The More Data You Have, The Further You Are From The Truth- Big Data Paradox in Medicine
Dr Pavlos Msaouel: https://faculty.mdanderson.org/profiles/pavlos_msaouel.html 0:00 Pretext and context MD Anderson Cancer Center 1:42 The Early Experiences That Shaped Dr Msaouel 3:39 The System of Cancer Research In the United States 5:25 FDA Drug Approvals and Special Designations For Oncology 11:09 A trade-off in our current system 12:35 Whoops 😞 I misspoke 14:35 Randomized trials are not supposed to be representative of a population 18:10 Inference for the treatment of Katie Coleman (metastatic oncocytoma) 25:12 It is much more valuable to refute hypotheses than to confirm them 28:05 All Models Are Wrong, But Some Are Useful 30:05 Recasting the Bias-Variance Trade-off as the Patient Relevance-Robustness Trade-off 33:38 High Relevance, Low Robustness: Subjective Bayes 34:46 High Robustness, Low Relevance: Pure Randomization 41:47 Signal To Noise Ratio 45:49 The Big Data Paradox 53:41 How the Big Data Paradox fits in with Katie Coleman's case 54:33 How the Big Data Paradox fits in with other branches of medicine 57:54 How do we rectify subjectivity in The Truth? 01:00:43 How do we address the problems introduced by the Big Data Paradox? 01:04:21 Duality in nature and a Twitter argument re: the Evidence-Based Deep Medicine Iceberg 01:07:03 Why the ideas coming from MD Anderson are coming from MD Anderson 01:09:39 Where you can find Dr Msaouel Dr Msaouel: https://twitter.com/PavlosMsaouel Heme Review: https://twitter.com/HemeReview
Produced by @chubbyemu Production Assistant: Nick Brown Secret channel: @BigEmus Cancer Patient Speaks With Her Oncologist ► https://youtu.be/Nddft_nc3Yc A Woman Had A Headache Lasting 3 Days (Katie's case) ► https://www.youtu.be/i9fLEvgZzRE References: Stephen Senn. Statistical Issues in Drug Development. Why representativeness should be avoided. https://academic.oup.com/ije/article/42/4/1012/656034 Commentary: Representativeness is usually not necessary and often should be avoided. https://academic.oup.com/ije/article/42/4/1018/658638 Impervious to Randomness: Confounding and Selection Biases in Randomized Clinical Trials. https://www.tandfonline.com/doi/full/10.1080/07357907.2021.1974030 A Causal Framework for Making Individualized Treatment Decisions in Oncology. https://www.mdpi.com/2072-6694/14/16/3923 Causal Diagram Techniques for Urologic Oncology Research. https://www.sciencedirect.com/science/article/pii/S1558767320301932 Causal inference and the data-fusion problem. https://www.pnas.org/doi/10.1073/pnas.1510507113 Causal Inference: What If. Miguel A Hernán and James M. Robins. https://www.hsph.harvard.edu/miguel-hernan/wp-content/uploads/sites/1268/2023/04/hernanrobins_WhatIf_31mar23.pdf There is Individualized Treatment. Why Not Individualized Inference? https://arxiv.org/abs/1510.08539 Unrepresentative big surveys significantly overestimated US vaccine uptake. https://www.nature.com/articles/s41586-021-04198-4 Unrepresentative Big Surveys Significantly Overestimate US Uptake https://arxiv.org/pdf/2106.05818.pdf Statistical Modeling: The Three Cultures. https://hdsr.mitpress.mit.edu/pub/uo4hjcx6/release/1 How “centaur AI” will radically reshape the future of healthcare. https://bigthink.com/health/how-centaur-ai-will-radically-reshape-the-future-of-healthcare/
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