A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic.
BMC Med Res Methodol
; 20(1): 208, 2020 08 12.
Article
in English
| MEDLINE | ID: covidwho-713161
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 'pandemic-free 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 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.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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Research Design
/
Randomized Controlled Trials as Topic
/
Outcome Assessment, Health Care
/
Practice Guidelines as Topic
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
BMC Med Res Methodol
Journal subject:
Medicine
Year:
2020
Document Type:
Article
Affiliation country:
S12874-020-01089-6
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