Objective To compare the effects of four methods of analysis on the results of randomised controlled trials that recruit women with multiple pregnancies and measure outcomes on their babies. Design Analysis of one real and two simulated data sets.Setting Secondary analysis of perinatal randomised controlled trials.Population Randomised controlled trials including women with multiple pregnancies.Methods The analytical methods compared were (a) assuming independence among babies, (b) analysing outcomes per women, counting a woman as having an outcome if any of her babies had it (equivalent to selecting the worst outcome among any of a woman's babies), (c) randomly selecting one baby from each set of multiples for inclusion in the analysis, (d) adjustment of the analysis to take account of nonindependence of babies from multiple pregnancies, using methods developed for analysis of cluster randomised trials. Main outcome measures Odds ratios for trials' main outcomes.Results Results from application of cluster trial methods were similar to those from assuming independence among babies, but with slightly wider confidence intervals, reflecting the reduced effective sample size caused by non-independence between babies from the same pregnancy. Results were more variable using the other two methods, and in some cases, departed markedly from the results of the cluster trial methods. Conclusions Cluster trial methods provide a simple way of adjusting the analysis to take account of nonindependence between babies from the same pregnancy. Random selection and analysis by pregnancy (methods (b) and (c)) have disadvantages and do not report outcomes for all of the babies in the trial. This may cause problems with incorporating trials analysed using these methods into systematic reviews.
INTRODUCTIONMany randomised controlled trials in perinatal medicine evaluate treatments that are given to women antenatally or during labour but measure outcomes on their babies. Some treatments given to women are specifically intended to improve outcomes for the baby, but even where the main effect of the treatment is on the woman rather than the baby, data about the baby are often important secondary outcomes.Antenatal recruitment and neonatal outcome measurement may cause a problem in the analysis when the trial population includes women with multiple pregnancies, because the outcomes of offspring from a multiple pregnancy are not independent. Babies from the same pregnancy are likely to have more similar outcomes than babies from different pregnancies, for several reasons: they will all be exposed to the same conditions before birth, they will be genetically similar or identical, and hence may react in the same way to interventions, and they may affect each other, so that one baby having a particular outcome may make the others in the same pregnancy more likely to have it.Inclusion of non-independent data means that the ''effective sample size'' of the trial is reduced: there are fewer independent outcomes in the trial than the number of b...