QCast: Data-Driven Dialogue in Drug Development

AI and machine learning can help research teams make more informed clinical trial design decisions, from assessing protocol feasibility to identifying suitable patient populations. In this QCast episode, Jullia and Tom discuss how predictive modelling, natural language processing, and retrospective analysis can support study planning. Key Takeaways AI can help identify restrictive eligibility crit...

In this episode, Jullia and Tom discuss direct and indirect pathways in observational studies, with a focus on mediation analysis and what it can, and cannot, show. Using an ankylosing spondylitis example, they look at how fatigue, anxiety/depression, and work productivity loss can be mapped as exposure, mediator, and outcome, and why interpretation needs care when the data are observational. Key...

Clinical traceability is about being able to follow evidence from a reported result back to its source and understand the transformations, decisions, and records in between. In this QCast episode, co-hosts Jullia and Tom discuss how traceability can become harder when studies involve multiple systems, CROs, laboratories, and technology providers, and why retaining files alone is not enough. Key Ta...

Clinical trial trends matter most when they affect how studies are designed, delivered, and overseen. In this QCast episode, co-hosts Jullia and Tom discuss how decentralised models, AI, real-world evidence, adaptive designs, RBQM, outsourcing, and digital trial systems are changing practical decisions for sponsors and CROs. Key Takeaways New trial models can reduce participant burden, but they ma...

Tipping point analysis helps trial teams understand how strongly a study conclusion depends on assumptions about missing data. In this QCast episode, co-hosts Jullia and Tom discuss how the method relates to MAR and MNAR, how delta adjustments can identify a tipping point, and why the numerical result still needs clinical interpretation. Key Takeaways Tipping point analysis tests how far assumptio...

Audit trail review can involve far more than checking whether a system records changes. In this QCast episode, co-hosts Jullia and Tom discuss how trial teams can decide what needs review, where risk should shape scope and timing, and why unusual activity needs context before it becomes a finding. Key Takeaways Audit trail review should focus on relevant risks and questions rather than attempting...

Drug adherence and persistence measures built from claims or EHR databases depend as much on study definitions as recorded treatment events. In this QCast episode, co-hosts Jullia and Tom explain how adherence differs from persistence, what prescribing and dispensing data can and cannot show, and how MPR, PDC, treatment-gap rules and thresholds shape the findings. Key Takeaways Adherence describes...

In this QCast episode, Jullia and Tom discuss the role of data monitoring committees, or DMCs, in clinical trials. They explain how DMCs support participant safety and trial integrity, when independent monitoring may be proportionate, and why confidential interim data needs careful handling. Key Takeaways A DMC provides independent review of accumulating trial data, especially where interim safety...

In this QCast episode, Jullia and Tom discuss how oncology Phase 1 trial design has moved beyond simply identifying the maximum tolerated dose. They look at dose optimisation, escalation methods, simulation, data readiness, expansion cohorts, biomarkers and site capability, with a focus on how these elements affect real dose decisions. Key Takeaways Safety, exposure, pharmacodynamic information, e...

In this QCast episode, Jullia and Tom look at how clinical data quality is shaped from study set-up through collection, review and integration. They discuss why clear definitions, well-designed data flows and proportionate quality control matter, particularly when trial data move across multiple systems or vendors. Key Takeaways Clinical data should be judged by whether they are fit for the decisi...

In this QCast episode, Jullia and Tom discuss how sponsors can assess a study in difficulty, decide how to handle existing databases and data, manage handovers, and set realistic expectations for recovery. Key Takeaways A rescue assessment should identify the root cause before corrective work begins. Database rescue does not always require a rebuild. Recovery plans need to reflect what can genuine...

In this QCast episode, Jullia and Tom examine how clinical trial performance metrics can support better oversight without creating unnecessary reporting burden. They explain the difference between metrics and key performance indicators, why ownership is often shared across sponsors, CROs and sites, and how leading and lagging indicators support different decisions. Key Takeaways Select a focused s...

