Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Deborah Plana, Geoffrey Fell, Brian M. Alexander, Adam C. Palmer () and Peter K. Sorger ()
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Deborah Plana: Harvard Medical School
Geoffrey Fell: Dana-Farber Cancer Institute
Brian M. Alexander: Dana-Farber Cancer Institute
Adam C. Palmer: University of North Carolina at Chapel Hill
Peter K. Sorger: Harvard Medical School

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Date: 2022
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DOI: 10.1038/s41467-022-28410-9

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