Can the meningitis vaccine do double duty against gonorrhea? We examine a fascinating randomized trial inspired by years of encouraging observational studies—and what happened when those earlier results were finally put to the test. Along the way, we discuss ecological studies, case-control studies, why statisticians distrust phrases like “trend toward significance,” how intention-to-treat differs from per-protocol analysis, and why no amount of statistical adjustment can fully substitute for randomization. Along the way we celebrate outrageously named clinical trials, ask whether “flirting with statistical significance” belongs in a romance novel or a medical journal, and discover that “practice safe statistics” may be the best advice of the episode.

Statistical topics

• Case-control studies
• Confounding
• Ecological studies
• Generalizability
• Intention-to-treat
• Meta-analysis
• Null results
• Observational studies
• P-values
• Per-protocol analysis
• Randomized clinical trials
• Survival analysis

Methodologic Morals

• “Don't mistake a p-value of 0.06 for a p-value of 0.04 with bad luck.”
•  “Practice safe statistics; randomize whenever possible.”

References

Seib KL, Donovan B, Jin F, et al. Meningococcal B Vaccine to Prevent Neisseria gonorrhoeae Infection. N Engl J Med. 2026; 395: 349-61. 

Petousis-Harris H, Paynter J, Morgan J, et al. Effectiveness of a group B outer membrane vesicle meningococcal vaccine against gonorrhoea in New Zealand: a retrospective case-control study. The Lancet. 2017; 390: 1603–10.

Wang, B., Mohammed, H., Andraweera, P., McMillan, M., Marshall, H., 2024. Vaccine effectiveness and impact of meningococcal vaccines against gonococcal infections: A systematic review and meta-analysis. Journal of Infection. 2024; 89: 106225. 

Molina JM, Bercot B, Assoumou, L, et al. Doxycycline prophylaxis and meningococcal group B vaccine to prevent bacterial sexually transmitted infections in France (ANRS 174 DOXYVAC): a multicentre, open-label, randomised trial with a 2 × 2 factorial design. The Lancet Infectious Diseases. 2024; 24: 1093–1104.

Thng C, Eskandari S, Jin F, et al. Efficacy of the meningococcal vaccine against Neisseria gonorrhoeae: a randomised clinical trial (MenGO). npj Vaccines. 2026.

Still Not Significant | Probable Error: List of funny disguises scientists use to describe p>.05

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

• (00:15) - - Introduction

Can you improve your endurance by sitting in a hot tub instead of doing another hard workout? We dig into a study that put elite runners through five weeks of what we affectionately call “endurance hot tubbing” to see whether heat alone could trigger the same cardiovascular adaptations as altitude training. Along the way, we explore crossover study designs, multiple testing, why significant physiological changes don’t necessarily translate into better athletic performance, and how an exploratory regression analysis ended up answering the wrong scientific question. We also discover that serious hot tub research involves surprisingly little relaxation, debate whether bubbles should count as an experimental condition, and learn why distance runners pay good money to sleep in tents with some of the oxygen sucked out.

Statistical topics

• best subsets regression
• causal inference
• crossover studies
• exploratory analyses
• mediation / mechanisms
• model specification
• multiple testing
• peer review
• sample size

• (00:15) - Introduction

Methodologic Morals

• “A model can be very good at answering the wrong question.”
•  “The label ‘peer reviewed’ does not guarantee ‘carefully reviewed’.”

References

• Jenkins EJ, Killick JA, Zerilli O, et al. Long-term passive heat acclimation enhances maximal oxygen consumption via haematological and cardiac adaptation in endurance runners. J Physiol. Published online November 20, 2025. doi:10.1113/JP289874
• Stembridge M, Jenkins E. Marathon training: Why hot baths might help you run faster. The Conversation. Published March 16, 2026. doi:10.64628/AB.6vknm9esh

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

• (00:15) - Introduction

Could four one-minute bursts of exercise really improve blood sugar? We try “exercise snacks” ourselves before taking a close look at the clinical trial that inspired headlines. We explain why the study’s main result wasn’t statistically significant, how 34 secondary outcomes complicated the story, and what pre-registration can reveal about a study after it’s published. Along the way, we compare notes on our own exercise-snacking adventures, debate continuous glucose monitors, and ask how much evidence a single study should generate before it becomes health news.

Statistical topics

• Crossover design
• Multiple testing
• Pre-registration
• Primary vs secondary outcomes
• Randomized controlled trial
• Research transparency

Methodologic Morals

• “It's good relationship advice to be transparent. It's also good research advice.”
• “If the primary outcome is not significant, say it up top.”

References

• Babir FJ, Marcotte-Chénard A, Sandilands RE, et al. Exercise snacks performed in real-world settings reduce postprandial hyperglycaemia and glycaemic variability in individuals living with type 2 diabetes: a randomised crossover study. Diabetologia. 2026;69(8):2200-2211. doi:10.1007/s00125-026-06741-2
• https://www.washingtonpost.com/wellness/2026/05/28/4-minutes-exercise-day-could-help-control-blood-sugar/
• clinicaltrials.gov pre-registration with changes: https://clinicaltrials.gov/study/NCT06382246?term=NCT06382246&rank=1&tab=history&a=1&b=2#version-content-panel
  
  

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

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 screening
• exploratory analyses
• hierarchical testing
• missing data
• multiple testing
• outcome measures
• post hoc analyses
• pre-registration
• primary and secondary outcomes
• randomized clinical trials
• screening 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-20-1134
• Neal 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/cancers14194818
• ASCO slides: https://grail.com/wp-content/uploads/2026/05/Swanton_ASCO-2026_NHS-Galleri_FINAL-Slides-05.26.2026.pdf
• UK 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

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 screening
• case-control studies
• counterfactuals
• machine learning
• negative predictive value
• overdiagnosis
• positive predictive value
• randomized clinical trials
• screening tests
• sensitivity and specificity
• validation

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

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 outcomes
• case-control studies
• logistic regression
• odds ratios
• risk ratios
• odds 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 ratios
• Reporting 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.aspx
• RR = 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

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 compliance
• as-treated analysis
• confidence intervals
• Cox proportional hazards regression
• hazard ratios
• intention-to-treat analysis
• micro-randomization
• multiple testing
• PICOT
• pre-registration
• primary vs secondary outcomes
• randomized clinical trials
• sensitivity analyses
• SMART framework
• survival 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/NEJMe1906559
• Marcus 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/NEJMoa2204737
• Wong 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

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 specification
• Piecewise linear regression
• Regression models
• Residual confounding
• Splines
• Statistical interactions
• Survey 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

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 distribution
• Binary variables
• Effect size vs statistical significance
• Logistic regression
• Measurement / operationalization
• Ordinal variables
• Outliers / extreme values
• Self-reported datagoog
• Social desirability bias
• Variable 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-11052178
• Virginity survey. DatingAdvice.com. Accessed April 19, 2026. https://www.datingadvice.com/studies/virginity-survey
• Twenge 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.l1525
• Burghardt 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

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 Rate
• Bayes Rule
• Case-Control Study
• Matching
• Conditional Probability
• Sensitivity
• Specificity
• Positive Predictive Value
• Prevalence
• Negative Predictive Value
• False Positives and Negatives
• True Positives and Negatives

Methodological 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/cellulite
• https://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