In this episode of "In the Interim…", Dr. Scott Berry, Dr. Elizabeth Lorenzi, and Dr. Amy Crawford discuss the STEP platform trial’s statistical methodology for evaluating which acute stroke patients benefit and which do not from endovascular therapy (EVT). The discussion critiques the inadequacy of traditional clinical trials powered for a single population to show benefit, as the goal of the trial is to identify who benefits, not if the entire population has a net benefit. The team walks through the development and simulation of a Bayesian change point model, addressing heterogeneous treatment responses across the NIH Stroke Scale. The adaptive platform design leverages scheduled interim analyses to draw timely, data-driven conclusions about patient subgroups, improving trial efficiency and relevance. The episode also previews scaling to two-dimensional modeling, incorporating both stroke severity and time since last known well, and emphasizes ongoing clinical trial simulation and close integration between clinicians and statisticians throughout trial design and execution.

Key Highlights

• STEP platform master protocol and the NIH StrokeNet collaborative infrastructure
• Clinical rationale for Bayesian change point modeling of the effect of EVT across the patients
• Shift from single to dual change point models to reflect regions of equivalence
• Development of custom C code and MCMC samplers due to limits of standard tools
• Interim analyses direct adaptive enrollment and define actionable conclusions
• Future extensions to multidimensional change point curves modeling 

This episode of "In the Interim…" features Dr. Scott Berry, Dr. Kert Viele, and Dr. Melanie Quintana of Berry Consultants dissecting the technical and operational landscape of Bayesian statistics in clinical trial design. The episode discussed what is Bayesian statistics, the impact of informative and non-informative priors, and clarifies when and why Bayesian approaches surpass frequentist analyses—especially in adaptive, platform, and rare disease trial settings. The discussion directly challenges the misconception that Bayesian methods “lower the bar," presenting evidence that they often require broader data synthesis and can raise evidentiary standards.

Key regulatory developments at FDA and EMA are reviewed, with attention to updated guidance and increased adoption. Case studies illustrate Bayesian methods in practice, including the prospectively combined phase 2 and 3 analysis for REBYOTA approval; hierarchical modeling in GNE myopathy; shared controls and endpoint integration in the HEALEY ALS Platform Trial; and robust subgroup borrowing in the ROAR basket trial. The team also addresses technical challenges such as multiplicity, subgroup analysis, complexity in endpoint modeling, and appropriate strategies for blending Bayesian and frequentist approaches for maximum regulatory and scientific clarity.

Key Highlights

• Clear explanation and real-world examples of Bayesian analysis in clinical trials.
• Theoretical and practical distinctions from frequentist methods
• Practical breakdown of control sharing, endpoint integration, and subgroup borrowing.
• Regulatory position and the increasing acceptance of Bayesian trial designs and analyses.
• Case examples: REBYOTA, GNE myopathy, HEALY ALS Platform Trial, ROAR basket trial.

Dr. Scott Berry and Dr. Kert Viele discuss the origins and implementation of the “time machine” modeling approach, beginning with sports analytics and progressing to adaptive platform clinical trials. The episode focuses on how techniques for comparing athletes across eras translate into methodology for platform trials.

Key Highlights

• Sports analytics as foundation: Early work of modelling athlete comparisons across eras using bridging methodologies.
• Platform trial application: The time machine model in I-SPY 2 enabled efficient control allocation through overlapping arms over extended trial periods.
• Core modeling principles: Additive treatment effect assumptions and the necessity of sufficient temporal overlap for reliable era comparisons.
• Statistical implementation: Approaches include categorical era adjustment and Bayesian smoothing splines for modeling change over time.
• Limitations and disease specificity: In conditions with rapid clinical or epidemiologic change, such as COVID-19, non-concurrent controls are avoided due to high risk of era by treatment interaction.
• Regulatory and methodological distinction: The model leverages within-trial overlapping data collected under a unified protocol, contrasting sharply with external or historical controls.

In this episode, Dr. Don Berry and Dr. Scott Berry provide an in-depth account of I-SPY 2, focusing on the trial’s use of the “time machine” methodology—a Bayesian solution allowing bridging across arms to inform ongoing analyses. The discussion details how predictive probabilities and adaptive randomization shaped pivotal decisions, including the handling of Pertuzumab’s approval and Neratinib’s subtype-specific performance. This episode also documents the technical and operational contributions of Laura Esserman, Anna Barker, Janet Woodcock, Meredith Buxton, and Ashish Sanil, clarifying the roles that enabled the platform’s success and broader impact on subsequent adaptive trials.

