A Four-Step Strategy for Handling Missing Outcome Data in Randomised Trials Affected by a Pandemic

Featuring: Professor James Carpenter,

Programme Leader in Methodology (MRC Clinical Trials Unit, LSHTM)

Sign up to watch the presentation

You may need to disable ad blocker

Webinar: A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

Featuring: Professor James Carpenter,
Programme Leader in Methodology (MRC Clinical Trials Unit, LSHTM)

Professor Jennifer Rogers,
VP, Statistical Research & Consultancy (PHASTAR)

Thursday 16th December
16:00 GMT | 11:00 EST | 08:00 PST

Image of Professor James Carpenter

Professor James Carpenter

Image of Professor Jennifer Rogers - VP, Statistical Research & Consultancy

Professor Jennifer Rogers

The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking.

We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a ‘pandemic-free world’ and ‘world including a pandemic’ are of interest.

In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a ‘pandemic-free world’, participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the ‘world including a pandemic’, all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption – potentially incorporating a pandemic time-period indicator and participant infection status – or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses.

Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

For more information see Professor Carpenter's paper here.


Meet the speakers

Professor James Carpenter
Programme Leader in Methodology (MRC Clinical Trials Unit, LSHTM)

A Professor of Medical Statistics, James leads the Methodology Analysis programme at the MRC Clinical Trials Unit, London School of Hygiene & Tropical Medicine, and has interests across the Methodology theme. The motivation for his research is finding practical methodological solutions to challenges in Phase III clinical trials and observational research, principally: choosing the appropriate estimand for a trial, practical approaches for exploring and communicating the sensitivity of trial results to a range of appropriate assumptions about the missing outcome data, statistical methods in meta-analysis, and using routinely collected health care data to inform trial outcome measures.

Professor Jennifer Rogers
VP, Statistical Research & Consultancy (PHASTAR)

Jen joined PHASTAR as Head of Statistical Research in August 2019, following a move from the University of Oxford where she was Director of Statistical Consultancy Services and an Associate Professor in the Department of Statistics. She had previously worked as a Post-Doctoral Research Fellow in the Department of Statistics funded by the National Institute of Health Research. Formerly Vice President for External Affairs at the RSS and now a member of its COVID-19 task force, Jen directs the statistical research strategy helping the company stay at the cutting edge of new methodological advances.

Enjoying our content? Click on the button for more PHASTAR videos

We are experts in study design, statistical analysis, data science, data capture and reporting for clinical trials