Zhou X, Eid D, Gnanasakthy A. Methods for reporting the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) data in cancer clinical trials. Poster presented at the ISPOR 23rd Annual International Meeting; May 21, 2018. Baltimore, MD.

OBJECTIVES: PRO-CTCAE has been developed to integrate patient perspectives on symptomatic adverse events in cancer trials. As a relatively new assessment, there are currently no standard approaches for analyzing and reporting PRO-CTCAE data. Basch et al. (2016) provided examples using stacked bar charts and recommended future directions consider baseline and more frequent reports of PRO-CTCAE to allow for granular longitudinal analyses. In light of this paper, we aim to provide a systematic and easy-to-apply approach that can be used to report PRO-CTCAE data in clinical trials for oncology.

METHODS: We propose two sets of analyses for baseline and postbaseline data. Analysis of baseline data will provide background information on disease burden in the clinical trial patient population. The postbaseline analysis will focus on treatment comparisons, although it can also be used in single-arm trials. We first discuss some of the challenges that arise when reporting PRO-CTCAE data. Then we provide example figures with detailed rationale and considerations. Figures are generated using SAS with simulated data. Finally, we summarize the advantages of the proposed method and suggest next steps.

RESULTS: We have expanded upon the stacked bar chart so that it presents percentages with different cut-off points (e.g., score ≥ 1, ≥ 3) all in one figure. For postbaseline data, both improved and worsened symptoms, relative to baseline, are displayed in one figure. This figure can be used to show results at one timepoint (e.g., maximum grade postbaseline) or longitudinally for multiple timepoints.

CONCLUSIONS: The presentation provides a simple and informative solution to PRO-CTCAE data reporting that considers both the baseline and longitudinal assessments that can be routinely implemented in clinical trials. The visualization of the results makes it easy to identify symptoms that matter to patients and are responsive to treatments.

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