Research Translation Podcast

David Newman
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Translating Medical and Health Research For All researchtranslation.substack.com

  1. 6d ago

    My Run-In With Elizabeth Holmes

    I met Elizabeth Holmes once, for about four seconds. It was 2013, I believe, at a Wired Health conference. Wired had profiled me and TheNNT, a website colleagues and I built to make medical evidence transparent and free, and they asked me to give a talk about evidence and data (you can see a 3-minute excerpt here). Holmes was on the same bill and spoke earlier in the day. We were introduced in the green room. That was pretty much the whole conversation. What I remember is not so much her, as the men around her. Earpieces, dark suits, scanning the room for threats. A security detail like the ones you might see around a cabinet secretary. I remember sitting with my Coke Zero and musing to myself what, exactly, the CEO of a blood-testing company needed protection from. In hindsight, the answer was obvious. She needed protection from scientific reality. After my talk I took questions from the floor. The first few were polite, perhaps feigning interest in the much dryer topic of translating research data. Then a few people in the audience, investors and VC types by the look of them, asked me what I thought of her company. To be honest I knew nothing about Theranos before that day. But two things struck me as self-evident. The first was that I read the medical literature obsessively, and there was nothing in it about rapid serum testing with a drop of blood. No validation studies. No peer-reviewed data. A company claiming to have reinvented laboratory medicine had, to my eye, published nothing about it. The second thing I knew was my own job. By then I had spent 15 years in emergency departments and about 25 in health care, counting paramedicine and the military. Every shift, we drew blood on sick people and then waited for the tubes go to a lab and, 45 minutes later, if we were lucky, we got numbers to help us tell whether someone was bleeding, infected, poisoned, or worse. So I told them what seemed obvious. If she really had a device that gave instant, reliable answers to hundreds of tests from a finger-prick, it seemed to me it would already be in my ER, and every other one on the planet. She wouldn’t need to raise money, and have a $9 billion valuation. She would be selling $200 billion worth of product, today. Real breakthroughs don’t hide. They get used. Then I asked THEM something, and I was not playing dumb. Am I missing something? Is her product just a blood test? Yes, they said. It’s a blood test. Well then, I said, I don’t get it. Because you can’t save a life with a blood test. Holmes talked as if information were treatment. Her signature story was about her uncle, who died of cancer, and she implied that if only her machine was at his side, testing him again and again, he never would have had cancer, or died of it. That is, to be blunt, just dumb. Anyone who’s done real work in medical evidence learns quickly that there is no perfect test. Every test misses some disease that’s present, and flags some that isn’t. Our fanciest and newest high tech blood test for cancer, the Galleri MCED, missed more than half of growing cancers, and more than half of those it flagged were false alarms. This kind of performance is typical for virtually all blood tests. Had Holmes, I asked, invented some new version of cancer testing that was 100% accurate? That she was hiding somewhere, instead of releasing it for all the world to use? Moreover, I told them, even when a test finds something real, we usually don’t know what it means. Every tumor is staggeringly complex. So is every human body. When the two meet, we have no idea how the two will interact. The immune system might quietly destroy the tumor, or it might sit harmlessly for decades, or it might take over the body, rapidly. If we could predict cancer-human interactions we would treat the dangerous tumors and leave the rest alone, and every cancer screening test would be a smashing success. But we can’t, so we treat nearly everything we find, doing countless surgeries, scopes, and scans, and most of the people we ‘save’ were never in danger. We are standing on a tiny island of knowledge in a vast sea of undiscovered information. With our current level of technology and our deeply limited understanding of human disease and biology, testing simply pours more mysterious water into the sea. It doesn’t make the island any bigger. Holmes was selling that water. Even if her machine worked flawlessly, the promise underneath it would have been false. It would be nice if we could file Theranos under fraud and move on. Holmes lied, she was convicted, she went to prison. Case closed. But the investors I met that day were sharp. One introduced herself to me as a “recovered cardiologist,” which I loved. She was funny, self-aware, and had spent years seeing up close what medicine can and can’t do. If anyone should have been immune to the pitch, it was her. Yet there she was with the rest of them, putting real money into the idea that the next gadget (a blood test!) would change everything. That’s the part we should sit with. This isn’t a story about gullible people. It’s about a way of thinking that we all share, smart people included, and we are blind to it. Call it gizmo idolatry. The quiet faith that medicine is more powerful than it is: That a certain surgeon has magic hands. Or a special test will catch the thing that would have killed you. Or a treatment is so good it can’t possibly hurt you. Or more blood testing means more living. And if we’re being honest with ourselves, it’s a reasonable mistake. You carry a phone that can call China at three in the morning, stream a film, and find your way home. It seems only natural to assume that the giant hospital systems, with their billions of dollars and research towers, are at least that impressive. Surely they can do more than the phone in my pocket. But they can’t. Not in the way we imagine. Trials of annual checkups and screening programs find more diagnoses and more procedures, not longer lives. Prostate screening brings more biopsies, more surgery and more frightened men, but no measurable gain in survival. The new multi-cancer blood tests are sold using the same emotional grammar Holmes used: one draw, dozens of cancers, catch it early, save a life! Yet randomized trials now show it didn’t even catch cancer early, much less help anyone live longer. Holmes didn’t invent this faith. She just discovered it in a dorm at Stanford, then sold it back to us. The fraud was hers—but the magical thinking is ours. I didn’t sniff out Theranos because I’m clever. I sniffed it out because I’ve spent a career watching tests not save people, and because I’ve learned to ask one boring question about any shiny new thing in medicine: Can you show me the people who lived longer because of it? Holmes never had that answer. Neither do most tests being sold to you right now. The security detail in that green room was protecting her from the simplest truth in medicine: A test can’t save your life. At best, it can tell us what to do next—and usually, we don’t even know what that is. But your doctor doesn’t have a security detail. So ask them. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    My Run-In With Elizabeth Holmes