Key Highlights

• Introduction of the “time machine” concept, enabling valid comparison between experimental and control arms even when enrollment periods differ—a pragmatic solution originally utilized in sports examples for evolving platform trials as treatments and control arms change.
• Ongoing trial conduct driven by a Bayesian adaptive algorithm, developed and maintained by Berry Consultants statisticians, which computes predictive probabilities to guide arm graduation, futility, and real-time adjustment of randomization probabilities.
• Neratinib serves as a case study in subtype-specific adaptive randomization: the platform set randomization probability to zero in subtypes without signal, while effective subtypes increased randomization and advanced to graduation.
• I-SPY 2’s methodologies shaped subsequent adaptive platform trials (GBM AGILE, Precision Promise, COVID-19 ACTIV networks), with regulatory acceptance reflected in FDA guidance and Janet Woodcock’s public recognition of adaptive randomization as “adequate and well controlled” for registration studies.
• Specific recognition: Laura Esserman (trial leadership), Anna Barker (funding and strategic input), Janet Woodcock (FDA guidance and adaptive methods support), Meredith Buxton (logistics; GCAR leadership), and Ashish Sanil (Berry Consultants; ongoing algorithm implementation).

In Episode 20 of Berry’s "In the Interim..." Podcast, The Legend of I-SPY 2 - Part A, Dr. Don Berry and Dr. Scott Berry discuss the origins and design of the I-SPY trials. Their conversation explains the inefficiency of traditional adjuvant breast cancer trials and details the shift to the neoadjuvant approach, where tumor response can be observed prior to surgery. 

I-SPY 1 served as a proof-of-concept using MRI for probabilistic prediction of pathologic complete response (pCR). I-SPY 2 represents a major advancement in clinical trial science, introducing a multi-arm bandit methodology, integration of biomarker-driven subtypes and signatures, and a structured funding model that transitioned from philanthropy to “pay to play” industry support.

This episode of "In the Interim..." features an in-depth discussion of the StrokeNet Thrombectomy Endovascular Platform (STEP), a multi-domain, multi-factorial, adaptive platform trial for acute stroke, anchored in the NIH StrokeNet network. Guests Dr. Eva Mistry (University of Cincinnati) and Dr. Jordan Elm (Medical University of South Carolina) join us to explain how STEP enables simultaneous investigation of multiple treatment strategies in patients with acute ischemic stroke. The conversation details the use of a master protocol, the integration of industry partners through the Other Transactional Authority (OTA) NIH mechanism, and innovative statistical designs to efficiently identify improved treatment strategies.

Key Highlights:

• STEP utilizes a master protocol within NIH StrokeNet, unifying eligibility, procedures, and data collection across all study domains.
• The platform supports multiple research questions.
• In an initial domain STEP applies a statistical change-point model to empirically estimate the thresholds where EVT is effective, neutral, or potentially deleterious based on medium vessel occlusions and baseline clinical status. 
• Protocols may be adapted in response to new external data, including pausing and revising enrollment in specific subpopulations when emerging science warrants.
• Shared control groups are used wherever applicable, improving trial efficiency by reducing the number of patients allocated to control arms and allowing eligible patients to contribute to multiple domains when protocol and scientific rationale permit.

Dr. Scott Berry applies a statistician’s review of a random trial result published in JAMA – the FAIR-HF2 clinical trial.  Interrogating the frequentist paradigm and the focus on the binary outcome of the primary hypothesis test. He scrutinizes the Hochberg multiplicity adjustment, challenges the prevailing disregard for accumulated scientific evidence, and contrasts the limitations of black/white view of clinical trial of over 1000 patients and 6 years of enrollment. A contrast is made to what a potential Bayesian approach, grounded in practical trial interpretation and evidence integration would look like. The episode argues how current norms, created by dogmatic statistical views, in clinical trial analysis can obscure or perhaps mislead from meaningful findings and limit the utility of costly, complex studies.

