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

Abstract

Background: 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. Methods: 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 'pandemicfree world' and 'world including a pandemic' are of interest. Results: 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 assumptionpotentially incorporating a pandemic time-period indicator and participant infection statusor 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. Conclusions: 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.

Guidelines for Reporting Trial Protocols and Completed Trials Modified Due to the COVID-19 Pandemic and Other Extenuating Circumstances

“…40 However, existing guidelines did not describe the connections among modifications to the conduct of trials, statistical analysis, and reporting. Other reports have provided analyses on the management of missing data in trials due to COVID-19, 41 ethical dimensions of conducting trials during public health emergencies, 33,42,43 and the challenges of trial sponsorship during a pandemic. [44][45][46] Clinical and research specialty societies have also provided guidance on conducting trials during the pandemic, including in oncology, cardiovascular disease and heart failure, and hepatology.…”

Estimands in published protocols of randomised trials: urgent improvement needed

“…As others have described, different estimands may be required by different stakeholders [ 7 , 14 , 19 , 22 ], and so multiple estimands may be of use. Choice of estimand should be made in collaboration between relevant stakeholders, rather than left to the statistician alone based on their preferred analytical approach.…”

“…Separating the statistical analysis (the method of answering the question) from the question itself (the estimand) has several benefits. It can (i) clarify which treatment effect is being addressed by each trial analysis, which may otherwise be opaque to readers; (ii) ensure that chosen treatment effects are relevant to patients and other stakeholders; and (iii) ensure the design, conduct, and analysis of the trial are aligned with the research objectives [ 19 – 27 ]. The aims of this paper are to evaluate how well estimands are described in a set of published RCT protocols and to determine whether it is possible to infer the exact treatment effect being estimated for each trial’s primary outcome.…”

Exercise therapy and patient education versus intra-articular saline injections in the treatment of knee osteoarthritis: an evidence-based protocol for an open-label randomised controlled trial (the DISCO trial)

“…The COVID-19 pandemic and associated restrictions may have affected the participants both directly and indirectly [ 38 , 39 ]. While the trial suspension was relatively short-lived, the impact of the pandemic will likely continue after the restart of the trial.…”