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A qualitative study evaluating causality attribution for serious adverse events during early phase oncology clinical

Som D Mukherjee1, Megan E Coombes, Mitch Levine

  • 1Juravinski Cancer Centre, McMaster University, 699 Concession Street, Hamilton, ON, Canada.

Abstract

Insights

Assigning causality for serious adverse events (SAEs) in oncology trials is complex. Researchers use logical reasoning but desire more objective tools for accurate assessment of investigational drug-related toxicities.

Area of Science:

  • Oncology
  • Clinical Trials
  • Pharmacovigilance

Background:

  • Novel targeted therapies in combination with traditional agents increase potential for complex toxicities in early phase oncology trials.
  • Accurate causality assessment of serious adverse events (SAEs) is critical for patient safety and drug development.
  • Understanding the process, tools, and challenges of causality assignment by researchers is essential.

Purpose of the Study:

  • To explore the clinical reasoning, tools, and challenges faced by researchers when assigning causality to SAEs in early phase oncology trials.
  • To identify areas for improvement in the current causality assessment process.

Main Methods:

  • Conducted 32 semi-structured interviews with medical oncologists and trial coordinators at six Canadian academic cancer centers.
  • Utilized verbatim transcription, individual content analysis, and thematic analysis across the interview set.

Main Results:

  • Causality assessment is a complex process often lacking complete data.
  • Researchers employ a logical strategy but find the process subjective and frequently rushed.
  • Participants reported a lack of useful tools and expressed a desire for increased objectivity.

Conclusions:

  • Attributing causality to SAEs in early phase oncology trials is challenging.
  • While researchers use logical reasoning, the current methods for causality assignment require improvement.
  • Development of a dedicated causality assessment tool for early phase oncology trials is recommended.

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