Key Highlights

• FAIR-HF2 randomized 1,105 patients with heart failure and iron deficiency to intravenous ferric carboxymaltose or placebo across 70 sites, with three pre-specified co-primary analyses.
• The study relied on the Hochberg procedure to control family-wise error across analyses: (1) time to first cardiovascular death or heart failure hospitalization; (2) total heart failure hospitalizations; (3) time to first event in a highly iron-deficient subgroup.
• Results showed a favorable hazard ratio (0.79) and a p-value below 0.05 for primary composite 1, but statistical significance was nullified under Hochberg multiplicity criteria as other endpoints failed threshold requirements.
• Berry challenges the reduction of trial outcomes to discrete “significant” or “not significant” designations—critiquing the scientific and statistical culture that ignores gradient evidence in favor of only black-and-white outcomes.
• He details the likelihood principle and Bayesian analysis as superior frameworks, quantifying a 98% posterior probability of benefit; he contextualizes findings with prior evidence from the HEART-FID, IRONMAN, and AFFIRM-AHF trials and published meta-analyses—arguing that isolated, negative conclusions defy cumulative data.
• The discussion extends to the inefficiency of fixed trial designs, the missed value in adaptive methodologies, and the inefficacy of requiring full-scale repeat trials all analyzed in isolation, when evidence already points strongly to a beneficial effect.

Scott Berry convenes co-authors Kert Viele, Joe Marion, and Lindsay Berry to discuss the statistical and developmental nuances of inferentially seamless phase 2/3 clinical trial designs. The group dissects the simple method for distributing alpha when including stage 1 data, whether it is a good idea to distribute alpha, and the optimal allocation of sample size when Stage 1 data are carried forward, all referencing their recently published work in Pharmaceutical Statistics.

Key Highlights:

• Systematic definition of seamless phase 2/3 trial designs, contrasting fixed, separate-phase models with integrated, inferentially seamless approaches.
• Detailed explanation of the required alpha adjustment when selecting doses partway through—leveraging group sequential theory, normal approximations, and quadrature for explicit formula derivation; R code and calculation procedure are made available for practitioners.
• Exploration of the information fraction curve for adjusted alpha, emphasizing that initial adjustment is numerically significant but does not inherently reduce statistical power.
• Findings indicate that power is always higher when including stage 1 data – and outperforms a closed testing procedure.
• Discussion of when seamless trials may not be advantageous: operational and statistical limitations: insufficient endpoint/regulatory understanding for phase 3, differences in manufacturing readiness, need for public phase 2 results for funding, and proof of concept hurdles; identifies real scenarios where seamless 2/3 designs are challenging.
• Considerations for operational bias and blinding, with technical commentary on the boundaries of unblinding within company roles.
• Provision of practical R code and explicit analytic guidance for calculating adjusted alpha in seamless design protocols.

In this solo episode of "In the Interim...", Scott Berry, President and Senior Statistical Scientist at Berry Consultants, addresses deep-rooted confusion in the field of adaptive clinical trial design surrounding the concept of “spending alpha.” Drawing on practical experience and rigorous statistical foundations, Berry addresses the prevailing language and myths that conflate interim analysis with loss of type I error. He clarifies that, with planned and transparent allocation of alpha, interim analyses enable more power with more efficient design, and robust clinical trials—without sacrificing statistical validity. This is a precise and fact-driven examination for those demanding technical clarity, not marketing gloss.

Key Highlights

• Explains the basics of hypothesis testing in superiority trials, highlighting why a one-sided 2.5% alpha is the operational standard despite persistent use of two-sided 5% language in clinical protocols.
• Refutes the widespread belief that reviewing interim data costs available alpha, making clear that statistical error is not “penalized”—it is allocated, with potential efficiencies in average sample size and, in thoughtfully extended designs, gains in operating characteristics such as power.
• Describes real-world examples, including the SEPSIS-ACT (selepressin) trial sponsored by Ferring Pharmaceuticals, which incorporated more than 20 interim analyses while maintaining a pre-specified final alpha of 0.025; underscores the necessity of transparent, prospective design and explicit documentation for regulatory acceptance.
• Distinguishes between interim actions—such as futility analyses or response-adaptive randomization, which require no alpha adjustment, and early efficacy analyses, which must be precisely modeled to preserve type I error.
• Challenges terminology like “penalty” and “spending alpha,” asserting that imprecise language fosters misunderstanding and leads to missed opportunities in adaptive trial efficiency.
• Emphasizes the crucial role of prospective, simulation-based planning and clear protocol definition at every interim, anchoring statistical practice in measured evidence, not historical convention.

