Australian Clinical Trials Alliance – Excellence in Trial Statistics Award

Congratulations to Dr Thomas Sullivan and the N3RO investigator team, who won the Excellence in Trial Statistics Award during the celebration of International Clinical Trials Day organised by the Australian Clinical Trials Alliance (ACTA) for their statistical contributions to the n3 fatty acids for improvement in respiratory outcomes (N3RO) trial.

International Clinical Trials Day, established in 2005, is held on the 20th of May each year to commemorate the day when James Lind, a Scottish physician, conducted the first controlled clinical trial to determine a better treatment for scurvy in 1747. In recent years, this milestone has been celebrated by the Australian Clinical Trials Alliance (ACTA) via a national tribute and award ceremony where they present the ACTA Trial of the Year award to recognise the outstanding achievement in investigator-initiated clinical trials by Australian researchers. In 2017 an additional Excellence in Trial Statistics award was established by the ACTA Statistics in Trials Interest Group (STInG) to promote the importance of statistics in trials. This year these two awards were presented by the Hon. Greg Hunt MP on May the 16th at the Royal Melbourne Hospital in Melbourne.

The Excellence in Trial Statistics award honours excellence in statistical clinical trial design, analysis, interpretation, and reporting. This year, Dr Thomas Sullivan from The University of Adelaide and the N3RO investigator team were presented with this award for their statistical contributions to the N3RO trial. Led by researchers from the Healthy Mothers, Babies and Children theme from the South Australian Health & Medical Research Institute, in collaboration with researchers from The University of Adelaide and the Women’s and Children’s Hospital N3RO was a randomised controlled trial in preterm infants born at less than 29 weeks’ gestational age comparing docosahexaenoic acid to control treatment for the reduction of bronchopulmonary dysplasia. Bronchopulmonary dysplasia is a serious complication in preterm infants characterized by an inflammatory process causing abnormal lung development, decreased vascular and alveolar development, and the need for supplemental oxygen or assisted ventilation at 36 weeks postmenstrual age (Collins et al., 2017).

Dr Thomas Sullivan and his team used state of the art statistical methods during the design and analysis of the trial, which were pre-planned in a comprehensive statistical analysis plan. As part of this analysis two new statistical methodological developments were required which were subsequently published in statistical journals. The first was around accounting for clustering in sample size calculations when infants from multiple births are randomised individually as opposed to being randomised together in a cluster (Yelland, Sullivan, Price, & Lee, 2017). The second was about how best to handle missing outcome data using multiple imputation when the aim of the analysis is to produce a relative risk (Sullivan, Lee, Ryan, & Salter, 2017). Congratulations to Thomas and his team.


Dr Thomas Sullivan

One of the aims of the ACTA STInG is to improve the statistical quality of investigator initiated clinical trials within Australia. The aim of the Excellence in Trial Statistics award is to raise awareness of the importance of high quality statistical contributions in investigator-initiated clinical trials.


Sabine Braat and Katherine Lee




Collins, C.T., Makrides, M., McPhee, A.J., Sullivan, T.R., Davis, P.G., Thio, M., Simmer, K., Rajadurai, V.S., Travadi, J., Berry, M.J. and Liley, H.G., 2017. Docosahexaenoic acid and bronchopulmonary dysplasia in preterm infants. New England Journal of Medicine, 376(13), pp.1245-1255.

Yelland, L.N., Sullivan, T.R., Price, D.J. and Lee, K.J., 2017. Sample size calculations for randomised trials including both independent and paired data. Statistics in medicine, 36(8), pp.1227-1239.

Sullivan, T.R., Lee, K.J., Ryan, P. and Salter, A.B., 2017. Multiple imputation for handling missing outcome data when estimating the relative risk. BMC medical research methodology, 17(1), p.134.


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