A language and environment for statistical computing [Computer software manual]. Vienna. Retrieved from http://www.R-project.org (ISBN 3-900051-07-0)
Visscher PM (2004) Power of the classical twin design revisited. Twin Res 7(5):505–512
Visscher PM (2006) A note on the asymptotic distribution of likelihood ratio tests to test variance components. Twin Res Hum Genet 9(4):490–495. doi:10.1375/183242706778024928
Wu H, Neale MC (2012) Adjusted confidence intervals for a bounded parameter. Behav Genet 42(6):886–898. doi:10.1007/s10519-012-9560-z
Acknowledgments
An earlier version of this paper was presented at the 2016 International Twin Workshop, March 10th, 2016. The author would like to thank the workshop faculty and students for their suggestions to improve the paper. This research was supported by by R25MH-019918 (PI: Hewitt), R01DA-018673 (PI: Neale) and R25DA-26119 (PI: Neale).
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Brad Verhulst declares that he has no conflicts of interest.
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Edited by John K Hewitt.
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Verhulst, B. A Power Calculator for the Classical Twin Design. Behav Genet 47, 255–261 (2017). https://doi.org/10.1007/s10519-016-9828-9
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DOI: https://doi.org/10.1007/s10519-016-9828-9