Cancer Blood Tests Part 2: The clinical trial episode artwork

EPISODE · Jun 29, 2026 · 57 MIN

Cancer Blood Tests Part 2: The clinical trial

from Normal Curves: Sexy Science, Serious Statistics · host Regina Nuzzo and Kristin Sainani

How do you decide whether a clinical trial “worked”? In Part 2 of our Galleri series, we examine the landmark randomized trial of a blood test designed to detect more than 50 cancers. We explore why different outcome measures led to dramatically different headlines, discuss primary versus secondary outcomes, pre-registration, hierarchical testing, and post hoc analyses, and explain why mortality remains the outcome everyone is waiting for. Along the way, we uncover a statistical mystery involving dozens of missing cancers and discover how a little arithmetic can sometimes reveal more than a press release.Statistical topicscancer screeningexploratory analyseshierarchical testingmissing datamultiple testingoutcome measurespost hoc analysespre-registrationprimary and secondary outcomesrandomized clinical trialsscreening testsMethodologic Morals“When the simple numbers don't add up, pay attention. The arithmetic may be trying to tell you something.”“The first question should not be, did it work? It should be, what counts as success?”ReferencesGiridhar KV, et al. Safety and performance results from PATHFINDER 2, a registrational study of a multi-cancer early detection test in an intended-use population. Presented at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting. May 2026.Hubbell E, Clarke CA, Aravanis AM, Berg CD. Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test. Cancer Epidemiol Biomarkers Prev. 2021;30(3):460-468. doi:10.1158/1055-9965.EPI-20-1134Neal RD, Johnson P, Clarke CA, et al. Cell-Free DNA-Based Multi-Cancer Early Detection Test in an Asymptomatic Screening Population (NHS-Galleri): Design of a Pragmatic, Prospective Randomised Controlled Trial. Cancers (Basel). 2022;14(19):4818. Published 2022 Oct 1. doi:10.3390/cancers14194818ASCO slides: https://grail.com/wp-content/uploads/2026/05/Swanton_ASCO-2026_NHS-Galleri_FINAL-Slides-05.26.2026.pdfUK registry protocol:  https://www.isrctn.com/ISRCTN91431511 Clinicaltrials.gov protocol: https://clinicaltrials.gov/study/NCT05611632 Common biases in cancer screening studiesCancer screening studies are subject to several well-known biases that can make a screening test appear more effective than it actually is. Three of the most important are:Lead-time bias: Screening advances the time of diagnosis, making survival from diagnosis appear longer even if the patient's lifespan is unchanged. For example, if a screening test detects a Stage II cancer at age 60 that otherwise would have been diagnosed because of symptoms at age 62, but the patient dies at age 68 regardless, survival from diagnosis appears to increase from 6 years to 8 years even though the patient did not live any longer. Length bias: Screening preferentially detects slower-growing, less aggressive cancers because they remain detectable for longer than fast-growing cancers. For example, a slow-growing cancer that remains in Stage I for 5 years is much more likely to be found by screening than an aggressive cancer that progresses to symptoms within months. This can make screened patients appear to have better survival simply because screening preferentially found the less aggressive cancers. Overdiagnosis: Screening detects cancers that would never have caused symptoms or death during a person's lifetime, leading to unnecessary diagnosis and treatment. For example, a screening test may detect a very slow-growing prostate or thyroid cancer in an older adult that would never have become clinically important if it had remained undiscovered. Kristin and Regina’s online courses: Demystifying Data: A Modern Approach to Statistical Understanding  Clinical Trials: Design, Strategy, and Analysis Medical Statistics Certificate Program  Writing in the Sciences Epidemiology and Clinical Research Graduate Certificate Program Programs that we teach in:Epidemiology and Clinical Research Graduate Certificate Program Find us on:Kristin -  LinkedIn & Twitter/XRegina - LinkedIn & ReginaNuzzo.com(00:00) - Intro (03:39) - The Claim: Not Ready for Primetime (03:58) - Trial Design: 142,000 Participants (07:50) - The Primary Outcome Problem (20:29) - The Primary Endpoint: Complete Miss (22:14) - Three Arguments for the Defense (28:29) - - Statistical Sleuthing: Missing Cancers (41:14) - - The Stage Shift Argument (50:30) - - Rating the Claim

