Navigating Diverging Perspectives: Reasoning, Evidence, and Decision-Making in Drug Safety

Abstract

Decision making in drug safety is a complex and iterative process that requires the integration of diverse evidence sources, scientific reasoning, and clinical judgment. Diverging opinions among stakeholders—including pharmacovigilance professionals, regulatory authorities, clinical researchers, statisticians, and epidemiologists—often stem from differences in data interpretation, methodological approaches, and thresholds for concern or action. This paper examines the key sources of these divergences and presents a structured framework to enhance alignment in drug safety decision making. The proposed framework outlines three core dimensions: evidence assessment, interpretation, and action. It distinguishes between quantitative aspects, such as effect magnitude and measurement error, and qualitative considerations, including contextual interpretation and risk thresholds. The framework also underscores the importance of multidisciplinary collaboration, as safety professionals must actively engage with other scientific and regulatory stakeholders to ensure a comprehensive evaluation of the evidence. A fundamental challenge in pharmacovigilance is the need to communicate the complexities of drug safety assessment to a broader audience, including those who may not be familiar with the nuances of safety decision making. This paper aims to serve not only as a resource for new pharmacovigilance professionals, but also as a tool to facilitate clearer communication between disciplines. By adopting a structured approach and fostering open dialogue, drug safety professionals can enhance transparency and improve regulatory and clinical decision-making processes.

Key Points

Drug safety decisions involve interpreting complex data and balancing different types of evidence. This paper provides a framework to help professionals navigate these challenges and make informed decisions.

Safety professionals, regulators, and researchers often interpret data differently due to the inherent limitations of the scientific tools, leading to disagreements. To bridge different perspectives, collaboration and clear communication can help align decision-making across disciplines.

Distinguishing between data-driven insights and clinical judgment, helps clarify various aspects of the safety decision-making for those not involved in the day-to-day activities as well as help colleagues interested in entering the field understand what it takes.

Introduction

Decisions in the scientific field, particularly in drug safety, are fundamentally driven by data-based reasoning, which is emphasized by Edwards Deming in his famous quote, “Without data, you’re just another person with an opinion”. The two main reasoning approaches that are most relevant to drug safety—deductive and inductive—play pivotal roles in the detection and management of safety signals. These reasoning methods, used by both regulatory authorities and pharmaceutical companies, ensure that decisions are based on comprehensive analyses and contribute to the understanding drug safety profiles.

Divergent opinions among safety professionals within and between regulatory authorities and pharmaceutical companies are common due to the intricacies of assessing drug safety evidence. Factors such as methodological and reasoning nuances, patient population variability, data limitations (particularly in post-marketing), and differing thresholds for concern or action contribute to these differences. It is worth noting that pharmacovigilance has evolved into a multifaceted evidence-based scientific field aimed at minimizing patient exposure to harmful effects while guiding appropriate risk mitigation and management strategies [1]. For instance, pharmacogenomic data increasingly play a role in drug safety decisions, as genetic variability can influence drug metabolism, efficacy, and susceptibility to adverse events (AEs). This adds an additional layer of complexity to safety assessments, necessitating local data validation and personalized risk stratification [2]. Varying interpretations of the evidence, the weight assigned to different data types, and the analysis methods employed often lead to further differing conclusions [3–8].

This paper examines sources of evidence in drug safety assessments and how they contribute to diverging opinions in decision making. A key challenge in pharmacovigilance is to ensure that all stakeholders—including clinical researchers, epidemiologists, statisticians, and regulatory professionals—understand the reasoning behind safety decisions. Safety professionals often navigate scrutiny from those who may not fully appreciate the nuances of the decision-making process. This paper provides guidance for those entering the drug safety field and also serves as a resource to facilitate clearer communication across disciplines. By explicitly framing the decision-making process and emphasizing the dynamic roles of multidisciplinary teams in assessing evidence, forming opinions, and deciding on actions, the framework presented aims to bridge gaps in understanding and improve alignment in drug safety discussions.

