Normal Curves: Sexy Science, Serious Statistics

Regina Nuzzo and Kristin Sainani
  • 4.9 (33)
  • Science
  • Updated Biweekly

Normal Curves is a podcast about sexy science & serious statistics. Ever try to make sense of a scientific study and the numbers behind it? Listen in to a lively conversation between two stats-savvy friends who break it all down with humor and clarity. Professors Regina Nuzzo of Gallaudet University and Kristin Sainani of Stanford University discuss academic papers journal club-style — except with more fun, less jargon, and some irreverent, PG-13 content sprinkled in. Join Kristin and Regina as they dissect the data, challenge the claims, and arm you with tools to assess scientific studies on your own.

  1. Cancer Blood Tests Part 2: The clinical trial

    3d ago

    Cancer Blood Tests Part 2: The clinical trial

    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 topics cancer screeningexploratory analyseshierarchical testingmissing datamultiple testingoutcome measurespost hoc analysespre-registrationprimary and secondary outcomesrandomized clinical trialsscreening tests Methodologic 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?” References Giridhar 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 studies Cancer 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/X Regina - 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

    58 min
  2. Cancer Blood Tests: Are they ready for primetime? Part 1

    Jun 15

    Cancer Blood Tests: Are they ready for primetime? Part 1

    Can a single tube of blood really detect dozens of cancers before symptoms appear? We dive into the science behind Galleri, a blood test that claims to detect more than 50 types of cancer from a simple blood draw. Recent headlines about the test ranged from “breakthrough” to “bust” after the release of results from a massive randomized clinical trial. In this Part 1 episode, we explore cell-free DNA, DNA methylation, machine learning, sensitivity, specificity, and positive predictive value. Along the way, we revisit the prenatal screening revolution, ask why detecting cancer earlier doesn’t always help patients, and learn how escaped DNA convicts end up swimming in a giant molecular pool party. And for the first time ever, Normal Curves ends on a cliffhanger: we’ll save the controversial results of that landmark trial for Part 2. Statistical topics cancer screeningcase-control studiescounterfactualsmachine learningnegative predictive valueoverdiagnosispositive predictive valuerandomized clinical trialsscreening testssensitivity and specificityvalidation References Bianchi DW, Chudova D, Sehnert AJ, et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA. 2015; 314:162-9. Liu MC, Oxnard GR, Klein EA, et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol. 2020. 31:745-59. Schrag D, Beer T, McDonnell C et al. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. The Lancet. 402: 1251-60.Giridhar 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. Statistic discussed in the episode PATHFINDER 2 investigators reported that adding Galleri to routine screening increased the number of screen-detected cancers by 6.5-fold. This figure compares 31 cancers detected through USPSTF-recommended screening (for breast, cervical, lung, and colon) with 204 cancers detected when Galleri was added, counting the same 31 conventional-screening cancers in both totals. Thus, describing the increase as 6.5-fold is misleading, since the combination of Galleri plus conventional screening is, by definition, guaranteed to detect at least as many cancers as conventional screening alone. Moreover, everyone in the study received Galleri, whereas conventional screening depended on which tests participants happened to be due for and completed during the study period. The comparison therefore does not involve two equally applied screening strategies. 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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - - Introduction (00:44) - - The Holy Grail of Cancer Testing (04:31) - - Headlines: Same Data, Opposite Stories (07:38) - - How Cell-Free DNA Works (13:54) - - DNA Methylation: GRAIL's Fingerprint (15:19) - - The Origin Story (22:18) - - The Pathfinder Studies (35:01) - - The Paradox: Why Earlier Detection Doesn't Always Help (40:32) - - The Cliffhanger

    43 min
  3. Odds Ratios: Do most people get them wrong?

    Jun 1

    Odds Ratios: Do most people get them wrong?