Scott Berry, Founder of Berry Consultants, interviews Professor Craig Ritchie—specialist in brain health and neurodegenerative diseases, Chief Investigator of EPAD (European Prevention of Alzheimer Dementia), and CEO of Scottish Brain Sciences—for a broad discussion of platform trial methodology in Alzheimer’s Disease research as well as looking towards the future of drug development. The conversation dissects the origins and ambitions of the EPAD initiative, the conception and scientific function of the readiness cohort, and the pragmatic obstacles to deploying innovative trial models within rigid institutional frameworks. Professor Ritchie details why the EPAD platform trial failed to initiate any therapies, explores the fallout and industry shifts following COVID-19, and maps how Scottish Brain Sciences is directly applying these lessons—establishing the IONA readiness cohort to drive integration between clinical research and clinical practice. 

Key Highlights
• Systematic review of EPAD’s objectives, specifically the platform trial and the development of a readiness cohort to streamline patient recruitment
• Detailed account of practical barriers that prevented EPAD from launching interventional arms, including pharmaceutical sponsor reluctance, inflexible IMI funding mechanisms, and the inherent risk aversion surrounding novel platform structures
• Discussion of participant contribution to research design and delivery—an early demonstration of patient involvement models now broadly recognized as best practice
• Analysis of COVID-19's dual impact—derailing EPAD's momentum while catalyzing a change in industry and regulatory acceptance of platform trials in drug development
• Tracing the origins and operationalization of the IONA readiness cohort at Scottish Brain Sciences, including direct integration of recruitment, biobanking, and engagement systems to address the translational gap in dementia medicine
• Evidence-based critique of persistent use of conventional clinical trial formats in Alzheimer’s disease, dissecting operational, financial, and data limitations that stall progress

In this engaging episode of "In the Interim...", host Dr. Scott Berry is joined by Dr. Nick Berry to explore the intriguing statistical parallels between sports and drug development, focusing on the concept of "regression-to-the-mean." Presenting examples that seem clear in sports, they discuss how these insights can illuminate the challenges faced in clinical trials and scientific inferences in medical decision making. Whether you're a statistician, drug developer, or sports enthusiast, this episode offers valuable perspectives on data interpretation and statistical phenomena.

Key Highlights:
• Discussion on how lessons from sports can benefit drug developers, emphasizing the concept of regression-to-the-mean.
• Personal anecdotes from Scott and Nick's experiences, illustrating statistical learning through sports.
• Examination of the regression-to-the-mean phenomenon through examples from baseball and golf.
• Exploration of how misunderstanding the regression-to-the-mean can lead to poor decision-making in clinical trials.
• Insights into placebo effects and how they are often confused with natural statistical phenomena.
• How regression-to-the-mean impacts expectations in financial markets and personal finance decision-making.

In this episode of "In the Interim…", host Dr. Scott Berry is true to the name of the podcast, as he discusses the unblinded world of adaptive clinical trials alongside Dr. Roger Lewis, a renowned expert in both statistical science and clinical medicine. Together, they explore the critical role of Data Safety Monitoring Boards (DSMBs) in safeguarding trial integrity and participant safety specifically for adaptive trials. The discussion navigates the complexities and challenges faced by DSMBs, particularly in adaptive trial contexts, offering valuable insights for anyone involved in clinical trial science.

Key Highlights
• Overview of the fundamental role and responsibilities of DSMBs in clinical trials.
• Discussion on how DSMBs ensure scientific integrity and participant safety in adaptive trials.
• Differences in DSMB involvement between traditional and adaptive trial designs.
• The evolving skillset required for DSMB members in the context of complex, adaptive trials.
• Exploration of the critical collaboration between DSMBs and Statistical Analysis Committees.

Quotes
• "The DSMB is tasked with balancing efficacy and safety at a very fundamental level." — Roger Lewis
• "Adaptive trials expand the role of the DSMB to ensure trials are conducted as intended." — Roger Lewis
• "The DSMB needs to review efficacy and safety to appropriately balance them." — Roger Lewis

In the latest episode of "In the Interim…", Dr. Scott Berry and Dr. Mike Krams sit down with Dr. Husseini Manji, to explore the potential of platform trials in advancing precision medicine within psychiatry. Listen as we discuss how an adaptive platform trial could transform drug development, paving the way for breakthroughs in understanding and treating psychiatric disorders.

