This blog covers:
- An overview of causality assessment
- Key considerations in causality assessment
- Common challenges in assessing causality
Table of Contents
Introduction
Causality assessment is a fundamental component of pharmacovigilance and plays a critical role in the evaluation of adverse events associated with medicinal products.
Despite the availability of regulatory guidance and structured assessment approaches, causality assessment remains inherently complex. Real-world safety data are often incomplete, confounded by underlying disease, concomitant medications, and variability in clinical judgement.
As a result, assessing relatedness requires not only methodological rigor but also informed medical and scientific judgement. Let’s explore.
Causality (Relatedness) Assessment
Causality assessment is the evaluation of whether there is a reasonable possibility that a medicinal product caused or contributed to an adverse event.
This assessment typically considers factors such as the temporal relationship between drug exposure and event onset, dechallenge and rechallenge information, the presence or absence of alternative explanations (including underlying disease or concomitant therapies), and biological plausibility.
Why Is Causality Assessment Important?
- It is a regulatory requirement for expedited reporting of certain serious adverse events (SAEs) to health authorities such as the FDA, EMA, and other regulatory agencies.
- It supports signal detection, benefit–risk evaluation, and decisions on whether an adverse event should be included in product labeling (e.g., Package Insert, SmPC).
- It enables appropriate risk communication to patients, investigators, and healthcare professionals.
“Causality assessment is not about certainty, but about making informed judgments in the face of uncertainty.”
Key Considerations and Nuances in Causality Assessment
- Causality assessment is not required for spontaneous (unsolicited) reports in many regulatory frameworks.
- From a public health perspective, it is often more important to determine whether a drug is capable of causing a particular adverse event in the population than to establish causality in an individual patient.
- Adverse reactions are rarely drug-specific; diagnostic tests are often unavailable, and rechallenge is seldom ethically justified.
- For common SAEs, individual case assessment may add limited value. Instead, causality is often evaluated by comparing event rates between treatment and control groups. A clearly higher frequency in the treatment group supports a causal association.
- In contrast, uncommon or rare SAEs require individual case-level judgment, where the reviewer must assess the overall plausibility of drug-relatedness.
- Causality assessment becomes even more complex when medical coding is inconsistent or incomplete, affecting case interpretation and aggregation.
Common Challenges in Assessing Adverse Event Causality
- Incomplete information, where reporters may observe only a single case, while sponsors or regulators have access to broader data from multiple sources.
- Polypharmacy, making it difficult to isolate the contribution of a single product.
- Variability in clinical response among patients.
- Underlying or intercurrent illnesses that can mimic or confound adverse events.
- Differences in medical training or perspective among reporters and reviewers.
- Duplicate or repetitive reporting of the same information.
- The need to balance biological plausibility against limited or conflicting evidence.
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Conclusion
Causality assessment remains one of the most complex and judgment-driven activities in pharmacovigilance. While regulatory frameworks provide guiding principles, real-world case evaluation is often challenged by incomplete information, confounding factors such as polypharmacy and underlying disease, and variability in clinical interpretation.
Ultimately, the goal of causality assessment is not to definitively prove drug-relatedness in every individual case, but to support meaningful safety evaluation at the population level.
If said in one sentence “Causality assessment is very tricky!”
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