    Odds ratios show up everywhere in medical research—but do readers, journalists, and even researchers always know what they mean? In this episode, we tackle one of the most common statistical misunderstandings in science: treating odds ratios like risk ratios. Along the way, we explore puppy photos, fish photos, first-date hookups, sugary drinks, cardiac care, and a listener challenge that started with an informal study of five medical residents and a box of chocolate truffles. We explain why logistic regression produces odds ratios, when odds ratios can wildly exaggerate effects, and why some famous headlines turned out to be much less dramatic than they sounded. Statistical topics binary outcomescase-control studieslogistic regressionodds ratiosrisk ratiosodds vs risk Methodological morals “Just because logistic regression gives you an odds ratio does not mean you have to report it.”“A lot of bad science communication starts long before the journalist even enters the story.” References Bleich SN, Herring BJ, Flagg DD, et al. Reduction in purchases of sugar-sweetened beverages among low-income Black adolescents after exposure to caloric information. Am J Public Health. 2012;102:329–35.Sainani KL. How Statistics Can Mislead. Am J Public Health. 2012. 2012;102:e3–e4.Bleich SN, Herring BJ, Flagg DD, et al. Bleich et al. respond. Am J Public Health. 2012;102:e4.  Press video: https://www.youtube.com/watch?v=IFyrqbf1XWs Sainani KL, Schmajuk G, Liu V. A Caution on Interpreting Odds Ratios. Sleep. 2009;32:976.Schulman KA, Berlin JA, Harless W, et al. The Effect of Race and Sex on Physicians' Recommendations for Cardiac Catheterization. NEJM. 1999;340:618–26.Schwartz LM, Woloshin S, Welch HG. Misunderstandings about the Effects of Race and Sex on Physicians' Referrals for Cardiac Catheterization. NEJM. 1999;341:279–83.Associated Press. Study Finds Bias in Doctors' Care of Women and Blacks. The New York Times. February 25, 1999.Knol MJ, Duijnhoven RG, Grobbee DE, et al. Potential Misinterpretation of Treatment Effects Due to Use of Odds Ratios and Logistic Regression in Randomized Controlled Trials. PLoS ONE. 2011;6:e21248.   More information on logistic regression and odds ratios: Sainani KL. Logistic Regression. PM&R. 2014;6:1157–62.Sainani KL. Understanding Odds Ratios. PM&R. 2011;3:263–67.Nuzzo RL. Communicating measures of relative risk in plain English. PM&R. 2022;14:283-287. When outcomes are common, odds ratios can exaggerate effect sizes. Alternatives include: Presenting raw percentages (absolute risks)Presenting adjusted percentages from logistic regression (these may be calculated by plugging in means for the covariates)Converting odds ratios to risk ratiosReporting risk ratios directly when appropriate Converting Odds Ratios to Risk Ratios: Zhang J, Yu KF. What's the Relative Risk? A Method of Correcting the Odds Ratio in Cohort Studies of Common Outcomes. JAMA. 1998;280:1690–91.ClinCalc. Odds Ratio to Relative Risk Calculator. https://clincalc.com/stats/convertor.aspxRR = OR / [(1 − P0) + (P0 × OR)]Example: OR=0.51, baseline risk=93.3% RR = 0.51 / [(1 − 0.933) + (0.933 × 0.51)] = 0.51 / (0.067 + 0.476) = 0.51 / 0.543 = 0.94 Thus, an odds ratio of 0.51 corresponds to a risk ratio of 0.94 when the baseline risk is 93.3%. The corresponding unadjusted risk ratio is 86%/93.3%=0.92 Correction: In the episode, we stated that the adjusted risk ratio was 0.92. In fact, it is 0.94, as shown above. 0.92 is the unadjusted risk ratio.  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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - Introduction (02:54) - What Are Odds Ratios? (04:02) - Puppy Photos and First Dates (06:09) - Risk Ratio Explained (08:10) - Calculating Odds Ratios (11:09) - Fish Photos and Reversed Numbers (16:01) - Real-Life Example: Sugary Beverages (22:08) - How Logistic Regression Works (31:53) - The Video: Researchers Made the Mistake Themselves (36:30) - The Cardiac Catheterization Study (39:24) - The New York Times Printed a Correction (46:10) - Using OR and RR Interchangeably for Case Control (47:00) - Reye Syndrome and Aspirin (49:37) - Rating the Claim and Methodological Morals

    54 min
  4. Coffee and the Heart: Is caffeine a trigger for AFib?

