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BMC Med Res Methodol. 2009 Feb 26;9:12. doi: 10.1186/1471-2288-9-12.

Analysis of neonatal clinical trials with twin births.

Author information

1
Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA. mshaffer@hes.hmc.psu.edu

Abstract

BACKGROUND:

In neonatal trials of pre-term or low-birth-weight infants, twins may represent 10-20% of the study sample. Mixed-effects models and generalized estimating equations are common approaches for handling correlated continuous or binary data. However, the operating characteristics of these methods for mixes of correlated and independent data are not well established.

METHODS:

Simulation studies were conducted to compare mixed-effects models and generalized estimating equations to linear regression for continuous outcomes. Similarly, mixed-effects models and generalized estimating equations were compared to ordinary logistic regression for binary outcomes. The parameter of interest is the treatment effect in two-armed clinical trials. Data from the National Institute of Child Health & Human Development Neonatal Research Network are used for illustration.

RESULTS:

For continuous outcomes, while the coverage never fell below 0.93, and the type I error rate never exceeded 0.07 for any method, overall linear mixed-effects models performed well with respect to median bias, mean squared error, coverage, and median width. For binary outcomes, the coverage never fell below 0.90, and the type I error rate never exceeded 0.07 for any method. In these analyses, when randomization of twins was to the same treatment group or done independently, ordinary logistic regression performed best. When randomization of twins was to opposite treatment arms, a rare method of randomization in this setting, ordinary logistic regression still performed adequately. Overall, generalized linear mixed models showed the poorest coverage values.

CONCLUSION:

For continuous outcomes, using linear mixed-effects models for analysis is preferred. For binary outcomes, in this setting where the amount of related data is small, but non-negligible, ordinary logistic regression is recommended.

PMID:
19245713
PMCID:
PMC2676314
DOI:
10.1186/1471-2288-9-12
[Indexed for MEDLINE]
Free PMC Article
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