  2. Oct 21

    The Babies Who Died of Early Detection

    What follows is a summary of the transcript, written by Claude. Let me know what you think, please. RT is fully reader-supported. Please become a paid subscriber—and spread the word, yo. My sons, 11 and 13, inform me that when I say something like “cancer screening can kill you,” the phrase I’m looking for is “no cap.” I’m grateful, because half of what I say about cancer screening sounds unhinged next to what we’ve all been taught. So: no cap. Facts. Let me start with a story I first told in 2007, in a book called Hippocrates’ Shadow. Nearly twenty years later, we still haven’t absorbed it. The urine test that found too much In 2002, two studies from Quebec and Germany reported what happened when public-health officials screened infants’ urine for neuroblastoma, one of the leading causes of cancer death in young children. Japan ran a similar program. The idea was flawless: catch the tumours early, while small, and fewer babies should die. Screening worked brilliantly at finding cancer. Children in the screened groups were diagnosed about three times as often as children in the unscreened comparison groups, even after years of follow-up. These weren’t false alarms. A positive urine test led to imaging, the imaging led to a biopsy, and the biopsy confirmed neuroblastoma under the microscope. Neuroblastoma deaths didn’t move. Roughly two out of three screen-detected tumors would have vanished or never caused a symptom—but they were all treated, aggressively. In the German program, the only three screened children who died were killed by the treatment: two by surgical complications, one by chemotherapy. Neuroblastoma screening was shut down worldwide. Leg one: You’re already running a screening program How do you find three times as many cancers that don’t matter? Your body was already on the case. Immunosurveillance is the continuous process by which your immune system finds abnormal cells and destroys or contains them. We’ve seen the evidence from the other direction for decades: people with AIDS, and transplant patients on immune-suppressing drugs, get more cancers. Take the guards off and more cancers get through. We can’t watch this happen at the cellular level, so we don’t know how often it succeeds. It seems to work better for some tumors than others. Screen for breast or prostate cancer and you don’t just find cancers earlier. You find more of them, over a lifetime. That makes no sense if every cancer is headed for the morgue, and perfect sense if your body was quietly handling a lot of them. The innate system almost certainly outperforms any screening technology we’ve built. Our technology’s main contribution is finding cancers the immune system already had under control, then treating them as if it didn’t. That breaks the escalator model of cancer most of us carry around: stage 1, 2, 3, 4, then dead. Every screening program is built on that escalator. Leg two: The barnyard Gil Welch, and Vinay Prasad after him, often talk about sorting cancers into three animals. Turtles are so slow that you can catch them whenever you like, or never. Birds fly out of the barn before any test can catch them, often showing up between screens. Rabbits sit in the middle: lethal if you find them as a lump under a clinician’s fingertip, curable if you find them as a shadow on a mammogram. Screening exists for rabbits. (Nobody has yet demonstrated that they exist, but perhaps they do). Screening also catches turtles and birds by the bushel. Leg three: Counting the bodies properly Screening trials mostly measure cancer-specific mortality, meaning deaths from breast cancer or from prostate cancer. Go back to the German babies. Count only “deaths from neuroblastoma” and leave out the children killed by surgery and chemo, and screening looks like a triumph. That’s why I care about all-cause mortality. More than 700,000 men have been randomized in PSA trials and roughly 600,000 women in mammography trials. Neither group shows a reduction in overall deaths. Some trials show fewer cancer-specific deaths, and some don’t even show that. That leaves two possibilities. Either the harms of overdiagnosis and overtreatment cancel out the lives saved, or the benefit is real but too small to detect even in trials of hundreds of thousands of people. The debate among experts is not about whether all-cause mortality dropped. It didn’t. The debate is whether we should trust cancer-specific mortality as a stand-in. We tried that with neuroblastoma. It was wrong, so I don’t subscribe to that school of thinking. No free lunch Take the advocates’ best case. Over 10 years of mammography, about 1 woman in 1,500 avoids a breast cancer death. Of those same 1,500, about 750 get a false positive and the anxiety and workup that follow, and about 50-75 have a surgical procedure on a breast that turns out to be normal. For PSA, the USPSTF’s numbers (with a little back-calculation from me) say that for every 1,000 men screened for a decade, 1 fewer dies of prostate cancer. Meanwhile 125 more have biopsies, 35 more are diagnosed with cancer, 27 more have their prostate removed or irradiated, 6 more become impotent and 4 more become incontinent. If you see those numbers and say, “I just don’t want to die of this cancer, sign me up,” that’s a rational choice. Make it. But that isn’t the conversation people are having. This year my new primary care team at the VA, a health system I genuinely love, was about to send a PSA test without mentioning it. They pulled it the moment I asked. Still, the default was to test everyone and discuss it with no one. Consider the source Who tells you to get screened? Radiology societies recommend mammography, and urology societies recommend PSA testing. I’m not accusing anyone of lying. But Upton Sinclair observed that it’s hard to get someone to understand something when their paycheck depends on not understanding it. Professional recommendations and evidence are different things. So ask for the numbers. The USPSTF’s evidence reviews are free and open. Ask your doctor how many people like you avoid death from this cancer, how many avoid death from anything, and how many get false alarms, biopsies and treatments they didn’t need. Then bring your own values to the decision. Cancer screening isn’t the lollipop at the end of the visit. Ask the babies. If this made you smarter, restack it. Newcomers, welcome; old guard, thank you. And if you want Research Translation to keep growing, become a paid subscriber. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    The Babies Who Died of Early Detection

