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 on gaps. 
•  Use principled methods such as mixed models and multiple imputation, consider pattern-mixture approaches, avoid last observation carried forward. 
•  Demonstrate robustness through targeted sensitivity analyses, tipping-point checks, and clear summaries of missingness and reasons. 

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📝 Episode Notes & Transcript: Find show notes, resources, and a full transcript at quanticate.com/podcast.
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About Quanticate

Founded in 1994, Quanticate is a biometrics-focused CRO with over 30 years' experience delivering expert clinical and post-marketing data services worldwide. Specialising in the collection, management, standardisation, analysis, and reporting of data, we support pharma and biotech companies globally with high-quality solutions for clinical development. We specialise in:

• Clinical Data Capture & Management
• Biostatistics & Statistical Consultancy
• Statistical Programming & PK/PD Analysis
• Medical Writing & Pharmacovigilance
• Regulatory & Submission Support
• AI & Automation
• Post-Marketing Safety & Real-World Data

Quanticate is committed to delivering expertise and solutions that drive the success of clinical development worldwide.