A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic
Open Access
- 12 August 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Medical Research Methodology
- Vol. 20 (1), 1-12
- https://doi.org/10.1186/s12874-020-01089-6
Abstract
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.Keywords
Other Versions
This publication has 27 references indexed in Scilit:
- Controlled multiple imputation methods for sensitivity analyses in longitudinal clinical trials with dropout and protocol deviationClinical Investigation, 2015
- Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputationStatistics in Medicine, 2014
- Missing data sensitivity analysis for recurrent event data using controlled imputationPharmaceutical Statistics, 2014
- Choosing sensitivity analyses for randomised trials: principlesBMC Medical Research Methodology, 2014
- Analysis of Longitudinal Trials with Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple ImputationJournal of Biopharmaceutical Statistics, 2013
- Strategy for intention to treat analysis in randomised trials with missing outcome dataBMJ, 2011
- Multiple imputation using chained equations: Issues and guidance for practiceStatistics in Medicine, 2010
- Modelling treatment-effect heterogeneity in randomized controlled trials of complex interventions (psychological treatments)Statistics in Medicine, 2007
- Instruments for Causal InferenceEpidemiology, 2006
- Inference and missing dataBiometrika, 1976