  3. Oct 14

    The Early Detection Cancer Test That Doesn’t Detect Early

    In college our physics teacher, Professor McGinnis, held an annual competition. Using a container made only of glue and toothpicks, we had to keep an egg from cracking when dropped out of the classroom’s first-story window. My team built a geometric lattice, buttressed with layers of internal protection. It was beautiful. For our (mandatory) test run, we dropped it empty, to be sure the container would survive and the egg compartment would hold. The container fell apart, and the compartment was gone. So we redesigned. Again the empty drop failed. But the lattice was so pretty. Perhaps the weight of a settled egg would strengthen the wood, we hoped. And with no time for a loaded test, we prepared for Egg Drop Friday. Suzanne, our dropper, had magic hands. With a quick lift and a soft release, our soccer-ball-like structure seemed to fall slower than gravity should allow. All morning, cheers had erupted at each impact. But when our dazzler struck the ground, the crowd fell silent. Toothpicks everywhere. The egg was not cracked, it was obliterated. No piece of shell was larger than a coin. Professor McGinnis wondered aloud whether our structure had somehow funneled the energy straight into the egg. Then, shaking his head, he asked the question we all knew was coming. “What happened when you did the empty drop?” Last week the New England Journal of Medicine published NHS-Galleri, a massive trial that randomized more than 140,000 people in England to have, or not have, their blood run through a multi-cancer early detection (MCED) test once a year for three years. The trial was funded by the test’s manufacturer, Grail, and Grail employees are among the authors. Let’s start with what matters: The point of cancer screening is to keep people from dying. This paper doesn’t even report how many people died in each group, from cancer or from anything else. This is despite the fact that the trial enrolled 140,000 people, easily enough to have a count of cancer and overall mortality, Instead, they offer us a test run: did the early detection test detect cancer earlier? It is the empty egg drop. It failed. The primary outcome was whether annual testing reduced the number of people diagnosed with late-stage (stage III or IV) cancer. It didn’t, and it wasn’t a near miss. The screened people were found to have 706 late-stage cancer diagnoses and the unscreened people were diagnosed with 688. In other words there were MORE, LATER STAGE, cancers in the tested group. Grail’s press release, however, leads with something different. They tout “20% drop in stage IV cancers”! This was a secondary endpoint, and the result’s confidence interval brushes up against no effect at all. Moreover this reduction, they confess was “offset by a higher rate of stage III cancer diagnosis.” The test didn’t lift anyone out of late-stage disease. Why? The companion paper in Nature Medicine shows us. In years two and three, which is what a real annual screening program looks like, the test missed about 73% of cancers, and roughly half of the 12 deadly types it was designed to catch. Worse yet, about half its positive results were false alarms: 864 healthy, cancer-free people were told they might have cancer. The test’s specificity, around 99.5%, sounds superb. But it isn’t. When you screen millions of healthy people for rare diseases, even 99.5% means half of all positive results are wrong. Scale it to 100 million Americans and roughly 450,000 people a year would be told they might have cancer when they don’t, each launched into scans, scopes, and biopsies. And Grail tuned the test for that specificity. The price was sensitivity, which is why it missed MOST cancers. This isn’t a bug to fix in version 2.0. Small, early tumors shed very little DNA into the blood. Crank up the sensitivity to find them and you drown in false positives and overdiagnosis, the treatment of ‘cancers’ that would never have hurt anyone. Cancer is not one disease marching in a straight line from small and curable to big and deadly. Some kill no matter how early you find them. Some never would have mattered. The mythical sweet spot, where finding it early changes everything, has never been proven to exist. And if it does, we know this: It’s way smaller than the brochures suggest. But fortunately for the test manufacturers, and unfortunately for us, early detection is more than a strategy. It’s an ideology, and for many it’s even a faith. And faith can ‘succeed’ even when the empty drop fails. Which brings us to September 23, exactly one day after the NEJM paper went online. An FDA advisory committee met to consider Grail’s application and voted to recommend approving the test. Someone on that panel should have asked Professor McGinnis’s question: What happened when you did the empty drop? Get full access to Research Translation at researchtranslation.substack.com/subscribe