In this QCast episode, co-hosts Jullia and Tom explain how priors, posterior probabilities and predictive probabilities support clinical decisions. They also discuss external data borrowing, adaptive trial features, simulation, regulatory expectations and the operational work needed to deliver interim decisions without weakening trial credibility. Key Takeaways A Bayesian design combines prior inf...

In this QCast episode, Jullia and Tom examine how adaptive trial design allows pre-specified changes to a clinical trial using accumulating interim data. They look at common adaptations, including early stopping, sample size re-estimation and dose selection, while also addressing the statistical, operational and governance controls needed to protect trial integrity. Key Takeaways Adaptive changes...

In this QCast episode, Jullia and Tom look at SAS vs R vs Python through the lens of clinical trial delivery. Rather than treating the choice as a programming preference, they focus on where each tool tends to fit: SAS for established clinical reporting, R for statistical analysis and visualisation, and Python for automation, integration, and data workflows. Key Takeaways SAS, R, and Python can al...

In this QCast episode, Jullia and Tom discuss decentralised clinical trials and what they mean for clinical development teams in practice. The episode looks at decentralisation as a spectrum of remote, local, digital, and site-based activity, rather than a move to fully virtual trials. Key Takeaways Decentralised clinical trials work best when remote and local activities are selected deliberately....

In this QCast episode, Jullia and Tom discuss the Global Statistical Test for multiple endpoints analysis and why it can be useful when a clinical trial needs to assess more than one outcome at the same time. They cover the problem of testing endpoints separately, how GST supports an overall assessment of treatment effect, and why endpoint choice, correlation, missing data, and clinical interpreta...

In this QCast episode, Jullia and Tom discuss CDASH standards and conformance in clinical data management, with a focus on where CDASH sits in the clinical data flow and why it matters before data reaches SDTM. The episode looks at CRF and eCRF design, naming conventions, metadata, controlled terminology, ePRO and eSource data, and the practical problems that can appear when collection standards a...

In this QCast episode, Jullia and Tom discuss multi-omics and its application in personalised medicine studies. The episode explains how genomic, transcriptomic, proteomic, metabolomic and other biological data layers can be analysed together to better understand disease, treatment response and patient subgroups. Key Takeaways Multi-omics brings together several types of biological information to...

In this QCast episode, Jullia and Tom discuss BIMO listings and their role in FDA inspection readiness. They explain how Bioresearch Monitoring looks at clinical trial conduct, participant protection, data credibility, and the records that support regulatory submissions. The episode also covers common misconceptions, including the idea that BIMO is only a site-level concern, and shows how sponsors...

In this QCast episode, Jullia and Tom discuss efficacy endpoints in oncology clinical trials and why endpoint choice shapes how treatment benefit is assessed. They look at overall survival, progression-free survival, objective response rate, disease-free survival, event-free survival, symptom endpoints, and quality-of-life measures. Key Takeaways Overall survival remains one of the clearest measur...

In this QCast episode, Jullia and Tom discuss how ADaM datasets are created for pharmacokinetic, or PK, analysis. They look at the role of ADPC for concentration data and ADPP for derived PK parameters, including how these datasets relate to SDTM PC and PP. The episode also covers why timing, dosing information, analysis values, flags and study-specific rules matter when preparing PK data for repo...

In this QCast episode, Jullia and Tom discuss clinical data review as an active part of study oversight, rather than a late-stage clean-up exercise. They explore how timely, connected review supports subject safety, data quality, medical review and confident decision-making, especially as trial data now comes from EDC, ePRO, eCOA, labs, imaging, biomarkers, wearables and other sources. Key Takeawa...

In this QCast episode, Jullia and Tom discuss clinical trial management systems and their role in keeping clinical studies under control. They explain how CTMS differs from EDC, why operational visibility matters across recruitment, monitoring, site activity and documentation, and how a well-used system can support risk-based oversight, audit trails and better decision-making across sponsors, CROs...

In this QCast episode, co-hosts Jullia and Tom explore clinical trial simulation and how it supports better study design decisions before a trial begins. They explain how simulation uses statistical and mathematical models to test plausible study scenarios, rather than relying on a single fixed assumption. Key Takeaways Simulation helps teams see how a study design may behave under different assum...