Real-World Data Analysis Applications

Struggles of doing data science in the real world 🤦: What do you do when there’s no A/B test but you still need insights? I recently faced that challenge (again): 👉 The growth team asked me to evaluate the impact of a new mobile app feature on conversions (a week after it launched) In the real world, data is messy, and A/B tests aren’t always an option. As a Data Scientist, you need to learn to be resourceful Here’s how I approached it: 1️⃣ Segmented analysis: I created pre- and post-launch groups based on user signup dates. 2️⃣ Exploratory data analysis (EDA): Visualized conversion trends, layering in cohort and seasonal comparisons. 3️⃣ Statistical testing: Ran an independent t-test to validate observed changes, carefully checking assumptions like normality and variance equality. Result? A clear signal of increased conversions on iOS, while Android showed minimal impact. 💡 Key takeaway: T-tests (or similar methods) can still deliver actionable insights outside traditional A/B testing, but validating assumptions and adding context is critical to making reliable conclusions. I broke down my full workflow and the lessons learned in my latest newsletter article (If you’re curious, check the link in the comments👇) What’s your go-to method for analyzing feature impacts without a perfect experimental setup?

Pharma is using electronic health records (#EHRs) and #claims data, but are missing the clearest picture of the patient. In the race to develop more targeted, effective therapies, pharma is making big investments in real-world data (#RWD). But one critical piece is still often missing from the puzzle: medical imaging. While claims data and EHRs provide a broad picture, imaging reveals what’s happening inside the patient, in ways other data types simply can’t. Here’s why real-world imaging data (#RWiD) matters more than ever for pharma: 🔍 Stronger Biomarker Discovery Imaging plays a crucial role in identifying and validating #biomarkers, especially in fields like #oncology, #neurology, and #cardiology. Access to real-world imaging helps researchers understand disease progression across diverse populations and clinical settings. ⏱ Faster, Smarter Clinical Trials Imaging endpoints are increasingly common in clinical trials. Using RWiD, sponsors can simulate trial cohorts, optimize inclusion criteria, and assess historical controls (or external control arms), saving both time and cost. 📊 Post-Market Evidence Generation Need to understand how a therapy is working in the real world, beyond the trial population? Imaging data helps track therapeutic response, safety signals, and long-term outcomes with anatomical and physiological detail. 🧠 Enabling AI and Radiomics From radiomics to imaging-based predictive models, RWiD is essential for training and validating the next generation of data-driven tools in drug development and personalized medicine. At Segmed, Inc., we’re making this data accessible: curated, standardized, and de-identified. So pharma innovators can build with confidence and clinical relevance. If you’re in drug development and haven’t yet explored real-world imaging data, now is the time. Because the body often tells a story that charts and codes alone can’t. Link: https://lnkd.in/gS8hiU4M #PharmaInnovation #RealWorldData #MedicalImaging #ClinicalResearch #Biomarkers #Radiomics #DrugDevelopment #AIinHealthcare #Segmed #EvidenceGeneration

Randomized clinical trials (#RCT) can be costly, lengthy, and limited in scope. MacLellan and colleagues developed an #AI-based method that uses real-world data (#RWD) to simulate clinical trials with diverse virtual patients, assessing treatment effects like GLP-1 receptor agonists for Type 2 diabetes. Their system reproduced outcomes from the LEAD-5 trial, showing potential to improve individualized care and inform policy, and may be useful for other diseases as well.

🚀 Stop Waiting For Perfect Data - Start Building Sophisticated Systems For The Real-World The computational revolution that transformed Google Maps, language processing, and autonomous systems didn't wait for perfect data. They built sophisticated systems to handle real-world complexity. The same can be true for Precision medicine. 📊 At Tempus, our systems process: - Petabytes of real-world multimodal patient data - Complex molecular signatures that can be reprocessed from raw files - source data to ensure data provenance for regulatory purposes 🔬 The Architecture: 1️⃣ Adaptive Validation: Maintains rigor while maximizing utility 2️⃣ Cross-Modal Verification: Automated cross-referencing of clinical, molecular, and imaging data 3️⃣ Transfer Learning: Derives insights from novel pattern combinations ⚡ Lessons from other industries: - Navigation systems: Process billions of messy GPS points for better predictions - Neural translation: Handles 100B+ daily words to radically improve accuracy and contextual insight 🎯 The Path Forward: Modern computational frameworks can handle both clinical trial precision AND real-world complexity. The architecture exists. The frameworks are established. The opportunity is now. I cover all this in my latest blog post: https://lnkd.in/gcdY5PUg #HealthcareInnovation #RealWorldData #Multimodal #AI #PrecisionMedicine #Oncology