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Clinical trials disruption has always represented a non negligible part ofthe ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemichas led to an impressive and unprecedented initiation of clinical research, ithas also led to considerable disruption of clinical trials in other diseaseareas, with around 80% of non-COVID-19 trials stopped or interrupted during thepandemic. In many cases the disrupted trials will not have the plannedstatistical power necessary to yield interpretable results. This paperdescribes methods to compensate for the information loss arising from trialdisruptions by incorporating additional information available from auxiliarydata sources. The methods described include the use of auxiliary data onbaseline and early outcome data available from the trial itself and frequentistand Bayesian approaches for the incorporation of information from external datasources. The methods are illustrated by application to the analysis ofartificial data based on the Primary care pediatrics Learning ActivityNutrition (PLAN) study, a clinical trial assessing a diet and exerciseintervention for overweight children, that was affected by the COVID-19pandemic. We show how all of the methods proposed lead to an increase inprecision relative to use of complete case data only.

Coping with Information Loss and the Use of Auxiliary Sources of Data: A  Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial  Disruptions