    May 18

    Coffee and the Heart: Is caffeine a trigger for AFib?

    Does coffee trigger atrial fibrillation — or have doctors been warning people away from caffeine without strong evidence? We dig into two recent randomized clinical trials testing whether caffeinated coffee causes dangerous heart rhythm problems. Along the way, we talk about AFib, survival analysis, intention-to-treat versus as-treated analyses, and one surprisingly elaborate effort to catch clinical trial cheaters with receipts and geolocation tracking. We also explore how a pope may have fueled a European coffee resurgence, why plants make caffeine, and how a game show competition explains hazard ratios. Statistical topics adherence and complianceas-treated analysisconfidence intervalsCox proportional hazards regressionhazard ratiosintention-to-treat analysismicro-randomizationmultiple testingPICOTpre-registrationprimary vs secondary outcomesrandomized clinical trialssensitivity analysesSMART frameworksurvival analysis Methodological morals “Never trust conventional wisdom until you see the randomized controlled trial.”“Trust your participants, but design the study so that they can be honest about their dishonesty.”References Harrington D, D'Agostino RB Sr, Gatsonis C, et al. New Guidelines for Statistical Reporting in the Journal. N Engl J Med. 2019;381(3):285-286. doi:10.1056/NEJMe1906559Marcus GM, Rosenthal DG, Nah G, et al. Acute Effects of Coffee Consumption on Health among Ambulatory Adults. N Engl J Med. 2023;388(12):1092-1100. doi:10.1056/NEJMoa2204737Wong CX, Cheung CC, Montenegro G, et al. Caffeinated Coffee Consumption or Abstinence to Reduce Atrial Fibrillation: The DECAF Randomized Clinical Trial. JAMA. 2026;335(4):317-325. doi:10.1001/jama.2025.21056@MarcKatzMD’s short video The Pitt- atrial fibrillation cardioversion scene 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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - - Introduction (02:15) - - What is AFib? (04:36) - - Frisky Goats and Satan's Bitter Invention (10:44) - - How Caffeine Works (14:43) - - The CRAVE Trial (15:53) - - PICOT: Evaluating the Study Design (23:24) - - CRAVE Results (30:07) - - Catching the Coffee Cheaters (37:01) - - The DECAF Trial (41:30) - - Time-to-Event Outcomes (43:21) - - Hazard Ratios: Balance Beams Over Shark Tanks (47:06) - - DECAF Results: Team Coffee Wins (50:38) - - Why Would Coffee Be Protective? (53:57) - - Rating the Claim

    57 min
  5. Sleep and Exercise: Does working out on too little sleep speed up aging?

    May 4

    Sleep and Exercise: Does working out on too little sleep speed up aging?

    Can exercise actually be bad for you if you don’t get enough sleep? A widely shared claim says yes—that working out while sleep deprived may speed up aging. In this episode, we put that claim under the microscope. We examine the study behind it, unpack how sleep and aging were measured, and explore key statistical ideas like interaction effects and flexible models that can “dance” to the data. With the help of a $400,000 handbag and a man with seven boats, we also break down what it really takes to show that one variable changes the effect of another. What we find: some clear study bloopers, inconsistent modeling results, and interpretations that are flat-out wrong.  Statistical topics Measurement error Model specificationPiecewise linear regressionRegression modelsResidual confoundingSplinesStatistical interactionsSurvey design Methodological morals “Before you believe something shocking, ask what had to go wrong to make it true.”“If slight modeling changes flip the story, there wasn't much story to begin with.”“Unethical Life Pro Tip: If you do not want your analysis critiqued, then just make it impossible to understand.”Kristin’s Biological Age Calculator References Original Viral Tweet: Ng D. "People who slept under 6 hours and exercised actually aged faster." X. March 9, 2026.Holmer B. Does exercise “age you faster” if you don’t sleep enough? Medium. March 16, 2026.You Y. Chen Y. Liu R., et al. Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study. Sci Rep. 2024;14:6247. Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10:573-591.  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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - Introduction (04:05) - What is NHANES? (06:38) - The Sleep Duration Results (12:50) - The 2015 Sleep Mystery (17:10) - Measuring Biological Aging (21:35) - The Penalized Cox Regression (28:16) - Sleep and Aging Results (30:03) - Cubic Splines and Dancing (36:49) - Adding Exercise to the Mix (40:57) - Boats, Handbags, and Interaction Effects (48:20) - The Cubic Spline Exercise Analysis (51:21) - The Opposite Result (55:54) - Academic Writing Gone Wrong (58:27) - The Writing Makeover (01:01:12) - Rating the Claim with Gatorinis