How do you decide whether a clinical trial “worked”? In Part 2 of our Galleri series, we examine the landmark randomized trial of a blood test designed to detect more than 50 cancers. We explore why different outcome measures led to dramatically different headlines, discuss primary versus secondary outcomes, pre-registration, hierarchical testing, and post hoc analyses, and explain why mortality remains the outcome everyone is waiting for. Along the way, we uncover a statistical mystery involving dozens of missing cancers and discover how a little arithmetic can sometimes reveal more than a press release.Statistical topicscancer screeningexploratory analyseshierarchical testingmissing datamultiple testingoutcome measurespost hoc analysespre-registrationprimary and secondary outcomesrandomized clinical trialsscreening testsMethodologic Morals“When the simple numbers don't add up, pay attention. The arithmetic may be trying to tell you something.”“The first question should not be, did it work? It should be, what counts as success?”ReferencesGiridhar KV, et al. Safety and performance results from PATHFINDER 2, a registrational study of a multi-cancer early detection test in an intended-use population. Presented at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting. May 2026.Hubbell E, Clarke CA, Aravanis AM, Berg CD. Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test. Cancer Epidemiol Biomarkers Prev. 2021;30(3):460-468. doi:10.1158/1055-9965.EPI-20-1134Neal RD, Johnson P, Clarke CA, et al. Cell-Free DNA-Based Multi-Cancer Early Detection Test in an Asymptomatic Screening Population (NHS-Galleri): Design of a Pragmatic, Prospective Randomised Controlled Trial. Cancers (Basel). 2022;14(19):4818. Published 2022 Oct 1. doi:10.3390/cancers14194818ASCO slides: https://grail.com/wp-content/uploads/2026/05/Swanton_ASCO-2026_NHS-Galleri_FINAL-Slides-05.26.2026.pdfUK registry protocol:  https://www.isrctn.com/ISRCTN91431511 Clinicaltrials.gov protocol: https://clinicaltrials.gov/study/NCT05611632 Common biases in cancer screening studiesCancer screening studies are subject to several well-known biases that can make a screening test appear more effective than it actually is. Three of the most important are:Lead-time bias: Screening advances the time of diagnosis, making survival from diagnosis appear longer even if the patient's lifespan is unchanged. For example, if a screening test detects a Stage II cancer at age 60 that otherwise would have been diagnosed because of symptoms at age 62, but the patient dies at age 68 regardless, survival from diagnosis appears to increase from 6 years to 8 years even though the patient did not live any longer. Length bias: Screening preferentially detects slower-growing, less aggressive cancers because they remain detectable for longer than fast-growing cancers. For example, a slow-growing cancer that remains in Stage I for 5 years is much more likely to be found by screening than an aggressive cancer that progresses to symptoms within months. This can make screened patients appear to have better survival simply because screening preferentially found the less aggressive cancers. Overdiagnosis: Screening detects cancers that would never have caused symptoms or death during a person's lifetime, leading to unnecessary diagnosis and treatment. For example, a screening test may detect a very slow-growing prostate or thyroid cancer in an older adult that would never have become clinically important if it had remained undiscovered. Kristin and Regina’s online courses: Demystifying Data: A Modern Approach to Statistical Understanding  Clinical Trials: Design, Strategy, and Analysis Medical Statistics Certificate Program  Writing in the Sciences Epidemiology and Clinical Research Graduate Certificate Program Programs that we teach in:Epidemiology and Clinical Research Graduate Certificate Program Find us on:Kristin -  LinkedIn & Twitter/XRegina - LinkedIn & ReginaNuzzo.com(00:00) - Intro (03:39) - The Claim: Not Ready for Primetime (03:58) - Trial Design: 142,000 Participants (07:50) - The Primary Outcome Problem (20:29) - The Primary Endpoint: Complete Miss (22:14) - Three Arguments for the Defense (28:29) - - Statistical Sleuthing: Missing Cancers (41:14) - - The Stage Shift Argument (50:30) - - Rating the Claim

NOW PLAYING

Cancer Blood Tests Part 2: The clinical trial

0:00 57:53

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

French Your Way Jessica: Native French teacher founder of French Your Way Boost your French listening skills and test your comprehension with this one of a kind series of podcasts. Get the chance to listen to a real conversation between native speakers talking at normal speed AND customise your learning experience through carefully designed sets of questions (2 levels of difficulty) available for download at www.frenchvoicespodcast.com. All interviews also come with the transcript. French teacher Jessica interviews native speakers of French from around the world who share a bit of their life and passion. Where else would you meet in one same place a French yoga teacher based in Melbourne, a soap manufacturer from Provence, or a couple cycling around the world? The Lee Olsen Show Lee Olsen CJF I want to help you improve all areas of your life by 3 types of podcasts!👉Blood, Sweat & Blessings-Interviews of normal people that have achieved BIG things!👉Series!!! For Love of the Horse- Brad Jackman DVM & Lee Olsen CJF, how to help your horse!👉Business Tips- Proven Life Changing Business Strategies with Lee Olsen Elevatin' The GetRight Spot & The Love Algorithm Elevatin' The GetRight Spot & The Love Algorithm A podcast that expresses the journey of taking ideas and turning them into a successful website and business. Using an ideology, philosophy and mental science as motivation, we shall Elevate Bodybyloud! and The GetRight Spot. We also inspire everyone to elevate their lives and go after their dreams, desires., and abundance. The Health Odyssey: Navigating Tomorrow's Medicine Podcast Welcome to 'The Health Odyssey: Navigating Tomorrow's Medicine,' where we embark on an adventurous journey through the ever-evolving world of healthcare. Each episode is like a treasure map, guiding you through the rich tapestry of ancient healing arts mixed with futuristic tech wizardry. We’ll chat about the wild west of health data privacy, the corporate giants reshaping our care, and the mind-bending potential of psychedelics for mental wellness. Think of us as your trusty sidekicks, unraveling the mysteries of modern medicine while keeping it real and relatable. Let’s dive into the stories, the science, and the soul of healthcare, paving the way for a healthier tomorrow.

Frequently Asked Questions

How long is this episode of Normal Curves: Sexy Science, Serious Statistics?

This episode is 57 minutes long.

When was this Normal Curves: Sexy Science, Serious Statistics episode published?

This episode was published on June 29, 2026.

What is this episode about?

How do you decide whether a clinical trial “worked”? In Part 2 of our Galleri series, we examine the landmark randomized trial of a blood test designed to detect more than 50 cancers. We explore why different outcome measures led to dramatically...

Can I download this Normal Curves: Sexy Science, Serious Statistics episode?

Yes, you can download this episode by clicking the download button on the episode player, or subscribe to the podcast in your preferred podcast app for automatic downloads.
URL copied to clipboard!