Numbers Don't Lie: Spotting Hidden Pitfalls in Clinical Trials

Clinical trials are notoriously costly gambles, frequently collapsing under the weight of biases and overlooked flaws. Our previous blog proposed Target Trial Emulation (TTE) as a forward-looking strategy to foresee and mitigate these risks before major investments are made. Yet, even with TTE, hidden biases—known as unmeasured confounding—pose significant and persistent threats. A recent study led by Gupta et al, with senior authorship from Miguel Hernán, a co-founder of Adigens Health, published in JAMA Network Open, highlights the critical importance of addressing these hidden pitfalls proactively through Quantitative Bias Analysis (QBA).

Beyond the Balance Sheet: Why CEOs Must Care About Hidden Biases

For CEOs and chief medical officers in biotechnology companies, addressing unmeasured biases is not merely an academic exercise—it is a strategic imperative. Decisions around clinical trial investments shape a firm’s trajectory, influence investor sentiment, and ultimately determine patient access to potentially life-changing therapies. Larger biopharma organisations, equipped with cross-functional expertise spanning clinical research, epidemiology, statistics, regulatory affairs, and real-world evidence, are particularly well-positioned to embed proactive strategies such as QBA into their decision-making frameworks. This integrated approach can turn risk management from a reactive headache into a proactive source of competitive advantage.

QBA serves as an advanced component within the broader TTE framework. While TTE provides a comprehensive structure to simulate ideal clinical trials using observational data, incorporating QBA further strengthens this methodology by explicitly quantifying hidden biases that might otherwise remain unnoticed. Rather than retrospectively noting potential flaws, QBA empowers trial designers to anticipate and mitigate these pitfalls upfront.

Turning Hidden Risks into Visible Opportunities

Consider, for example, a hypothetical clinical trial evaluating a new immunotherapy for advanced lung cancer. Traditionally, such trials might overlook factors like nutritional status or socioeconomic conditions—factors that could significantly alter treatment effectiveness but are rarely fully recorded in conventional datasets.

By embracing a pre-mortem mindset, the trial team would first convene a multidisciplinary group comprising clinicians, statisticians, epidemiologists, and regulatory experts. Their initial task: systematically identify potential hidden confounders through literature review and expert consensus. Nutritional status, for example, might emerge as crucial, given its documented impact on immune responses and patient survival outcomes.

Next, structured expert elicitation techniques, such as Delphi panels, would quantify these risks, providing clear numerical estimates of bias impacts. Early QBA simulations could then test how varying assumptions about nutritional deficiencies across the patient population might influence outcomes.

Armed with these insights at an early stage, trial designers could fine-tune their protocols accordingly. They might include mandatory baseline nutritional assessments, use nutritional status as a stratification criterion, or opt for endpoints less susceptible to nutritional variations.

Documenting these QBA-driven decisions explicitly in the trial protocol can significantly enhance transparency and regulatory credibility. Regulatory agencies, including the FDA, EMA, and NICE, increasingly seek evidence of rigorous pre-trial risk management. Proactive QBA delivers precisely this, transforming vague concerns about hidden biases into tangible, quantifiable analyses.

Yet, implementing QBA proactively is neither trivial nor effortless. It requires robust methodological expertise, careful data preparation, and disciplined collaboration across functions. Teams must also regularly reassess their assumptions throughout the trial to ensure ongoing accuracy.

Nevertheless, the potential rewards justify the effort. By bringing previously invisible risks into clear view, proactive QBA significantly enhances trial reliability and confidence. It doesn't eliminate uncertainty entirely, but it dramatically improves the odds of success.

Future blogs in this series will explore whether earlier adoption of this and similar strategies might have averted costly missteps in high-profile trials like those involving Biogen and Roche. Stay tuned as we continue to explore how smarter, data-driven methodologies could redefine clinical trial design from risky gambles to carefully calculated strategies.