    The Early Detection Cancer Test That Doesn’t Detect Early

  4. Oct 9

    Deconstructing the Alcohol-Cancer Obsession

    Below is a summary of the audio transcript, written by Gemini AI with some heavy editing by me. Let me know if you like or hate these, I’m still experimenting! “STROLLERS THAT KILL! At eleven.” RT is reader-supported. To help, PLEASE become a paid subscriber. For the price of a cappucino once a month, you’ll keep us afloat. This is one of my all-time favorite teasers for the evening news. These days, it’s pretty typical for headlines and news teasers to threaten you or your children’s lives. Which is why it wasn’t so surprising to see recent headlines about how alcohol-linked cancer deaths have doubled—AND ONE DRINK A DAY COULD KILL YOU. Behind this sensationalism, however, lies a not-so-master class in statistical deception. To understand why these claims are absurd, we can look at how public health authorities manipulate observational data—and why a glass of wine with dinner is not exactly a death sentence. 1. Severe Alcoholism vs. Moderate Drinking First, let’s stipulate what everyone agrees on: chronic, severe alcoholism is bad. Heavy, years-long drinking leads to liver cirrhosis and hepatocellular carcinoma (often fatal liver cancer). It also contributes to violence and fatal accidents. Public health efforts should target severe addiction, drunk driving, and alcohol-related violence. But authorities and mass media are conflating severe alcoholism with moderate intake, and they’re doing it on purpose. Why? Because vilifying the glass of wine with dinner is a surefire path to more clicks and more attention. 2. The Observational Evidence Trap Every study claiming moderate drinking causes cancer is observational, in which researchers observe people over time and collect data including self-reported surveys. Here are a few classic problems with using observational data to make cause-and-effect claims. * Self-Selection & Confounders: People who drink and those who abstain carry vastly different cultural, socioeconomic, genetic, and lifestyle traits. In observational data, these background differences—called confounders—distort any comparisons that are, in theory, supposed to be about alcohol consumption. * The Trial Difference: In randomized trials, flipping a coin balances background traits equally between groups. Historically, observational ‘discoveries’ (like hormone replacement for post-menopausal women preventing heart disease, or high fiber diets preventing colon cancer) evaporated when tested in trials. * Underreporting: Patients famously underreport alcohol intake. When a study links “a drink a day” to a health outcome, it may really mean two to four drinks. Weak Relative Risks Are Not Causation To evaluate a health claim, you have to look at the relative risk. When scientists look at cigarette smoking and lung cancer, smokers face a staggering 2,000% to 4,000% increase in risk. Multiple severe sunburns double or triple the risk of melanoma (200% to 300%). Those are huge, indisputable signals. Compare that to moderate alcohol intake and breast cancer, where public health authorities point to a paper-thin 4% relative increase. A relative risk that low is not a cause-and-effect relationship—it is the classic residue of lifestyle confounding. Furthermore, public health agencies selectively report these tiny upward bumps while ignoring the other side of the ledger. The same studies show that moderate drinking is associated with a 15% lower rate of kidney cancer, thyroid cancer, and lymphomas. Neither direction represents true causation, but health agencies cherry-pick the upward noise, tipping their hand: This isn’t about truth, it’s about pushing an agenda. Absolute Risk Puts Fear in Perspective When agencies cite relative multipliers like ‘1.4-fold increases’, converting them to absolute numbers exposes how negligible the real-world risk is. For esophageal cancer, that relative bump translates to 1 additional case per 7,000 moderate drinkers over a decade. For oral cancer, it works out to 1 additional case per 700 moderate drinkers over ten years. To put those numbers in perspective, the average American has a 1 in 93 lifetime chance of dying in a car crash and a 1 in 2,482 chance of choking on food—making daily life far riskier than a decade of moderate wine consumption. In private admissions, public health researchers acknowledge that these numbers do not represent true cause-and-effect, but justify scaring moderate drinkers under the assumption that it might indirectly curb drunk driving or severe abuse. Deconstructing the “Doubling” Headline The recent headline claiming alcohol-attributable cancer deaths doubled between 1990 and 2023 comes from a Lancet secondary analysis of Global Burden of Disease data, citing a jump from roughly 11,000 deaths to 23,000. When you strip away the spin, the “doubling” vanishes. Ninety-two percent of that increase was driven entirely by population growth and the aging of the Baby Boomer generation into peak cancer years. In fact, per-capita alcohol consumption in the U.S. was exactly the same in 2023 as it as in 1990. And the small remaining statistical increase? It was driven almost entirely by liver cancer deaths—the majority of which are caused by Hepatitis C infections, not alcohol. Meanwhile, actual U.S. age-adjusted cancer mortality has dropped massively in the last three decades, thanks to improved treatments. The Bottom Line When public health modeling turns aging into scary headlines, it isn’t science—it’s spin. True causal relationships are strong, clear, and undisputed. When studies contradict each other and rely on 4% relative increases, there is no meaningful real-world risk. Public health agencies should address severe alcohol abuse and drunk driving directly, rather than using statistical ghosts to fearmonger moderate drinkers. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    Deconstructing the Alcohol-Cancer Obsession