Handling Safety Data: The Reasoning Behind the Reasons for Concern

Inductive Reasoning in Drug Safety

Inductive reasoning1^,2 involves drawing general conclusions from specific observations or data and is a fundamental component of drug safety practices. It involves analyzing AE reports to identify patterns, trends, or signals that suggest potential safety issues with a drug, making it essential for hypothesis generation. For instance, inductive reasoning would lead to the hypothesis that a drug may be associated with liver toxicity when multiple cases of liver toxicity are reported. This hypothesis is based on the accumulation of individual cases while also considering underlying disease biology, epidemiology, and the scientific mechanism with known or hypothesized on- and off-target effects, while excluding the influence of concomitant medications that might be the cause. However, inductive reasoning can be subject to the “exception fallacy” because it often begins with anecdotal evidence or isolated cases prone to biases and confounding factors. The exception fallacy arises when such individual cases or rare events are used to draw conclusions about a larger population, potentially resulting in misleading results.3 This can cause an overestimation of risk, especially when these observations are not representative or systematically collected, making conclusions inherently uncertain and in need of further validation.

One of the factors behind diverging opinions in drug safety is that the AEs emerging during a drug’s lifecycle, which might trigger inductive reasoning about a particular drug’s safety profile, are not homogeneous. Adverse events vary widely in their nature, the strength of evidence supporting them, their timing of occurrence, and consequently the attendant level of uncertainty. Some of the AEs are identified in the development phase in a controlled environment, such as clinical trials. Although the evidence is stronger for the detected AEs, rare or long-latency AEs may not be captured due to the relatively limited sample size and the fact that the population is highly selected. Other AEs may emerge in more diverse and less controlled post-marketing settings, where the real-world use of the drug may differ significantly from the controlled conditions of clinical trials. Further, some AEs may be biologically plausible, or supported by multiple streams of evidence, while others might even seem counterintuitive to what is expected from the drug effect. For instance, the finding of increased risk of myocardial infarction in studies of the diabetes drug rosiglitazone was contrary to what would be expected from a drug treating diabetes [9].

The dynamic nature of identifying AEs, coupled with the inherent limitations of existing data infrastructure and variations in exposure duration and dosages, adds layers of difficulty to AE detection and management. Media coverage can also lead to ‘stimulated reporting,’ where patients and healthcare professionals, influenced by publicity, report specific AEs at increased rates for one drug than for others in the same class, introducing potential bias into safety analyses [10]. Detection of AEs is further complicated by different collection and reporting mechanisms of AEs: passive reporting, which gathers data from electronic health records, or insurance claims databases; spontaneous reporting, which relies on voluntary reports by healthcare professionals or patients; and solicited reporting from structured programs, such as patient support initiatives, where information is actively gathered. This inherent variability necessitates a nuanced approach to AE management, recognizing that the level of uncertainty associated with different types of AEs can significantly impact regulatory and pharmaceutical companies' decision making.

Deductive Reasoning in Drug Safety

Deductive reasoning4 starts with a general hypothesis or theory and evaluates it by examining evidence in a more structured way. In drug safety, it is commonly applied in clinical trials and post-marketing studies using real-world evidence to evaluate predefined hypotheses about drug safety, playing a vital role in confirming or refuting suspected safety findings and establishing causality. For instance, if a drug has a mechanism of action that could potentially cause liver toxicity, deductive reasoning could involve designing a study to evaluate this hypothesis and determine if there is a deviation from the null hypothesis of no harmful effects. While this approach is highly valuable for establishing causality and confirming the risks associated with a drug, it may not be as effective in detecting unexpected or rare AEs, as it relies on predefined hypotheses.

Clinical research and its underlying premises, operating under the deductive reasoning, is often seen as a journey to uncover the truth—a truth that sometimes remains elusive due to the limitations of available scientific tools and methodologies. The first premise of research lies in sampling. In an ideal world, we would have complete data on every individual within a patient population, making the notion of "re”-searching through conducting several studies redundant. However, in reality, resource constraints necessitate that we rely on sampling to estimate population parameters from the sample data and then extrapolate these findings to infer broader conclusions about the drug effects. This reliance on sampling introduces inherent uncertainty challenges in drug safety, as the information we end up with is often a mere estimate influenced by the limitations of our sampling methods.

