Reasoning About Individual Drug Causality in a Multifactorial World

Introduction

Pharmacovigilance (PV) has made substantial progress over the past two decades. Signal detection has become more systematic, disproportionality methods more refined, and real-world evidence increasingly integrated into regulatory decision-making.

Yet despite these advances, a persistent gap remains between how safety evidence is generated and how safety decisions are actually made in practice. Most PV tools are designed to answer population-level questions:

• Is there an association between a drug and an adverse event?
• Is the signal stronger than expected?
• Does it warrant further evaluation?

These questions are essential for surveillance, but they do not fully address the questions faced by safety physicians, clinical teams, and regulators when confronted with an individual case. In routine safety practice, the question is often different—and far more specific: How much did this drug actually contribute to this adverse event in this particular patient?

This distinction is not semantic. Safety decisions routinely depend on patient-level reasoning: whether to discontinue or rechallenge a therapy, how to weigh drug-related risk against disease severity, how to interpret complex cases in periodic safety update reports, or how to justify causality assessments in regulatory or medico-legal contexts.

In these settings, binary or qualitative labels such as “possible,” “probable,” or “unlikely” frequently provide insufficient guidance.

Why Individual-Level Attribution Matters in Pharmacovigilance

Real-world adverse events are rarely monocausal. They emerge from the interaction of drug exposure, comorbid conditions, physiological states, concomitant medications, and patient-specific susceptibility factors.

Yet existing PV tools were not designed to decompose this multifactorial landscape. Signal detection methods identify statistical associations at the population level.

Epidemiologic studies estimate average effects across groups. Qualitative causality algorithms classify likelihood based on predefined criteria.

None of these approaches explicitly address proportional contribution at the level of the individual patient. This limitation becomes particularly apparent in complex cases.

A safety team may reasonably conclude that a drug is “possibly” or “probably” related to an adverse event, while still lacking clarity on whether the drug was the dominant driver, a substantial contributor alongside other factors, or a marginal element in a broader causal constellation. As a result, much of patient-level causal reasoning remains implicit—experienced, but opaque—making it difficult to reproduce, explain, or defend.

An explicit approach to individual-level attribution does not seek to replace expert judgment. Rather, it aims to structure and make transparent how evidence, patient context, and uncertainty are combined to support real decisions.

A Structured Approach to Patient-Level Causal Reasoning

An effective framework for individual attribution must satisfy four practical requirements.

• First, it must filter evidence. Not all data sources are equally informative for individual causality. Randomized trials, high-quality observational studies, and mechanistic evidence contribute differently and must be weighted accordingly.
• Second, it must translate associations into patient-specific risk. Relative measures such as relative risks or hazard ratios acquire practical meaning only when combined with baseline risk. Absolute excess risk provides a clinically interpretable scale for attribution.
• Third, it must partition causality when multiple contributors coexist. The key question is not whether each factor is associated with the event, but how much each contributed. Without structured partitioning, overlapping pathways risk being double-counted or arbitrarily prioritized.
• Finally, it must support reasoned interpretation. Attribution estimates must be interpretable in decision terms—distinguishing dominant, substantial, and marginal contributors—and must explicitly acknowledge uncertainty.

Organizing causal reasoning along these steps allows patient-level assessments to move beyond qualitative labels toward transparent, reproducible conclusions.

Illustrative Case: Statin-Associated Muscle Toxicity

Consider an older patient presenting with muscle symptoms while receiving statin therapy. The patient also has untreated hypothyroidism and is concomitantly exposed to a strong CYP3A4-inhibiting medication.

Each of these factors is independently associated with muscle toxicity, yet none alone provides a complete explanation. A qualitative causality assessment might reasonably conclude that the event is “possibly” or “probably” related to statin therapy.

While not incorrect, such a conclusion does not clarify whether the statin was the primary driver of the event, a substantial contributor alongside other factors, or a secondary element. Using a structured attribution approach, baseline risk of muscle toxicity is first estimated based on patient characteristics.

Relative effects associated with statin exposure, hypothyroidism, and pharmacokinetic interaction are then combined to estimate total excess risk. This excess risk can subsequently be partitioned across contributors, yielding proportional contributions rather than a single categorical label.

In this case, causal partitioning indicates that the drug–drug interaction represents the largest contributor, followed by statin exposure and underlying thyroid dysfunction. Statin therapy remains a substantial contributor, but not the dominant one.

This interpretation differs materially from a binary attribution and has practical implications for risk mitigation, rechallenge decisions, labeling considerations, and patient counseling.

Addressing Uncertainty Explicitly

Individual attribution is inherently probabilistic. Effect estimates vary across studies, baseline risk is imperfectly known, and correlations between risk factors are rarely measured precisely. Treating attribution as a single point estimate risks overconfidence.

Structured approaches address this limitation by explicitly propagating uncertainty across inputs and expressing results as ranges rather than absolutes. For safety teams, this allows assessment of robustness: whether a drug’s contribution remains substantial across plausible assumptions, or whether conclusions are highly sensitive to uncertain parameters.

Importantly, uncertainty does not weaken causal reasoning. When made explicit, it clarifies the strength of inference and supports proportionate, defensible decision-making.

How This Fits Within Existing Pharmacovigilance Systems

Individual-level attribution is not a replacement for established PV processes. Signal detection, epidemiologic evaluation, and qualitative causality assessment remain essential upstream tools. The approach described here operates downstream, once a case has been identified for detailed evaluation.

By formalizing patient-level reasoning, structured attribution complements existing systems. It improves consistency across assessments, supports clearer documentation, and facilitates communication with regulators, clinicians, and other stakeholders—without altering core PV workflows.

Practical Takeaways for PV Teams

• Use structured attribution when qualitative labels no longer support decisions.
• Focus on proportional contribution rather than binary causality.
• Make assumptions and uncertainty explicit.
• Apply patient-level attribution as a complement to existing PV tools.

Conclusion

PV increasingly operates at the intersection of population-level evidence and individual decision-making. While current tools excel at detecting and characterizing associations, they leave a critical gap at the patient level.

Structured causal attribution provides a way to bridge that gap, translating epidemiologic evidence into transparent, interpretable reasoning that aligns with real-world safety decisions.

About the Author

Dr Eytan Ellenberg, MD, MPH, PhD, Director, Research Academy & Office of Medical Affairs, National Insurance, Institute of Israel.

Founder, Fair Research Organization (FRO), Jerusalem, Israel.