  5. Oct 1

    The Cardiologist Contorting To Compliment the STAREE Trial

    Below is a summary of the transcribed audio, brought to you by Gemini. I’m trying different AI options for these summaries, please feel free to let me know which ones you like. For an additional note answering one subscriber’s excellent question about statins, check out the audio. Enjoy! RT is 100% subscriber-supported. To help us endure and expand, become a paid subscriber! The New England Journal of Medicine recently published its print edition featuring the long-awaited STAREE trial, alongside an editorial authored by Dr. Martin Mortensen. STAREE set out to answer a fundamental, patient-centered question for millions of aging adults: Can statins help healthy people 70 and older live longer, preserve cognitive function, or prevent disability? To appreciate why STAREE matters, you have to understand that over 90% of the statin trial literature driving clinical guidelines originates from pharmaceutical companies. Drug manufacturers design studies around tightly controlled, easily measurable markers carefully engineered to secure regulatory approvals and support marketing claims. But a commercially viable, marketable endpoint does not automatically translate into a meaningful clinical benefit for a human being taking a daily pill. STAREE flipped that approach on its head. Publicly funded and conducted across Australian primary care practices with roughly 10,000 enrolled participants aged 70 and older, the trial asked a far more rigorous question. While the investigators measured traditional cardiovascular events, those outcomes were explicitly treated as an intermediary step. The true goal was disability-free survival—a hard composite of all-cause mortality, incident dementia, and physical disability. The trial tested whether reducing cardiovascular events translated into a tangible improvement in the duration and quality of older adults’ lives. The answer was a resounding, unambiguous “no.” STAREE demonstrated no reduction in death, no reduction in dementia, no reduction in disability, and no reduction in the composite combining all three. On the outcome it was designed to test, the trial was a complete failure. Enter the accompanying editorial. Despite these definitive findings, Dr. Mortensen summarizes STAREE as providing “compelling evidence for the benefit and safety of initiating statin therapy in relatively healthy older adults.” Arriving at an enthusiastic, green-light recommendation from a trial that completely missed its primary patient-centered endpoint requires a masterclass in rhetorical and analytical contortion. First, the editorial obscures the fact that the trial investigators moved the goalposts mid-study. Mortensen opens his analysis by focusing heavily on the trial’s cardiovascular composite endpoint, presenting it as a combination of cardiovascular death, nonfatal heart attack, stroke, or coronary revascularization. What readers are not told is that revascularization was not part of the original outcome. According to the original protocol and registry, the outcome was strictly limited to cardiovascular death, nonfatal heart attack, and stroke. Enrollment began in 2015. Revascularization was tacked on in 2024—nine years into the study—after investigators realized event rates were falling short of projections. So they moved the goal posts. Expanding an endpoint mid-trial to capture procedural numbers is critical context, but the editorial presents it as if it were the plan all along. Second, the editorial equates procedural interventions with true clinical preservation. The statistically significant reductions touted in the study were driven almost entirely by nonfatal heart attacks and coronary revascularizations. Cardiovascular mortality itself was not reduced in the slightest. Placing a stent in a stable coronary artery is a procedure that is subjectively chosen, and proven not to reduce heart attacks, strokes, or death. So how, pray tell, can it act as a surrogate for those outcomes? Third, when Mortensen directly confronts the trial’s central primary outcome—the complete lack of impact on disability-free survival—he labels it a “neutral finding that warrants a nuanced interpretation.” Calling the failure of a 10,000-person, multi-year trial’s main outcome ‘neutral’ is pure gas-lighting. Thousands of seniors participated precisely so we could learn whether statins preserve independent living. that failure isn’t an incidental or ‘neutral’ footnote—it is the core finding of the entire enterprise. Fourth, the editorial performs a complete logical inversion by reframing a lack of harm as a positive justification for intervention. Mortensen points out that statins did not statistically increase death or dementia, and declares victory. The drugs were supposed to reduce both, and failed. But Mortensen uses the absence of benefit to reassure clinicians about prescribing statins. Gas-lighting. Mortensen’s mandatory disclosures reveal active financial relationships with major pharmaceutical manufacturers Novartis and Eli Lilly. Once again, the Death Star selected an editorialist with commercial ties to companies that sell cholesterol-lowering drugs, to provide commentary on cholesterol-lowering trial results. While Mortensen may sincerely believe his conclusions serve public health, good intentions do not compensate for an unpersuasive, contorted interpretation. Mortensen concludes by advocating for informed, shared decision-making in practice—a principle on which we agree. However, genuine shared decision-making cannot happen when trial results are repackaged to hide their primary failures. Telling an older patient “your cholesterol is high, take this pill” is not shared decision-making. Honest consent requires informing patients that while statins may lower the risk of certain nonfatal and procedural events, large-scale public trials prove they do not help healthy older adults live longer, prevent dementia, or avoid disability—all while carrying established risks of diabetes and muscle problems. STAREE asked the right question, and came to the right answer. Perhaps the commentary should do the same. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    The Cardiologist Contorting To Compliment the STAREE Trial

