Twin study designs

The appropriate study design and analytic method always depends on the specific research question and aim. The statistical model used depends on whether the outcome is continuous, binary or other (e.g.censored survival time, ordinal), and also (although to a lesser extent) on whether the exposure is continuous or binary. Although the exact study design and analytic approach may be unique to every study, here are some general classes of study designs involving twins and some statistical guidelines.

Things to keep in mind

  • More complex statistical methods are not always better (the simple paired t-test can be very useful in twin studies.)
  • General statistical principles still apply when analysing data from twins and families:
    • Explore your data thoroughly first
    • Be aware of model assumptions and test these whenever possible (e.g., normality, linearity and equal environments)
    • Provide estimates, 95% CIs and p-values
    • Start with simple analyses and models, and build on these
    • Adjust for measured variables before considering unmeasured effects
    • Analyses of continuous outcomes are usually more powerful than those of binary outcomes
  • Detailed advice should always be sought from a statistician if you are unsure.

Articles outlining the benefit of using twin designs can be downloaded below:

 

Broad classes of twin study designs & statistical packages:

Many twin models can be fitted using standard statistical software such as R, Stata and SA ....
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TRA keeps contact with twins, enabling prospective longitudinal studies Twins were stud ....
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This design randomly assign twins within a pair, as a pair or randomly, matching for age, ....
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This design involves twins discordant for specific environmental factors or exposures, and ....
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When working with twins discordant for disease, a matched case–control study can be ....
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The classic twin design aims to quantify the roles of genetic and environmental causes of ....
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