Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.
BACKGROUND
Informative birth size occurs when the average outcome depends on the number of infants per birth. Although analysis methods have been proposed for handling informative birth size, their performance is not well understood. Our aim was to evaluate the performance of these methods and to provide recommendations for their application in randomised trials including infants from single and multiple births.


METHODS
Three generalised estimating equation (GEE) approaches were considered for estimating the effect of treatment on a continuous or binary outcome: cluster weighted GEEs, which produce treatment effects with a mother-level interpretation when birth size is informative; standard GEEs with an independence working correlation structure, which produce treatment effects with an infant-level interpretation when birth size is informative; and standard GEEs with an exchangeable working correlation structure, which do not account for informative birth size. The methods were compared through simulation and analysis of an example dataset.


RESULTS
Treatment effect estimates were affected by informative birth size in the simulation study when the effect of treatment in singletons differed from that in multiples (i.e. in the presence of a treatment group by multiple birth interaction). The strength of evidence supporting the effectiveness of treatment varied between methods in the example dataset.


CONCLUSIONS
Informative birth size is always a possibility in randomised trials including infants from both single and multiple births, and analysis methods should be pre-specified with this in mind. We recommend estimating treatment effects using standard GEEs with an independence working correlation structure to give an infant-level interpretation.
Women's and Children's Health Research Institute, North Adelaide, South Australia, Australia.|School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia.
Lisa N
LN
Yelland
School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia.
Thomas R
TR
Sullivan
Department of Statistical Science, University College London, London, UK.
Menelaos
M
Pavlou
MRC Biostatistics Unit, Cambridge, UK.
Shaun R
SR
Seaman
D000328:Adult; D005260:Female; D005317:Fetal Growth Retardation / Q000453:epidemiology*; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D007234:Infant, Premature; D008297:Male; D011159:Population Surveillance; D011247:Pregnancy; D011272:Pregnancy, Multiple / Q000706:statistics & numerical data*; D047928:Premature Birth / Q000453:epidemiology*; D016032:Randomized Controlled Trials as Topic; D012015:Reference Standards
clustering; generalised estimating equations; informative cluster size; multiple births; statistical methodology