  6. Sep 25

    The Single Most Important Trick in Medical Research

    Today’s episode explains the broader theme that ran through our episode from earlier this week. I wasn’t sure if I was clear enough, so here it is. Since it was just a quick audio I had ChatGPT turn the transcript into a written piece for the many written word addicts in our group (respect!). Naturally, I couldn’t help but heavily edit. RT is 100% reader-supported, and we want to keep doing it. You can help ensure that, by becoming a paid subscriber. Thanks. Here’s a trick that, once you see it, is hard to unsee. It’s everywhere in research, and understanding it can change how you read a study in about ten seconds. The trick is called a surrogate, something researchers measure that stands in for the thing we actually care about. Let’s start with an easy one: Cholesterol. Nobody wants a lower cholesterol number for its own sake. You don’t frame your lab report and put it next to the grandkids. You want to live longer and live better. Which illustrates just one of the problems with surrogates: Sometimes they’re not good surrogates. For most people, lower cholesterol does not predict longevity or health. But that’s a story for another day. The important part here is that it’s not the true prize. When researchers do a study to prove they can lower cholesterol—which is common—the cholesterol is acting as a surrogate for what matters. Imagine I offered you a pill that raised your cholesterol into the thousands, but research showed it helped you live longer and better. You’d be interested, of course. Because lower cholesterol isn’t the prize. Your health is. Now, for a less obvious example: breast cancer mortality. This sounds like the real thing. Death! What could be more important? When people hear “fewer breast cancer deaths,” their brains instantly translate it to “fewer deaths.” But those aren’t the same. This is the central tension in debates about whether cancer screening saves lives. No randomized trial has ever found that mammography reduces deaths—but a few have found it reduces breast cancer deaths. How can that be? In my book I talk about an imaginary device called the HeartSaver 3000. Touch it to someone’s chest during a heart attack and—presto!—it eliminates their chance of dying from that heart attack. Unfortunately, it replaces that risk with an equal chance of dying from a stroke. In such a case heart attack deaths are reduced, but the total number of deaths is unchanged. Suppose mammography screening prevents some deaths due to breast cancer but also leads to extra, unnecessary biopsies and surgeries and chemotherapy, some of which cause deaths—perhaps enough to offset any lives saved. Or, suppose there’s ambiguity about which deaths are due to breast cancer. A woman treated for breast cancer dies from a blood clot. Is that a blood clot death or a cancer death? This kind of ambiguity can sway the numbers in one direction. Counting total deaths, rather than breast cancer deaths, avoids the labeling problem. It also keeps deaths caused by treatment in the calculation (where they should be). This is why I call disease-specific mortality (like breast cancer-, or heart attack mortality) a surrogate. It’s standing in for the true prize, longer life. Which brings us to the adult vaccines. The big trial of the pneumococcal vaccine found fewer pneumonias due to certain bacterial strains. But not fewer pneumonias (or deaths). And ten years later, CDC data confirm that pneumococcal disease rates have not changed with the introduction of the adult vaccine. In other words, the surrogate suggested a benefit that failed to appear. Yet, when we hear a study found “less pneumococcal pneumonia” our brains often drop the middle word. We hear ‘less pneumonia’—which the study never found. This week, along comes the new RSV trial, of more than half a million people. RSV-associated respiratory hospitalizations fell from 64 to 19. But neither hospitalizations nor deaths fell. At this point someone invariably says: perhaps the study just wasn’t large enough to detect the broader benefit. But it would probably be there if the study was bigger. Perhaps. But another possibility is that the surrogate failed. Perhaps harms offset gains, or blurry definitions swayed the result. Whatever the reason, history tells us we cannot assume a benefit in the true prize is hiding below the statistical radar when we see only a surrogate succeed. Meanwhile, the burdens need no such leap of faith. The appointment takes time. The shot costs money. Pain, swelling, fatigue, and serious effects like Guillan-Barré Syndrome all count. Whether it’s a vaccine, a surgery, or a pill, there’s no free lunch in medicine. So here’s your thirty-second research-reading habit: Find out what they actually measured. Then ask whether it’s the true prize. Is it a blood test? Or a type of pneumonia? Or death from one disease? Those are surrogates. Is it living longer and living better? These are prizes. Don’t let anyone sell you a better number, and call it better health. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    The Single Most Important Trick in Medical Research