    1h 5m
  6. Sex Recession: Are young people really having less sex?

    Apr 20

    Sex Recession: Are young people really having less sex?

    Are young people really having less sex? Headlines about a “sex recession” suggest a dramatic decline—but what do the data actually show? In this episode, we trace that claim back to the research behind it—and find a story that’s far more nuanced than the headlines suggest. We examine large national surveys, including the General Social Survey and the National Survey of Sexual Health and Behavior, and uncover how small analytical choices can completely change the story. Along the way, we tackle ordinal versus quantitative data, why averages can mislead, how logistic regression reframes the question, and what happens when researchers try to time-travel with statistics. Plus: the surprising role of extreme values, why “eight fewer sexual encounters per year” may not mean what you think, and whether young men and women are really following the same trends. Statistical topics Average vs distributionBinary variablesEffect size vs statistical significanceLogistic regressionMeasurement / operationalizationOrdinal variablesOutliers / extreme valuesSelf-reported datagoogSocial desirability biasVariable coding / transformation Methodological morals “You shouldn't use data from people in their 80s to guess what they were doing in their 20s unless your data come with a time machine.”“When extreme values drive the average, the average stops describing most people.” References Julian K. Why are young people having so little sex? The Atlantic. December 2018. Accessed April 19, 2026. https://www.theatlantic.com/magazine/archive/2018/12/the-sex-recession/573949/Skwarecki B. Nearly half of Gen Z adults have never had sex: report. Newsweek. January 7, 2025. Accessed April 19, 2026. https://www.newsweek.com/nearly-half-of-gen-z-adults-have-never-had-sexreport-11052178Virginity survey. DatingAdvice.com. Accessed April 19, 2026. https://www.datingadvice.com/studies/virginity-surveyTwenge JM, Sherman RA, Wells BE. Declines in sexual frequency among American adults, 1989-2014. Arch Sex Behav. 2017;46(8):2389-2401.Ueda P, Mercer CH, Ghaznavi C, Herbenick D. Trends in frequency of sexual activity and number of sexual partners among adults aged 18 to 44 years in the US, 2000-2018. JAMA Netw Open. 2020;3(6):e203833.Herbenick D, Rosenberg M, Golzarri-Arroyo L, et al. Changes in penile-vaginal intercourse frequency and sexual repertoire from 2009 to 2018: findings from the National Survey of Sexual Health and Behavior. Arch Sex Behav. 2022;51(3):1419-1433.Wellings K, Palmer MJ, Machiyama K, Slaymaker E. Changes in, and factors associated with, frequency of sex in Britain: evidence from three National Surveys of Sexual Attitudes and Lifestyles (Natsal). BMJ. 2019;365:l1525. Published 2019 May 7. doi:10.1136/bmj.l1525Burghardt J, Beutel ME, Hasenburg A, Schmutzer G, Brähler E. Declining Sexual Activity and Desire in Women: Findings from Representative German Surveys 2005 and 2016. Arch Sex Behav. 2020 Apr;49(3):919-925. doi: 10.1007/s10508-019-01525-9. Epub 2019 Dec 4. Erratum in: Arch Sex Behav. 2020 Apr;49(3):927. doi: 10.1007/s10508-019-01622-9. PMID: 31802290.Twenge JM. Possible Reasons US Adults Are Not Having Sex as Much as They Used To. JAMA Netw Open. 2020;3(6):e203889. Published 2020 Jun 1. doi:10.1001/jamanetworkopen.2020.3889 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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - Introduction (04:04) - Fact-Checking the Headlines (07:37) - The Twenge Study and the GSS (16:02) - The Hill-Shaped Trend (19:23) - The Ordinal Variable Problem (24:59) - The Married vs. Never-Married Paradox (27:42) - Time-Traveling to the 1920s (31:38) - The Ueda Study: A Better Approach (35:25) - The Two Classrooms (42:42) - What Counts as Sex? (48:52) - Historical Sex Terms (52:35) - The Sexual Repertoire Results (55:53) - Why Is This Happening? (01:02:12) - Rating the Claim