The second premise is the necessity to depend on group experience in a study to calculate the desired estimate of drugs’ effects. An important pitfall in interpreting group-level data in drug safety is the “ecological fallacy”.5 This occurs when conclusions about individuals are drawn from aggregate data, potentially over- or underestimating the true risks for specific patients as the approach overlooks individual variability and unique characteristics within a group. For example, a study might report a high overall rate of kidney toxicity in a group of patients taking a new drug. One might mistakenly characterize the risk or its severity as being equal in all patients, when, in reality, this risk could be driven by a specific subgroup, such as those with pre-existing kidney disease or on additional nephrotoxic drugs. Conversely, assuming that all patients share a low risk based on overall data could mask the vulnerability of certain subgroups, like older adults with higher susceptibility. Misinterpreting these risks can lead to overly cautious use of the drug or inadequate safety monitoring for high-risk patients. In this context, it is important to note that given the typically low overall rate of AEs in general, it is not always possible to identify such subgroups, as the number of affected individuals may be too small to detect significant differences in risk across different subpopulations. This limitation, and the underlying dynamics of relying on group data, can contribute to the uncertainty in the interpretation of safety data and hence potentially diverging decisions among stakeholders.

In short, by design, approaches that rely on sampling and group experience simplify complex biological and clinical phenomena into measurable components, like average kidney or liver function test levels in the studied patient sample. While this reductionism helps manage data, it sacrifices the nuances and variability of individual patient data. The trade-off between operational efficiency and data richness is a key challenge in drug safety adding to the potential for diverging opinions, requiring a balance between simplification and the need for detailed data to ensure accurate safety assessments.

Decision Dimensions in Drug Safety: Framework for Evaluating Data

Decision making in drug safety involves assessing whether a drug is responsible for an AE and determining the appropriate protective and mitigative response. In that regard, benefit-risk (BR) considerations are central to drug safety decision making, as they frame the evaluated safety signals and determine appropriate actions. Given the inherent uncertainties in safety data, these decisions often rely on assessment that balances potential risks against therapeutic benefits. This requires an interplay between data-driven evidence and clinical judgment. While quantitative evidence forms the foundation for assessments, particularly in scenarios driven by deductive reasoning, translating this into meaningful actions requires a qualitative understanding of the context. Clinical judgment is informed by experience, contextual understanding, and an awareness of the methodological nuances and scientific underpinnings involved in safety data interpretation [11]. As a result of this dynamic, differences are common with regard to opinion among safety professionals at regulatory authorities and pharmaceutical companies, prescribers, and patients. These differences are further fueled by the fact that individual risk tolerance varies based on personal experiences, expectations, and perceived benefits of the treatment [12].

A structured decision-making framework can help reduce divergence in opinions by providing a consistent method for evaluating safety data. Figure 1 presents such framework, emphasizing three core dimensions—evidence assessment, interpretation, and action. These dimensions are depicted as distinct yet interconnected steps, reflecting the iterative nature of safety decision making [13, 14]. Each step requires active contributions from diverse stakeholders, as drug safety professionals must engage experts from various disciplines—such as preclinical and clinical researchers, statisticians, and epidemiologists—who provide unique insights that are essential for a comprehensive evaluation of the evidence. The nature and role of these stakeholders varies with each step of the decision-making framework. Moving systematically from evidence to interpretation and then to action ensures that decisions are grounded in objective data while also incorporating clinical insights and practical considerations, ultimately supporting the safe and effective use of medications.

Fig. 1.

Representation of a structured decision-making framework emphasizing the three core dimensions—evidence assessment, interpretation, and action—as distinct yet interconnected steps that reflect the iterative nature of safety decision making. It distinguishes quantitative aspects of evidence, such as effect magnitude and measurement error, from qualitative aspects such as contextual interpretation and thresholds for action. The diagram underscores the role of clinical judgment in bridging these quantitative and qualitative concepts, ensuring that operational considerations are appropriately factored into safety assessments. The framework highlights the critical interplay between data-driven insights and interpretative expertise, reinforcing the necessity of close collaboration among diverse stakeholders to ensure robust and actionable safety decisions

Evidence: What Do the Data Say?

