4 Concept development and empirical data

4.1 Rationale

Traditional concept development in research ethics involves the systematic analysis and refinement of ethical principles that guide the conduct of research involving human and animal subjects. Rooted in philosophical inquiry and bioethics, this approach typically focuses on foundational concepts such as autonomy, beneficence, non-maleficence, and justice. Scholars use conceptual analysis to clarify the meaning, scope, and application of these principles, often drawing from case studies, historical precedents, and normative ethical theories. The goal is to establish clear, coherent, and universally applicable ethical standards that inform research policies, institutional review board (IRB) guidelines, and the ethical training of researchers.

In contrast to the traditional, normative approach in research ethics (which is largely conceptual and philosophical), integrating empirical knowledge—such as data from Health Technology Assessments (HTAs), clinical trial registries, or stakeholder surveys—is less common but increasingly recognized as valuable. While empirical methods are standard in clinical research for generating and refining questions, their use in research ethics is still emerging and sometimes viewed with caution.

This is partly because research ethics has traditionally focused on what ought to be done, rather than what is happening. However, empirical ethics—a growing interdisciplinary field—argues that understanding actual practices, stakeholder experiences, and outcomes can enrich ethical reflection and make it more context-sensitive. For example, analyzing HTA data might reveal patterns of exclusion, inefficiencies, or unintended harms in research practices that raise new ethical concerns or highlight neglected issues. Thus, while it may be less common, empirical approaches can play a crucial role in identifying ethically relevant gaps and informing the evolution of ethical standards in clinical research.

A strong example of such patterns becoming visible through a bird’s-eye view of clinical research—especially via analysis of large-scale data like from HTAs, trial registries, or systematic reviews—is the systematic underrepresentation of certain populations in clinical trials, such as: Example: Underrepresentation of Older Adults in Cancer Clinical Trials

Insights from large-scale empirical data analysis:

A: Bird’s-eye view through trial registries or meta-analyses shows that older adults (e.g., 65+) are consistently underrepresented in oncology trials, despite being the population most affected by cancer.
B: This trend emerges only when you aggregate and analyze data across many studies.
C: The reasons might include concerns about comorbidities, presumed frailty, or protocol exclusions—but these often go unchallenged until empirically documented.

From a conceptual ethics standpoint, justice and fairness demand equitable access to research participation and the benefits of scientific advancement. But this normative claim doesn’t immediately reveal whether inequities are actually occurring in practice.

Ethical implications:

Unintended harms: Treatments are approved based on data from younger, healthier populations, leading to less safe or less effective care for older adults.
Inefficiencies: Clinical research may not generalize well to the actual patient population, leading to wasted resources or repeat trials.
Injustice: Systematic exclusion undermines the ethical principle of equitable access and can exacerbate health disparities.

Without large-scale, empirical insight, these ethical issues remain diffuse or anecdotal. But with a data-informed view of the current state of research, they become actionable ethical concerns that can lead to refined inclusion criteria, new trial designs, or policy changes.

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