  7. Sep 22

    The New Adult RSV Vaccine, the Death Star, and Promises Broken

    Apologies to my paid subscribers, who are getting this for the second time. I sent it out as a paid-only post, which was a mistake. I will be having paid subscriber-only content fairly soon, but I do try to make most of my content available to everyone, so I’m re-sending to all. Sorry for the inbox clutter! In third grade Marc Diaz explained to me, very earnestly, that if one’s hand was larger than their face, that person was technically deformed. Concerned, I held my palm to my face, carefully comparing. At which point he cracked the back of my hand with his own palm, sending my hand into my face and me toppling backwards out of my chair. Good one, I thought, ready to line it up on the next sucker. But he wasn’t finished. “Sorry sorry,” he said “I had to do it, so funny, everyone checks. But seriously,” he went on, “I swear it’s totally true. My dad told me, it means you’re deformed.” “No way” I said, and checked again. So he cracked me again. Fool Us Once: The Adult Pneumococcal Vaccine Below is a graph of CDC data tracking the rates of serious pneumococcal illness since 1997 in the United States. Uptake of the adult pneumococcal vaccine, a shot intended to reduce such illnesses, began in earnest in the 1990s. The only two inflection points in the timeline are at 2000 and 2010. Each point appears to initiate a 5-year downward trend in pneumococcal disease, followed by five years of no change. Both points coincide almost perfectly with newly implemented childhood pneumococcal vaccines—a classic manifestation of herd protection, where immunizing toddlers stops them from passing bacteria to their grandparents. The adult pneumococcal vaccine, conversely, first gained widespread use in the late 1990s—where the line lies stubbornly flat for its first three years on record. Then, in 2014, the CDC’s vaccine advisory committee recommended routine PCV13 (pneumococcal) vaccination for those over 65. With expanded Medicare coverage, this fueled a surge in uptake from 3% to 49% of Medicare beneficiaries by 2019. And yet, through the surge years, pneumococcal disease remained unchanged. These findings fit with results from the vaccine’s one relevant large trial—but only if you know how to read it. The CAPITA trial randomized 85,000 older adults in the Netherlands to a pneumococcal vaccine or a placebo, and found the vaccine did not measurably reduce either pneumonia or mortality. It did, however, achieve its stated goal: reducing illnesses labeled as due to vaccine strains of the bacteria. But that’s a shifty substitution. Patients want fewer pneumonias, fewer hospital stays, and fewer deaths. Instead, the trial claimed a tiny reduction in pneumonias with selected bacterial names. It was a classic bait and switch, since doctors and patients would see the ‘reduction in pneumonias’ and equate it with a reduced chance of pneumonia overall. But that’s not what the trial showed. Pneumonia was as common in the vaccine group as in the placebo group. How can one subtype be reduced, while pneumonia rates overall are not? One way would be if vaccination increased pneumonia due to other bacteria or viruses, offsetting those it prevented. Another would be if pneumonia was just as common but vaccination made it harder for labs to detect and isolate covered strains, making those pneumonias appear less common in the vaccine group. Or, perhaps the vaccine prevents so few cases that the difference disappears among all the other pneumonias. Whatever the mechanism, people who received the vaccine weren’t less likely to experience pneumonia. Instead, a tiny reduction in selected bacterial strains stood in for what people actually care about. And that surrogate became a brilliant sales pitch in the vaunted New England Journal of Medicine. But the broader benefit it claimed to represent never materialized—either in trials or in population trends. RT is fully reader supported. Please become a paid subscriber, so we can continue to TR! Fool Us Twice: The Adult RSV Vaccine Last week, trial findings from Pfizer’s RSV vaccine appeared in NEJM Evidence, accompanied by an editorial titled “A Hat Trick for RSV Vaccines — Prevention of Disease, Hospitalization, and Beyond.” And beyond! Now we’re vaccinating Buzz Lightyear. But before boarding the tiny spaceship, here’s a question that hearkens back to our first rodeo: Were vaccinated people less likely to end up in the hospital, or to die? The Pfizer-funded DAN-RSV trial randomized an incredible half million adults to vaccine or no-vaccine. During the study more than 27,000 of them were hospitalized and 1,200 died—enough to show even tiny benefits in the two outcomes. But the vaccine reduced neither. So where does the victory dance come from? Their primary outcome: RSV-attributed respiratory hospitalizations, at 19 vs 64 in the two groups. Sound familiar? This isn’t our first rodeo. In the DAN-RSV study the adjective ‘RSV-attributed’, much like ‘vaccine-type strains’ in the CAPITA study, is doing serious work. If we drop it, and instead simply ask whether people were less likely to be hospitalized or die, the triumph disappears. People deserve to understand that distinction before rolling up their sleeves, because people don’t lie in hospital beds hoping for a more flattering diagnostic code. They hope not to be in a hospital at all. And of course, we all deserve to know if the vaccine truly works, because vaccination comes with ginormous individual and societal costs, both human and monetary. Setting aside appointments, out-of-pocket costs, then pain, swelling, and fatigue from the shot, there were also 21 people in the trial judged to have had serious vaccine side effects. These included cardiac arrhythmia, pericarditis, facial paralysis (Bell’s palsy), and five cases of Guillain–Barré Syndrome. Financially, Abrysvo (the shot’s brand name) generated roughly $1.7 billion globally in 2023 and 2024—numbers poised to explode if professional societies and governments are convinced by NEJM’s ‘hat trick’. Similarly, the pneumococcal vaccine, recommended by the CDC and dozens of societies, has added a billion per year in Medicare spending alone—i.e. tax dollars. Which raises a question: At this point, given the data, why does anyone get that shot? The answer is that by the time the pneumonia shot enters the room, the sales job is largely complete. The New England Journal of Medicine called it a success. The FDA approved it. Public-health authorities recommended it. Your doctor brought it up. Who could imagine the best science shows it failed to reduce pneumonias or deaths, or that public health trends show it had no impact? That is the awesome power of the Death Star, turning a narrow surrogate success into a sweeping promise for less illness and lives saved. Meanwhile, the price we pay is billions of dollars and millions of side effects—all while the promise is quietly broken. And up goes our hand again. Footnotes: Get full access to Research Translation at researchtranslation.substack.com/subscribe

