Progression‐free survival in oncological clinical studies: Assessment time bias and methods for its correction. (29th March 2021)

• Record Type: Journal Article
• Authors: Miltenberger, Robert; Götte, Heiko; Schüler, Armin; Jahn‐Eimermacher, Antje
• Is Part Of: Pharmaceutical statistics. Volume 20:Number 4(2021)
• Page Start: 864
• Page End: 878
• Publication Date: 2021-03-29
• Subjects: approximate Bayesian computation -- bias correction -- interval censoring -- progression‐free survival -- Weibull distribution; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2115
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract: Progression‐free survival (PFS) is a frequently used endpoint in oncological clinical studies. In case of PFS, potential events are progression and death. Progressions are usually observed delayed as they can be diagnosed not before the next study visit. For this reason potential bias of treatment effect estimates for progression‐free survival is a concern. In randomized trials and for relative treatment effects measures like hazard ratios, bias‐correcting methods are not necessarily required or have been proposed before. However, less is known on cross‐trial comparisons of absolute outcome measures like median survival times. This paper proposes a new method for correcting the assessment time bias of progression‐free survival estimates to allow a fair cross‐trial comparison of median PFS. Using median PFS for example, the presented method approximates the unknown posterior distribution by a Bayesian approach based on simulations. It is shown that the proposed method leads to a substantial reduction of bias as compared to estimates derived from maximum likelihood or Kaplan–Meier estimates. Bias could be reduced by more than 90% over a broad range of considered situations differing in assessment times and underlying distributions. By coverage probabilities of at least 94% based on the credibility interval of the posterior distribution the resulting parameters hold common confidence levels. In summary, the proposed approach is shown to be useful for a cross‐trial comparison of median PFS.