  Clinical trials disruption has always represented a non negligible part of
the ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemic
has led to an impressive and unprecedented initiation of clinical research, it
has also led to considerable disruption of clinical trials in other disease
areas, with around 80% of non-COVID-19 trials stopped or interrupted during the
pandemic. In many cases the disrupted trials will not have the planned
statistical power necessary to yield interpretable results. This paper
describes methods to compensate for the information loss arising from trial
disruptions by incorporating additional information available from auxiliary
data sources. The methods described include the use of auxiliary data on
baseline and early outcome data available from the trial itself and frequentist
and Bayesian approaches for the incorporation of information from external data
sources. The methods are illustrated by application to the analysis of
artificial data based on the Primary care pediatrics Learning Activity
Nutrition (PLAN) study, a clinical trial assessing a diet and exercise
intervention for overweight children, that was affected by the COVID-19
pandemic. We show how all of the methods proposed lead to an increase in
precision relative to use of complete case data only.