The first step in evaluating safety data is to examine the raw data collected from clinical trials, real-world observational studies, spontaneous reports, or other sources. These data provide an objective foundation, describing what has been observed in terms of AEs, their frequency, severity, and the context in which they occur. The focus at this stage is purely on the facts to ensure that conclusions are based on solid, empirical evidence and the key question is: What do the data reveal about the potential relationship between a particular drug and an AE and how robust are the findings? Determining the relationship between a drug and an AE involves a systematic exploration of a range of factors that could influence this relationship, starting with the identification of potential effect modifiers and confounders [11]. Recognizing effect modifiers helps to refine our understanding of which subpopulations may be at greater risk and under what specific conditions. Accounting for the confounding factors is essential to ensure that the observed association is genuinely attributable to the drug itself and not to other influencing variables. This distinction between causality and association is a critical concept in evaluating drug safety evidence. While an association may indicate a relationship between the drug and an AE has been observed, it does not necessarily imply causation. Establishing causality requires a thorough analysis to confirm that the drug itself is (or is not) responsible for the observed AE [1].

Subsequently, the focus shifts to examining the magnitude of the observed effect—essentially, the strength of the association between the drug and the AE. Equally important is assessing the level of variation in this observed effect, which has two main components: measurement error and natural variation. Measurement error arises from inaccuracies in ways in which data are collected, recorded, or analyzed. As pharmacovigilance systems become increasingly digitalized, challenges such as interoperability, data harmonization, and duplicate reporting arise. The fragmentation of healthcare databases across jurisdictions can lead to inconsistent AE capture, while errors in reporting, inconsistent measurement techniques, and duplicate reports can introduce artificial variability into signal detection analyses. Investigating and reducing measurement error, as far as possible, is key, as it can obscure the true relationship between the drug and the AE [11]. On the other hand, natural variation reflects the genuine differences among patients, driven by a wide array of attributes such as genetics, lifestyle, age, gender, co-existing health conditions, and other factors. Unlike measurement error, unbundling the sources of this natural variation can provide valuable insights into patient subgroups that may experience various levels of risk or benefit from a drug.

Interpretation: Given the Evidence, What Do We Now Believe?

Once data have been collected and analyzed, the next step is to interpret the results to form an opinion about the safety profile of the drug. The interpretation is done in the broader clinical and scientific context. Several factors might influence what, and how strongly, we believe about the relationship between a drug and an AE, which play a key role in determining causality. For instance, the confidence in the interpretation is reinforced if the AE is supported by multiple streams of evidence, such as positive rechallenge, findings from clinical trials, observational studies, and spontaneous reports, along with mechanistic studies that support biological plausibility as well as safety information within the same class of drugs [1]. However, as mentioned earlier, some findings may be counterintuitive or unexpected based on the current understanding of the drug's safety profile, which would require careful consideration and investigation. An unexpected AE might prompt a re-evaluation of prior information about the drug, particularly if supported by credible evidence, or it may lead to skepticism if the finding contradicts established knowledge without a clear explanation.

At this stage, safety professionals should engage relevant subject matter experts on the targeted patient population, as well as experts on the pertinent organ toxicity, to provide input, as applicable. By integrating these elements, safety professionals form a nuanced understanding about the safety of the drug. However, this understanding is not static; it evolves as new data become available and as our knowledge of the drug and its potential risks deepens. The goal is to construct a balanced view that reflects both the strengths and uncertainties of the evidence, guiding the decision-making process towards actions that best protect patients.

Action: Given the Evidence and Our Interpretation, What Should We Do?

