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

Abstract

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 article 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.

KEYWORDS:

• Auxiliary data
• Bayesian inference
• External data
• Frequentist inference
• Interrupted studies
• Statistical power

Acknowledgments

The authors thank the National Institute of Statistical Sciences for facilitating this work on Coping with Information Loss and the Use of Auxiliary Sources of Data, which is part of the Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions. The authors would also like to recognize the organizers of this forum series (those who are not an author on this article): Chris Jennison and Adam Lane as well as the speaker at the motivating workshop who was not an author on this article: Heng Li. The views expressed are those of the authors and not necessarily those of Fulbright Belgium, Belgian American Educational Foundation, VLAIO, the National Institutes of Health, the UK Medical Research Council, the NIHR or the Department of Health and Social Care. We are grateful for feedback of an anonymous reviewer and Alessandra Salvan on earlier versions of this manuscript.

Data Availability Statement

The illustrative example was analyzed with R software (http://www.r-project.org) v. 4.1.1. R Code is available at github.com/reidcw/NISS-Information-Loss.

Additional information

Funding

K.V.L. is supported by Fulbright Belgium, Belgian American Educational Foundation and VLAIO (Flemish Innovation and Entrepreneurship) under the Baekeland grant agreement HBC.2017.0219. S.T. is partially supported by the Department of Health and Human Services of the National Institutes of Health under award number R40MC41748.