    1h 7m
  7. Diagnostic Testing: Do the stats tell you what you need to know?

    Apr 6

    Diagnostic Testing: Do the stats tell you what you need to know?

    Diagnostic testing: what do those statistics actually tell you? Sensitivity, specificity, positive predictive value . . . you’ve probably seen these terms before. Maybe you memorized them for a test. But do you actually know what they mean? In this episode, we take a closer look at how diagnostic tests are evaluated—and how they’re often misinterpreted. From a genetic test for cellulite to a blood test for autism, we explore how “statistically significant” findings can turn into tests that don’t actually help anyone. Along the way we meet the freckle gene, the wanderlust gene, and infidelity gene. Statistical topics Base RateBayes RuleCase-Control StudyMatchingConditional ProbabilitySensitivitySpecificityPositive Predictive ValuePrevalenceNegative Predictive ValueFalse Positives and NegativesTrue Positives and NegativesMethodological morals “A biomarker paper is not the same thing as a biomarker test.”“If your sample doesn't match the real world, then for all of your positive predictive value needs, call on Bayes' theorem.”Detailed Show Notes with calculations References Emanuele E, Bertona M, Geroldi D. A multilocus candidate approach identifies ACE and HIF1A as susceptibility genes for cellulite. Journal of the European Academy of Dermatology and Venereology; 2010. 24: 930-35. https://genomelink.io/traits/cellulitehttps://www.genexdiagnostics.com/ Ebstein RP, Novick O, Umansky R, et al. Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait of Novelty Seeking. Nat Genet. 1996;12:78-80. Kluger AN, Siegfried Z, Ebstein RP. A meta-analysis of the association between DRD4 polymorphism and novelty seeking. Mol Psychiatry. 2002;7:712-7.He Y, Martin N, Zhu G, Liu Y. Candidate genes for novelty-seeking: a meta-analysis of association studies of DRD4 exon III and COMT Val158Met. Psychiatr Genet. 2018 Dec;28(6):97-109. Smith AM, King JJ, West PR, et al. Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder. Biol Psychiatry. 2019;85:345-54.Sainani K, Goodman S. Lack of Diagnostic Utility of “Amino Acid Dysregulation Metabotypes.”Biol Psychiatry. 2018; 85: e41-e42. 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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - Introduction (02:24) - The Cellulite Test (06:41) - Understanding Sensitivity and Specificity (12:50) - Enter Positive Predictive Value (18:40) - Why Base Rates Matter (24:06) - More Ridiculous Tests (32:33) - The Wanderlust Gene Deep Dive (40:30) - The NeuroPoint Autism Test (51:37) - Trying to Set the Record Straight (01:00:42) - Personal Stories (01:03:57) - Wrap-up

    1h 7m
  8. Epidurals: Are labor epidurals really linked to autism?

    Mar 23

    Epidurals: Are labor epidurals really linked to autism?

