Abstract
BackgroundThe analysis of perinatal outcomes often involves datasets with some multiple births. These are datasets mostly formed of independent observations and a limited number of clusters of size two (twins) and maybe of size three or more. This non-independence needs to be accounted for in the statistical analysis. Using simulated data based on a dataset of preterm infants we have previously investigated the performance of several approaches to the analysis of continuous outcomes in the presence of some clusters of size two. Mixed models have been developed for binomial outcomes but very little is known about their reliability when only a limited number of small clusters are present.MethodsUsing simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Logistic models, several methods of estimation for the logistic random intercept models and generalised estimating equations were compared.ResultsThe presence of even a small percentage of twins means that a logistic regression model will underestimate all parameters but a logistic random intercept model fails to estimate the correlation between siblings if the percentage of twins is too small and will provide similar estimates to logistic regression. The method which seems to provide the best balance between estimation of the standard error and the parameter for any percentage of twins is the generalised estimating equations.ConclusionsThis study has shown that the number of covariates or the level two variance do not necessarily affect the performance of the various methods used to analyse datasets containing twins but when the percentage of small clusters is too small, mixed models cannot capture the dependence between siblings.
Highlights
The analysis of perinatal outcomes often involves datasets with some multiple births
Before methods to control for non independent data were widely available, researchers analysing studies among preterm infants have tended to ignore the non-independence in such data and treated the multiple births as if they were independent observations [7]
Recalling what we already mentioned in [8], researchers have discussed the methods available to deal with clustering in different contexts; Gates adjusted the standard error for a binary outcome in multiples [9], Carlin analysed twins using mixed models and generalized estimating equations (GEE) [10], Louis discussed a range of approaches including mixed models and GEEs for analysing studies of repeated pregnancies [11], and Shaffer compared mixed models and GEEs for continuous and binary outcome models without covariates [12]
Summary
Using simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Several methods of estimation for the logistic random intercept models and generalised estimating equations were compared
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