  8. Sep 22

    The New Adult RSV Vaccine, the Death Star, and Promises Broken

    In third grade Marc Diaz explained to me, very earnestly, that if one’s hand was larger than their face, that person was technically deformed. Concerned, I held my palm to my face, carefully comparing. At which point he cracked the back of my hand with his own palm, sending my hand into my face and me toppling backwards out of my chair. Good one, I thought, ready to line it up on the next sucker. But he wasn’t finished. “Sorry sorry,” he said “I had to do it, so funny, everyone checks. But seriously,” he went on, “I swear it’s totally true. My dad told me, it means you’re deformed.” “No way” I said, and checked again. So he cracked me again. Fool Us Once: The Adult Pneumococcal Vaccine Below is a graph of CDC data tracking the rates of serious pneumococcal illness since 1997 in the United States. Uptake of the adult pneumococcal vaccine, a shot intended to reduce such illnesses, began in earnest in the 1990s. The only two inflection points in the timeline are at 2000 and 2010. Each point appears to initiate a 5-year downward trend in pneumococcal disease, followed by five years of no change. Both points coincide almost perfectly with newly implemented childhood pneumococcal vaccines—a classic manifestation of herd protection, where immunizing toddlers stops them from passing bacteria to their grandparents. The adult pneumococcal vaccine, conversely, first gained widespread use in the late 1990s—where the line lies stubbornly flat for its first three years on record. Then, in 2014, the CDC’s vaccine advisory committee recommended routine PCV13 (pneumococcal) vaccination for those over 65. With expanded Medicare coverage, this fueled a surge in uptake from 3% to 49% of Medicare beneficiaries by 2019. And yet, through the surge years, pneumococcal disease remained unchanged. These findings fit with results from the vaccine’s one relevant large trial—but only if you know how to read it. The CAPITA trial randomized 85,000 older adults in the Netherlands to a pneumococcal vaccine or a placebo, and found the vaccine did not measurably reduce either pneumonia or mortality. It did, however, achieve its stated goal: reducing illnesses labeled as due to vaccine strains of the bacteria. But that’s a shifty substitution. Patients want fewer pneumonias, fewer hospital stays, and fewer deaths. Instead, the trial claimed a tiny reduction in pneumonias with selected bacterial names. It was a classic bait and switch, since doctors and patients would see the ‘reduction in pneumonias’ and equate it with a reduced chance of pneumonia overall. But that’s not what the trial showed. Pneumonia was as common in the vaccine group as in the placebo group. How can one subtype be reduced, while pneumonia rates overall are not? One way would be if vaccination increased pneumonia due to other bacteria or viruses, offsetting those it prevented. Another would be if pneumonia was just as common but vaccination made it harder for labs to detect and isolate covered strains, making those pneumonias appear less common in the vaccine group. Or, perhaps the vaccine prevents so few cases that the difference disappears among all the other pneumonias. Whatever the mechanism, people who received the vaccine weren’t less likely to experience pneumonia. Instead, a tiny reduction in selected bacterial strains stood in for what people actually care about. And that surrogate became a brilliant sales pitch in the vaunted New England Journal of Medicine. But the broader benefit it claimed to represent never materialized—either in trials or in population trends. Fool Us Twice: The Adult RSV Vaccine Last week, trial findings from Pfizer’s RSV vaccine appeared in NEJM Evidence, accompanied by an editorial titled “A Hat Trick for RSV Vaccines — Prevention of Disease, Hospitalization, and Beyond.” And beyond! Now we’re vaccinating Buzz Lightyear. But before boarding the tiny spaceship, here’s a question that hearkens back to our first rodeo: Were vaccinated people less likely to end up in the hospital, or to die? The Pfizer-funded DAN-RSV trial randomized an incredible half million adults to vaccine or no-vaccine. During the study more than 27,000 of them were hospitalized and 1,200 died—enough to show even tiny benefits in the two outcomes. But the vaccine reduced neither. So where does the victory dance come from? Their primary outcome: RSV-attributed respiratory hospitalizations, at 19 vs 64 in the two groups. Sound familiar? This isn’t our first rodeo. In the DAN-RSV study the adjective ‘RSV-attributed’, much like ‘vaccine-type strains’ in the CAPITA study, is doing serious work. If we drop it, and instead simply ask whether people were less likely to be hospitalized or die, the triumph disappears. People deserve to understand that distinction before rolling up their sleeves, because people don’t lie in hospital beds hoping for a more flattering diagnostic code. They hope not to be in a hospital at all. And of course, we all deserve to know if the vaccine truly works, because vaccination comes with ginormous individual and societal costs, both human and monetary. Setting aside appointments, out-of-pocket costs, then pain, swelling, and fatigue from the shot, there were also 21 people in the trial judged to have had serious vaccine side effects. These included cardiac arrhythmia, pericarditis, facial paralysis (Bell’s palsy), and five cases of Guillain–Barré Syndrome. Financially, Abrysvo (the shot’s brand name) generated roughly $1.7 billion globally in 2023 and 2024—numbers poised to explode if professional societies and governments are convinced by NEJM’s ‘hat trick’. Similarly, the pneumococcal vaccine, recommended by the CDC and dozens of societies, has added a billion per year in Medicare spending alone—i.e. tax dollars. Which raises a question: At this point, given the data, why does anyone get that shot? The answer is that by the time the pneumonia shot enters the room, the sales job is largely complete. The New England Journal of Medicine called it a success. The FDA approved it. Public-health authorities recommended it. Your doctor brought it up. Who could imagine the best science shows it failed to reduce pneumonias or deaths, or that public health trends show it had no impact? That is the awesome power of the Death Star, turning a narrow surrogate success into a sweeping promise for less illness and lives saved. Meanwhile, the price we pay is billions of dollars and millions of side effects—all while the promise is quietly broken. And up goes our hand again. RT is totally reader-supported. Please become part of the support—become a paid subscriber. Get full access to Research Translation at researchtranslation.substack.com/subscribe

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