How should randomised trials including multiple pregnancies be analysed?


*Dr S. Gates, National Perinatal Epidemiology Unit, Institute of Health Sciences, Old Road, Oxford OX3 7LF, UK.


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 non-independence 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 non-independence 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.