Key Highlights:

• Overview of the burden of serious mental illness and the pressing need for innovative treatment approaches.
• Discussion on precision psychiatry and the potential of a platform trial to address the heterogeneity of psychiatric disorders.
• Insights into the advantages of biomarker-based adaptive trials in improving drug development success rates.
• Examination of potential sponsorship models for platform trials, emphasizing patient and industry collaboration.

Quotes:

• "Mental illnesses represent a significant global challenge with a staggering unmet need." – Husseini Manji
• "There's a real excitement about precision psychiatry—moving away from a one-size-fits-all approach." – Husseini Manji
• "The patient perspective is crucial for driving significant advances in psychiatric treatment." – Mike Krams
• "We believe that precision medicine biomarker-based adaptive trials could be game-changing in this space." – Husseini Manji

In Episode 11 of "In the Interim…", we discuss the nuances of implementing adaptive clinical trials with Dr. Anna McGlothlin and Dr. Michelle Detry from Berry Consultants. Both Anna and Michelle, seasoned Directors and Senior Statistical Scientists, shed light on the critical role their team plays in innovative adaptive clinical trials. They describe the frequent challenges and highlight the importance of high-quality trial implementation to ensure accurate and reliable outcomes, making this episode a must-listen for anyone involved in clinical trials.

Key Highlights:

• Insight into the statistical implementation of adaptive clinical trials.
• Logistics of data handling, to running the statistical model, to interactions with Data and Safety Monitoring Boards (DSMBs).
• Preparatory steps required before an adaptive analysis, ensuring the pre-specified design is adhered to and carried out as planned.
• The importance of understanding data in real-time and dealing with interim data idiosyncrasies.

Quotes:

• "We want the adaptive part of the trial to be invisible to sites—analyses might happen in the background without interference." – Scott Berry
• "Our goal is five business days from when we receive the data to when we send the result to the DSMB." – Michelle Detry
• "We always want to make sure that we have time, not just to hit a button and run an analysis and spit out a table, but to think and make sure that the results we’re producing make sense." – Anna McGlothlin

In this episode of "In the Interim..." we revisit the ground-breaking seamless phase 2/3 clinical trial for the GLP-1 agonist, dulaglutide—better known as Trulicity. We discuss the intricacies of the adaptive trial design, and the unique features that helped expedite development by 12-18 months. Listeners will gain insight into how Bayesian algorithms and innovative statistical methods were pivotal in navigating a complex trial design, benefiting Eli Lilly's pipeline and changing the landscape of diabetes treatment.

Key Highlights:

• Outline of the trial design and the barriers faced during its inception in 2007-2008.
• Explanation of the Clinical Utility Index and its role in adaptive randomization.
• The DSMB's role and interaction with Bayesian decision-making models.
• Simulation-based design to optimize development efficiencies.
• Insights into the predictive power of the trial on weight loss outcomes in subsequent trials.

Quotes:

• "The trial was run entirely by Bayesian algorithms." – Scott Berry
• "They believed this utility function was absolutely the right way to go forward." – Scott Berry

In this episode of "In the Interim...," we sit down with Dr. Meredith Buxton to explore the evolution of platform trials from I-SPY 2 to GBM AGILE and Beyond. With a rich history in innovative trial design, Meredith shares the journey from pioneering adaptive clinical trials in breast cancer with I-SPY 2 to her current role at the Global Coalition for Adaptive Research (GCAR). This conversation offers insights into accelerating clinical trial timelines, innovative operational frameworks, and their applications across multiple medical domains, making it a must-listen for anyone involved in clinical development and platform trials.

Key Highlights:

• Meredith Buxton discusses the origins and groundbreaking operations of the I-SPY platform in breast cancer.
• Exploration of how the I-SPY 2 model inspired subsequent platform trials in diverse areas such as glioblastoma and COVID-19.
• GCAR's role as a non-profit entity to foster adaptive trial designs and Meredith’s influential contributions to its formation and success.
• Discover the operational complexities and regulatory considerations essential for modern platform trials.
• Insights into Meredith’s vision for the future of drug development and the ongoing necessity for innovation in trial design.

Quotes:

• “The ideas of this are groundbreaking in many ways.” – Scott Berry
• "The I-SPY2 model could be replicated in other spaces." – Meredith Buxton

In this episode of "In the Interim...," Scott Berry and Kert Viele navigate the nuanced debate surrounding the integration of external data in clinical trials. Discover the implications and potential benefits and pitfalls of leveraging historical and real-world evidence in the analysis of clinical trials.

