Linnea Olson: An Appreciation of a Giant (Preprint)

UNSTRUCTURED Reviewing the life and impact of the late Linnea Olson, a cancer patient advocate who helped shape cancer clinical trials

The Case Against Censoring of Progression-Free Survival in Cancer Clinical Trials – A Pandemic Shutdown as an Illustration

Missing data may lead to loss of statistical power and introduce bias in clinical trials. The ongoing Covid-19 pandemic has had a profound impact on patient health care and on the conduct of cancer clinical trials. Restricted access to sites, medication and evaluations brings challenges to the analysis of clinical trials due to missing data. Although several endpoints may be affected, progression-free survival (PFS) is of major concern, given its frequent use as primary endpoint in advanced cancer and the fact that missed radiographic assessments are to be expected. If patients with progression have delayed radiographic assessment due to the pandemic, there is controversy between censoring at the last visit prior to a shutdown period or ascribing the progression date to the day the assessment is eventually done after the end of the shutdown. The recent introduction of the estimand framework creates an opportunity to define more precisely the target of estimation and ensure alignment between the scientific question and the statistical analysis. Two basic approaches can be considered for handling missing tumor scans due to the pandemic: a “treatment policy” strategy, which consists in ascribing events to the time they are observed, and a “hypothetical” approach of censoring patients with events during the shutdown period at the last assessment prior to that period. In this article, we show through simulations how these two approaches may affect the overall power of a study and bias the estimated treatment effect and median PFS estimates. As a general rule, we suggest that the treatment policy approach, which conforms with the intent-to-treat principle, should be the primary analysis in order to avoid unnecessary loss of power and minimize bias in median PFS estimates.