Abstract
Background
In early 2020, the World Health Organization declared the novel corona virus disease (COVID-19) a pandemic. On top of prompting various trials to study treatments and vaccines for COVID-19, COVID-19 also had numerous consequences for ongoing clinical trials. People around the globe restricted their daily activities to minimize contagion, which led to missed visits and cancelling or postponing of elective medical treatments. For some clinical indications, COVID-19 may lead to a change in the patient population or treatment effect heterogeneity.
Methods
We present three models for clinical trial disruptions. The first model will account for the change in patient population based on chronological bias. The other will model the disruption based on the assessment of an early biomarker that is correlated with the primary outcome. The third model will account for missed visits. We will measure the effect of the disruption on randomization tests. Randomization tests are a non-parametric, design-based method of inference. We derive a methodological framework for randomization tests that allows for the assessment of clinical trial disruptions, and we will conduct a simulation study to assess the impact of disruptions on type I error probability and power in practice. Finally, we will illustrate the results with a simulation study and a case study based on a clinical trial that was interrupted by COVID-19.
Results
We show that randomization tests are robust against clinical trial disruptions in certain scenarios, namely if the disruption can be considered an ancillary statistic to the treatment effect. As a consequence, randomization tests maintain type I error probability and power at their nominal levels.
Conclusions
Randomization tests can provide a useful sensitivity analysis in clinical trials that are affected by clinical trial disruptions.
Organizzatore
Rosamarie Frieri
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