Key Highlights:

• Exploration of how external data can influence clinical trial analyses and the inherent risks versus rewards.
• Examination of the frequentist versus Bayesian perspectives on data integration.
• Discussion of real-world cases where external data has been used.
• Debate on the conservative nature of current scientific approaches and how they may hinder progress.
• Insight into the future of clinical trials harnessing external data – a step towards better medical science.

Quotes:

• "If prior data isn't generally leading us in the right direction, we ought to reconsider the basis of scientific inquiry." – Kert Viele
• "The industry's hesitance to use existing data slows innovation and limits our ability to bring effective treatments to market." – Scott Berry

In this episode of "In the Interim," Don Berry shares the life and work of Jimmie Savage, his advisor and a legendary figure in Bayesian statistics. Hosted by Scott Berry, the discussion reveals the personal and professional experiences that shaped Savage's groundbreaking contributions. Discover the intricacies of Savage's influence on statistical thought and his profound legacy, from his tragic childhood to a profound effect on Bayesian statistics and scientific thought.

Key Highlights:

• Don Berry shares the personal story of Jimmie Savage's troubled childhood and how it influenced his work and personality.
• Insights into Savage's pioneering role as the father of modern Bayesian statistics.
• Discussion on Savage's varied interests and collaborations with figures like Milton Friedman and John von Neumann.
• Don Berry recounts his academic experiences alongside Savage and his own journey into clinical trial design.
• Exploration of Savage's legacy through his students and his axiomatic approach to subjective probability.

Quotes:

• "I think he's the father of modern Bayesian statistics. How can you argue about that?" – Don Berry
• "The world around you when you're with Savage is tingling with intellect." – Don Berry
• " We probably wouldn't exist... if it were not for him." – Don Berry

In this compelling episode of "In the Interim," Dr. Mike Krams, a seasoned expert in clinical trials and drug development, joins us to discuss the art and slog of innovation in pharmaceutical companies. With over 30 years in the field, Dr. Krams shares insights on leveraging Bayesian statistics and innovative designs to transform development approaches. The conversation explores disruptive approaches to drug development, the importance of having champions for change, and the future of innovation in clinical trials. Mike highlights the necessity of integrating strategic decision-making with statistical expertise to enhance the efficiency and effectiveness of clinical trials.

Key Highlights:

• Discussion on the ASTIN Stroke Trial, a groundbreaking experience with Bayesian methodology in drug trials.
• Examination of how adaptive designs can lead to more efficient learning processes in clinical research.
• Exploration of the cultural and strategic challenges of bringing innovative trial designs to conservative pharmaceutical environments.
• Insight into the vital role of having internal champions to advocate for change and innovation.
• The importance of integrating strategic thinking with statistical expertise to drive innovation forward.

Quotes:

• "Respect is earned, but very good communication skills are a necessary condition for implementing innovation." – Mike Krams
• "Innovation for innovation's sake is not the goal; it's about making better decisions for future patients." – Mike Krams

In this episode of "In the Interim," Dr. Scott Berry discusses the vital topic of ordinal outcomes in clinical trials—a subject as controversial as politics and religion at the dinner table. Using historical examples like James Lind's 1747 scurvy trial and Austin Bradford Hill’s pioneering randomized trial, the episode explores the complexities and ongoing debates about analyzing ordinal endpoints. Berry challenges conventional analysis methods and advocates for more refined, explicit approaches, delivering valuable insights for statisticians, clinicians, and anyone involved in clinical trial designs.

Key Highlights

• Examination of the historical context of ordinal outcomes, starting with James Lind's 1747 scurvy trial.
• Discussion of the first randomized human clinical trial by Austin Bradford Hill and its implications for ordinal endpoint analysis.
• Exploration of the Modified Rankin Score as a current example of ordinal outcomes in stroke trials.
• Critique of conventional methods like dichotomization and proportional odds models for analyzing ordinal data.
• Argument for adopting utility-based approaches in clinical trial analysis for meaningful outcomes.

Quotes

• "Almost every endpoint is ordinal. So you can't escape this." – Scott Berry
• "My claim is nobody has that weight. But yet, it's very commonly done." – Scott Berry
• "Hiding behind ad hoc ways to do this, I think just leads us to bad places." – Scott Berry