%0 Journal Article
%T Analysis of neonatal clinical trials with twin births
%A Michele L Shaffer
%A Allen R Kunselman
%A Kristi L Watterberg
%J BMC Medical Research Methodology
%D 2009
%I BioMed Central
%R 10.1186/1471-2288-9-12
%X 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.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.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.Neonatal studies involving singletons and twin births pose a unique correlated data problem. Data from singletons and twins whose siblings are not included in the study (unmatched twins) meet the basic assumption of independence, while the remaining complete twin births have a hierarchical structure. In the absence of twin births, classical statistical techniques are valid and appropriate. In the absence of singletons, hierarchical methods that account or adjust for the nested structure can be applied. Failing to a
%U http://www.biomedcentral.com/1471-2288/9/12