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QCast: Data-Driven Dialogue in Drug Development
Quanticate
27 episodes
1 week ago
In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...
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Life Sciences
Science
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All content for QCast: Data-Driven Dialogue in Drug Development is the property of Quanticate and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...
Show more...
Life Sciences
Science
Episodes (20/27)
QCast: Data-Driven Dialogue in Drug Development
Episode 27: Machine Learning in the Pharmaceutical Industry
In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...
Show more...
1 week ago
10 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 26: Medical Coding in Clinical Data Management
In this QCast episode, Jullia and Tom explore medical coding in clinical data management, clarifying how clinical narratives are translated into standardised terminology, why consistent coding underpins safety review and regulatory confidence, and how coding decisions shape analysis-ready datasets across a study’s lifecycle. Key Takeaways Understand how medical coding aligns adverse events, medical history, and medications using controlled dictionaries to support reliable aggregation and inte...
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1 week ago
9 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 25: The Proportional Odds Assumption in Clinical Trials
In this QCast episode, Jullia and Tom unpack the proportional odds assumption in ordinal logistic regression, explaining what it means in practice, why it matters for ordinal endpoints in clinical trials, and how to diagnose and handle violations without losing the value of ordered scales. Key Takeaways Understand that proportional odds implies a single treatment effect across all cut points of an ordered endpoint, enabling an efficient summary of benefit.Examine outcome distributions, use fo...
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3 weeks ago
11 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 24: Health Economics and Outcomes Research (HEOR)
In this QCast episode, Jullia and Tom explore health economics and outcomes research, explaining how outcomes research looks beyond controlled trials to real world care and how HEOR evidence informs pricing, reimbursement, and access decisions. Key Takeaways Anchor HEOR work to a clear decision question around value, pricing, access, or policy.Match methods to the data: define cohorts carefully, respect real world data limits, and invest in strong database infrastructure and quality control.P...
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4 weeks ago
10 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 23: Query Management in Clinical Trials
In this QCast episode, Jullia and Tom unpack query management in clinical trials, outlining what queries are, how they move from detection to closure, and how thoughtful design, clear communication, and focused metrics turn them from administrative noise into a practical quality tool that protects data integrity, timelines, and inspection readiness. Key Takeaways Define query management as part of study design, not just an operational clean up activity.Use clear, targeted edit checks and conc...
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1 month ago
10 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 22: Quality Tolerance Limits in Clinical Trials
In this QCast episode, Jullia and Tom explore quality tolerance limits in clinical trials, explaining what they are, how they support risk based quality management, and how to define, monitor and govern them so they genuinely protect participant safety and trial integrity rather than becoming a tick box exercise. Key Takeaways Define QTLs from the risk assessment, link them to truly critical to quality parameters.Set a small number of clear, study level limits, document rationale and calculat...
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1 month ago
9 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 21: The Role of Reconciliation in Clinical Data Management
In this QCast episode, Jullia and Tom unpack data reconciliation in clinical data management — what it is, why it underpins data integrity and safety oversight, and how to plan, run, and document it so analyses reflect the truth across clinical, safety, and vendor systems. Key Takeaways Map streams early, set owners and cadence, lock vendor specifications.Prioritise high-risk data such as safety and endpoints, monitor trends.Standardise units, ranges and identifiers, enforce chang...
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1 month ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 20: Missing Data in Clinical Trials
In this QCast episode, Jullia and Tom unpack missing data in clinical trials — why it biases effect estimates, how the estimand framework drives prevention and analysis choices, and what good sensitivity work and reporting look like for credible, inspection-ready results. Key Takeaways Define estimands and intercurrent-event strategies, then align follow-up and data collection. Prevent over correct with simpler schedules, remote options, continued follow-up, and early action ...
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1 month ago
10 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 19: Data Validation in Clinical Data Management
In this QCast episode, Jullia and Tom demystify data validation in clinical data management — how a lean, risk-based approach safeguards data integrity and supports confident database lock, inspection readiness, and downstream SDTM and ADaM deliverables. Key Takeaways Prioritise a lean Data Validation Plan that targets critical data, tunes top checks, controls changes and captures test evidence.Treat third-party data as first-class by standardising units and timestamps, validating impor...
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2 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 18: Real World Evidence in Clinical Trials
In this QCast episode, Jullia and Tom unpack real-world evidence in modern drug development — what distinguishes real-world data from the evidence it enables, and how to apply it alongside randomised trials. Key Takeaways Start with the decision, emulate the target trial, and justify methods with clear diagnostics.Make data fitness and governance non-negotiable: provenance, completeness, traceability, privacy, and compliant linkage.Use RWE to complement trials; for external controls, ensure c...