    Epidurals are widely used and widely trusted for pain relief during labor. So when a 2020 study reported that they might be linked to autism, it raised a troubling question: could a routine medical decision have long-term consequences? We follow that claim from headline to evidence—and watch what happens when other scientists take a closer look. We dig into the original study, a wave of replication studies from around the world, and a meta-analysis that tries to make sense of it all. Along the way, we unpack hazard ratios, Cox regression, inverse probability weighting, and sibling analyses—and why even sophisticated statistical adjustment can’t eliminate confounding. Plus: why bigger datasets don’t solve everything, what happens when results shrink after adjustment, and how a controversial study turned into a case study in science working as it should. Bonus: our first guest journalist interview! Statistical topics ConfoundingCox regressionHazard ratiosInverse probability weighting (IPTW)Multivariable adjustmentObservational studiesResidual confoundingRetrospective cohort studiesSibling analysisStatistical adjustmentStatistical significance vs practical significanceSurvival analysis Methodological morals “Every time you adjust the model and the effect gets smaller, that's the universe whispering, maybe don't build a causal story out of this.”“Consistency across studies is gold.”“There's more to the story than the statistics.” References Dattaro, Laura. A questionable study linked autism to epidurals. Then what? Spectrum. April 18, 2023. Dattaro, Laura. How to find baby sharks. Nautilus. September 9. 2024.Laura Dattaro’s home page.Phil Kearney’s blog post about the SMART framework.Qiu C, Lin JC, Shi JM, et al. Association Between Epidural Analgesia During Labor and Risk of Autism Spectrum Disorders in Offspring. JAMA Pediatr. 2020;174:1168-1175. Joint Statement. Labor Epidurals Do Not Cause Autism; Safe for Mothers and Infants, say Anesthesiology, Obstetrics, and Pediatric Medical Societies. American Society of Anesthesiologists. October 12, 2020.Wall-Wieler E, Bateman BT, Hanlon-Dearman A, Roos LL, Butwick AJ. Association of Epidural Labor Analgesia With Offspring Risk of Autism Spectrum Disorders. JAMA Pediatr. 2021;175:698-705. Christakis DA. More on epidurals and autism. JAMA Pediatrics. 2021; 175: 705.Mikkelsen AP, Greiber IK, Scheller NM, Lidegaard Ø. Association of Labor Epidural Analgesia With Autism Spectrum Disorder in Children. JAMA. 2021;326:1170–1177. Hanley GE, Bickford C, Ip A, et al. Association of Epidural Analgesia During Labor and Delivery With Autism Spectrum Disorder in Offspring. JAMA. 2021;326:1178-1185. Hegvik TA, Klungsøyr K, Kuja-Halkola R, et al. Labor epidural analgesia and subsequent risk of offspring autism spectrum disorder and attention-deficit/hyperactivity disorder: a cross-national cohort study of 4.5 million individuals and their siblings. Am J Obstet Gynecol. 2023;228:233.e1-233.e12. Epub 2022 Aug 13. Hu X, Wang B, Chen J, Han D, Wu J. Association Between Epidural Labor Analgesia and Autism Spectrum Disorder in Offspring: A Systematic Review and Meta-Analysis. J Pain Res;17:227-240. 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/X Regina - LinkedIn & ReginaNuzzo.com (00:00) - Intro (01:40) - Why autism is hard to study (05:18) - The original 2020 study (11:38) - Results & hazard ratios (15:24) - Confounding & adjustment (26:32) - Criticism & plausibility (34:11) - Replications begin (44:38) - Converging evidence & meta-analysis (50:48) - What does it mean? (53:38) - Guest & wrap-up

    1h 9m
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About

Normal Curves is a podcast about sexy science & serious statistics. Ever try to make sense of a scientific study and the numbers behind it? Listen in to a lively conversation between two stats-savvy friends who break it all down with humor and clarity. Professors Regina Nuzzo of Gallaudet University and Kristin Sainani of Stanford University discuss academic papers journal club-style — except with more fun, less jargon, and some irreverent, PG-13 content sprinkled in. Join Kristin and Regina as they dissect the data, challenge the claims, and arm you with tools to assess scientific studies on your own.

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