The last step in the decision-making process is determining the course of action. The key question here is: What is the most proportionate action to take to protect patients while ensuring access to beneficial therapies? This step requires balancing the evidence with clinical judgment, ethical considerations, regulatory requirements, and optimally the perspectives of various stakeholders, including patients, healthcare providers, key opinion leaders, and regulatory authorities. Introducing BR considerations early in the discussion helps set the stage for understanding the challenges in decision making, particularly the subjective elements that influence regulatory and clinical actions. Structured BR frameworks are essential to guide such decisions and actions, as they provide a balanced approach to weighing the therapeutic benefits of a drug against its potential risks.6

When determining a course of action, key considerations include the level of uncertainty in the evidence and the threshold for intervention. This threshold guides whether safety data warrant regulatory or clinical action, yet there is no universal standard for its definition. Decisions vary based on risk magnitude and severity, evidence strength, and the applicable regulatory framework. Extent of risk tolerance also depends on the context of use, including the severity of the disease being treated, available treatment alternatives, considerations for establishing an adequate risk management strategy, and the magnitude of the drug’s benefits. For instance, an AE associated with a drug used for a common, non–life-threatening disease with many alternatives available may be viewed differently than the same AE associated with a drug used to treat severe or life-threatening disease where there is significant unmet need. Additionally, patients' lived experiences provide invaluable insights into the real-world impact of safety concerns. Their perspectives on BR trade-offs, treatment tolerability, and adherence patterns would offer a dimension of evidence. Incorporating patient perspectives into safety evaluations strengthens the relevance and applicability of regulatory actions [15]. Another potential factor is cost-effectiveness considerations, which might play a role in clinical decision making, particularly in the context of genetic testing for drug safety. While pharmacogenomic testing can help identify patients at risk for severe adverse drug reactions [16], its implementation depends on whether the benefits of preventing harm outweigh the costs of widespread screening. For example, genetic testing for HLA-B*58:01 before prescribing allopurinol helps prevent life-threatening hypersensitivity reactions, but its adoption is influenced by factors such as test affordability, healthcare resource allocation, and population-specific risk prevalence [17]. However, pharmaceutical companies and regulatory decisions are not based on cost effectiveness but rather on scientific evidence of safety and efficacy, leaving the consideration of economic factors to clinical practice and healthcare systems.

Possible actions might include close monitoring of a particular AE, updating product labeling including warning and precaution or contraindication statements, risk communication, e.g., issuing Dear Health Care Provider letter, restricting use in specific populations, conducting post-marketing safety studies, proposing risk-management plans, or, in extreme cases, withdrawing the drug from the market. Since the evidence is rarely unequivocal, decisions often require navigating ambiguity, where the level of concern must be carefully balanced with the practicalities of public health needs and regulatory requirements to make informed decisions that prioritize patient safety while acknowledging the inherent limitations in the data. Open communication between drug safety professionals in pharmaceutical companies and those working at regulatory authorities is essential in this step.

Conclusion

Navigating drug safety decisions requires integrating quantitative and qualitative dimensions while managing uncertainties in evidence and differing stakeholder perspectives. Divergent opinions often arise due to varying emphases on data points or thresholds for action, highlighting the need for multidisciplinary collaboration. A structured framework ensures comprehensive safety assessments by engaging experts across disciplines, particularly since drug development relies on evidence-based methodologies not typically covered except during postgraduate training. Without such integration, stakeholders may assign disproportionate weight to their own data, leading to misalignment.

As safety profiles evolve with new data, decision making must adapt to balance benefits and risks. Prioritizing open communication, transparency, and standardized interpretation methods can minimize discrepancies. By fostering collaboration, drug safety professionals and regulators can align on strategies that protect patients while maintaining access to essential therapies.

Declarations

Funding

Not applicable.

Conflicts of Interest

TH and SD are employed by Takeda and own stocks in the company.

Ethics Approval

Ethics approval was not required for this manuscript as it did not involve human subjects or identifiable personal data. The topic falls outside the scope of human research ethics requirements.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Availability of Data and Material

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Author Contributions

TH contributed to conception of the manuscript. TH and SD contributed to manuscript revision, read, and approved the submitted version.

Code Availability

Not applicable.