– Written by Venkatesan Balu, Director – Global Data Sciences, Navitas Life Sciences

Quality by Design (QbD) is a systematic approach to product development, particularly in pharmaceuticals and clinical trials, aimed at ensuring that quality is built into the design, conduct and monitoring of a study from the outset. The ICH E6(R3) Good Clinical Practice (GCP) guideline introduces a framework that promotes flexibility in study conduct, modernisation of trial approaches, and greater efficiency in clinical trial execution. Statistical elements are central to achieving these objectives.

In this context, greater emphasis is placed on the QbD principle of planning for quality –from the earliest stages of study design through to regulatory submission. A critical factor in this process is the effective use of statistical methods to identify and monitor the key factors influencing trial quality. This blog post will explore how and where statistics play a vital role in implementing QbD effectively, highlight their significance, and examine the implications for all stakeholders in clinical research, including researchers, sponsors, regulators and patients.

Quality by Design (QbD): A Conceptual Overview

With the growth of clinical trials and the adoption of new practices, Quality by Design (QbD) has become an integral framework in clinical research. It emphasises proactive planning, stakeholder engagement and continuous improvement, ultimately leading to trials that are safer, more efficient and of higher quality. A key principle of QbD is building quality into both study design and execution, focusing on proactively preventing errors rather than generating deviations and implementing corrective and preventive actions (CAPA) after issues occur.

Here are some insights into QbD In clinical trials:

Critical-to-quality (CTQ) factors

• A primary focus in QbD is early identification of CTQ factors rather than corrective action. Elements of CTQ are critical for protecting patient safety, maintaining data integrity and for trial success. These help minimise inadequacies and prioritise the prevention of errors that could change trial outcomes.
• QbD uses statistical methods such as Design of Experiments (DoEs) to identify the most significant factors affecting the trial’s outcome. This helps scrutinise CtQs that directly impact on the reliability and validity of the trial result.
• Risk-scoring algorithms are used to quantify and prioritise these CtQ elements based on impact and probability.

Involvement of Stakeholders

• With the increasing need for real-world data and central monitoring, it is essential to engage stakeholders at every level of the study, including clinical investigators, site staff, medical monitors, data managers and statisticians, to ensure all critical elements are addressed during study design development.
• Putting these external controls in place allows us to take proactive steps from the start of the study so the trial runs smoothly while protecting data integrity, participant safety and regulatory compliance. By building quality into the process from the onset rather than identifying and fixing issues later, we can make trials more efficient. This approach also helps reduce rework and deviations.
• Using quantitative models (risk matrices, Bayesian networks) plus control charts and trend analysis for continuous RBM, and keeping risk identification data-driven using indicators such as SDV rates and protocol deviations, surfaces early signals.

Using statistical methods to guide decisions

• An important aspect of any study is decision-making regarding trial outcomes, where understanding statistics and interpreting results play a vital role. Within the QbD framework, statistics take on a proactive role, enabling teams to make informed decisions during trial design, conduct and analysis through various approaches. Statistical imputation techniques, anomaly detection and pattern analysis ensure robust data handling, while statistical process controls (SPCs) monitor site performance, data entry timelines and protocol adherence. These are further supported by robust data governance systems and validated statistical tools.
• For example, converting a study to an adaptive design (e.g. group sequential methods, sample size re-estimation) uses interim data to modify trial conduct without compromising the validity or integrity of the overall study. Adaptive designs and Bayesian approaches not only facilitate continuous learning and decision-making under uncertainty but also underscore the value of flexible designs in ensuring the robustness and efficiency of statistical analyses.

Continuous Improvement, Learning and Regulatory Requirement

• Ensures alignment between study hypothesis or objectives, design, analysis and interpretation. Clarifies treatment effect through predefined estimands, accounting for intercurrent events.
• Builds on ICH E9(R1), integrating estimand principles into trial planning to improve transparency and interpretability. One of QbD’s strengths is its emphasis on lessons learned. This concept fosters ongoing assessment and refinement of trial design and execution, allowing organisations to apply insights from past studies to improve future trials.

Implementation Challenges and Opportunities

Challenges

• There's a growing need for cross-functional teams, like clinicians, data managers, and operations staff, to become more comfortable with advanced statistical concepts. That’s not always easy, especially when everyone comes from different technical backgrounds.
• Integrating real-time analytics into day-to-day clinical operations is promising, but it can be complex. It requires the right tools, workflows, and mindset to act on insights quickly.
• As trial designs evolve, especially with adaptive or Bayesian methods, the regulatory landscape can feel unclear. Sponsors may hesitate to adopt innovative approaches without solid guidance.

Opportunities

• Smarter, more focused data collection can make trials run faster and cost less, without sacrificing quality.
• Using clear, transparent statistical approaches builds trust in the results, not just within teams but also with regulators and stakeholders.
• As regulators increasingly support modern trial methods, there's a real opportunity for sponsors to align their strategies and work more efficiently within those evolving expectations.

ICH E6(R3) sets a new standard for clinical trial conduct by embracing Quality by Design and integrating statistical thinking into every stage of the study lifecycle. From risk assessment to adaptive designs, the statistical elements within QbD are essential. Sponsors and investigators must invest in statistical expertise and infrastructure to meet the expectations of this modernized GCP framework and to deliver trials that are compliant, efficient, ethical, and scientifically robust.

References

1. ICH E6(R3) Draft Guideline: Good Clinical Practice
2. ICH E9(R1): Addendum on Estimands and Sensitivity Analysis
3. EMA Reflection Paper on Risk-Based Quality Management in Clinical Trials
4. FDA Guidance on Adaptive Design Clinical Trials
5. How Biostatisticians Use Advances in Technology to Reshape Risk-Based Monitoring
6. How Open Source Data Analytics Tools Are Revolutionizing Data Science - Blog - Navitas Life Sciences
7. Insights on Maximizing the Value of Clinical Registries and Accessing Real-World Data and Real-World Evidence - Blog - Navitas Life Sciences
8. Key Insights on Maintaining Digital Resilience and Data Security - Blog - Navitas Life Sciences

Author Bio

Venkatesan Balu is Director, Global Data Sciences, Navitas Life Sciences, with 15+ years of experience in the biostatistics domain, and in Phase I to Phase IV clinical trials across various therapeutic areas, BABE and PK studies. He has invaluable expertise in providing inputs to study design, sample size, SAP, outlier evaluation, interim analysis, complex statistical evaluation and model selection, and regulatory requirements. Venkatesan is a technical leader in drug development strategy, adaptive design, portfolio optimisation, and decision-making in clinical trials.