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2 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 17: Dose Expansion Phases in Oncology Trials
In this QCast episode, Jullia and Tom explore the dose expansion phase of phase one oncology trials — the critical bridge between dose finding and proof of concept. They discuss how expansion cohorts confirm safety, explore early efficacy signals, and refine dose and schedule decisions, along with best practices for design, governance, and regulatory alignment that set the stage for a successful phase two. Key Takeaways Treat the dose expansion phase as a structured bridge between dose ...
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2 months ago
13 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 16: Statistical Analysis Plans (SAPs)
In this QCast episode, Jullia and Tom unpack Statistical Analysis Plans (SAPs) — the blueprints that define how clinical trial data are turned into evidence. They explore what a SAP includes, how it links to the protocol and estimand framework, and the controls that keep analyses credible, reproducible, and inspection-ready. Key Takeaways Treat the SAP as the bridge between protocol intent and statistical execution. Anchor all analyses to the estimand framework to maint...
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2 months ago
13 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 15: CRF Annotation in Clinical Trials
In this QCast episode, Jullia and Tom demystify case report form annotation in clinical trials. They explain what an annotated CRF is, why it is central to traceability and compliance, and how teams use CDASH at collection and SDTM at tabulation to keep mappings clean. Key Takeaways Treat the annotated CRF as the contract between collection and analysis to ensure end-to-end traceability.Start from CDASH templates and map cleanly to SDTM; extend standards only with clear rationale.Lock units a...
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3 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 14: Using R Programming for Clinical Trial Data Analysis
In this QCast episode, Jullia and Tom break down how the R programming language is being used for clinical trial data analysis. They explore its role across the trial lifecycle, from planning and cleaning through efficacy, safety, and reporting. Key Takeaways Use the language for simulations, cleaning, modelling, safety, and reporting across the trial lifecycle.Build “reporting datasets” to simplify creation of inspection-ready tables and figures.Validate processes with pinned versions, docum...
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3 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 13: Estimands in Clinical Trials
In this QCast episode, Jullia and Tom demystify estimands and show how a clear question, defined up front, sharpens trial design, data capture, and analysis. They unpack the four elements, explain practical strategies for handling intercurrent events, and discuss picking summary measures that clinicians and payers can interpret. Key Takeaways Define the estimand early and in plain language; align objectives, CRFs, and the analysis plan to the same question.Match intercurrent event strat...
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3 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 12: AI and Automation in Clinical Data Management
In this QCast episode, Jullia and Tom explain how AI and automation make clinical data work smoother from smarter screening and electronic consent to streaming device data, auto-summarising notes, targeted monitoring, and faster reports. They outline the basics for linking core trial systems, clarify what regulators expect on validation and audit trails, and share a simple first-year plan with quick wins and common traps to avoid. Key Takeaways Start small with a high-value workflow, define s...
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3 months ago
13 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 11: A Guide to Virtual Clinical Trials
In this QCast, Jullia and Tom explore the rise of virtual clinical trials—what they are, when they work best, and how to design them without compromising safety or data quality. They cover the regulatory expectations across the US, EU, and UK, walk through a participant’s journey in a decentralised model, explain how oversight, technology, and logistics must align for success, highlight pitfalls that commonly derail virtual studies, and share the practical safeguards that make them work. Key ...
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4 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 10: Managing Clinical Data Challenges with a DSMB
In this QCast, Jullia and Tom uncover how Data and Safety Monitoring Boards (DSMBs) keep trials safe and on track—what they are, when you need one, and how to avoid data pitfalls at interim looks. They unpack the DSMB charter versus the Data Safety Monitoring Plan, translate stopping boundaries into plain English, and share practical tactics for clean, blinded packages that enable confident decisions. Key Takeaways Proportional oversight keeps risk in check; higher-risk or adaptive tria...
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4 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 9: Randomisation in Clinical Trials
In this QCast episode, Jullia and Tom break down randomisation in clinical trials—why it matters, how different methods work, and what safeguards keep allocations fair and consistent across sites. They cover simple, block, and stratified randomisation, touch on unequal allocation and adaptive designs, and share a practical case study from a 5-arm trial. Key Takeaways Randomisation reduces bias, supports blinding, and strengthens trial validity.Simple, block, and stratified methods suit ...
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4 months ago
12 minutes

QCast: Data-Driven Dialogue in Drug Development
Episode 8: The Database Lock Process in Clinical Trials
In this QCast episode, join co-hosts Jullia and Tom as they unpack the database lock process in clinical trials. You’ll get a clear explanation of what a lock is, why it matters, and the difference between soft and hard locks. They’ll walk through the planning steps that keep the final weeks on track, the habits that reduce last-minute issues, and the cross-functional coordination needed to reach lock on time. Key Takeaways Database lock is a formal milestone where the trial dataset becomes c...
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4 months ago
11 minutes

QCast: Data-Driven Dialogue in Drug Development
In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...