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

TL;DR

This paper discusses statistical methods to mitigate information loss in disrupted clinical trials by integrating auxiliary data sources, demonstrated through a pediatric nutrition study affected by COVID-19.

Contribution

It introduces new statistical approaches, including Bayesian and frequentist methods, for incorporating auxiliary data to improve analysis of disrupted clinical trials.

Findings

Methods increase precision over complete case analysis.

Auxiliary data integration compensates for information loss.

Application to pediatric nutrition trial demonstrates effectiveness.

Abstract

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.