American Statistical Association Research Articles

Design Strategy and Consideration for Oncology Dose-Optimization: An Industry Perspective

The conventional more-is-better dose selection paradigm is often inappropriate for novel molecularly targeted agents. In response, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. Furthermore, the FDA recently also published draft guidance “Optimizing the dosage of human prescription drugs and biological products for the treatment of oncologic diseases guidance for industry,” providing overviews on the rationale, importance, and principles of dose optimization from the regulatory perspective. However, detailed guidance on how to design dose optimization trials is still lacking; it is a key issue faced by drug developers. The article discusses statistical and design strategies of dose optimization in oncology by sharing current industry practice on dose-finding, delineating key statistical components for dose optimization trials, describing available design strategies, and providing recommendations and future directions. This article is a joint work of the Statistical Methods in Oncology Scientific Working Group (Dose-Optimization Workstream) of the American Statistical Association Biopharmaceutical Section.

Sequential Markov chain Monte Carlo for Lagrangian data assimilation with applications to unknown data locations

AbstractWe consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and provably convergent computational methodology from Berzuini et al. (1997, Journal of the American Statistical Association, 92, 1403–1412); Centanniand Minozzo (2006, Journal of the American Statistical Association, 101, 1582–1597); Martin et al. (2013, Annals of the Institute of Statistical Mathematics, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about – dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals , , respectively.

Defining “Ethical Mathematical Practice” Through Engagement with Discipline-Adjacent Practice Standards and the Mathematical Community

This project explored what constitutes “ethical practice of mathematics”. Thematic analysis of ethical practice standards from mathematics-adjacent disciplines (statistics and computing), were combined with two organizational codes of conduct and community input resulting in over 100 items. These analyses identified 29 of the 52 items in the 2018 American Statistical Association Ethical Guidelines for Statistical Practice, and 15 of the 24 additional (unique) items from the 2018 Association of Computing Machinery Code of Ethics for inclusion. Three of the 29 items synthesized from the 2019 American Mathematical Society Code of Ethics, and zero of the Mathematical Association of America Code of Ethics, were identified as reflective of “ethical mathematical practice” beyond items already identified from the other two codes. The community contributed six unique items. Item stems were standardized to, “The ethical mathematics practitioner…”. Invitations to complete the 30-min online survey were shared nationally (US) via Mathematics organization listservs and other widespread emails and announcements. We received 142 individual responses to the national survey, 75% of whom endorsed 41/52 items, with 90–100% endorsing 20/52 items on the survey. Items from different sources were endorsed at both high and low rates. A final thematic analysis yielded 44 items, grouped into “General” (12 items), “Profession” (10 items) and “Scholarship” (11 items). Moreover, for the practitioner in a leader/mentor/supervisor/instructor role, there are an additional 11 items (4 General/7 Professional). These results suggest that the community perceives a much wider range of behaviors by mathematicians to be subject to ethical practice standards than had been previously included in professional organization codes. The results provide evidence against the argument that mathematics practitioners engaged in “pure” or “theoretical” work have minimal, small, or no ethical obligations.

How to Use and Report on p-values.

The use of the p-value in quantitative research, particularly its threshold of "P < 0.05" for determining "statistical significance," has long been a cornerstone of statistical analysis in research. However, this standard has been increasingly scrutinized for its potential to mislead findings, especially when the practical significance, the number of comparisons, or the suitability of statistical tests are not properly considered. In response to controversy around use of p-values, the American Statistical Association published a statement in 2016 that challenged the research community to abandon the term "statistically significant". This stance has been echoed by leading scientific journals to urge a significant reduction or complete elimination in the reliance on p-values when reporting results. To provide guidance to researchers in health professions education, this paper provides a succinct overview of the ongoing debate regarding the use of p-values and the definition of p-values. It reflects on the controversy by highlighting the common pitfalls associated with p-value interpretation and usage, such as misinterpretation, overemphasis, and false dichotomization between "significant" and "non-significant" results. This paper also outlines specific recommendations for the effective use of p-values in statistical reporting including the importance of reporting effect sizes, confidence intervals, the null hypothesis, and conducting sensitivity analyses for appropriate interpretation. These considerations aim to guide researchers toward a more nuanced and informative use of p-values.

CO2 emissions–economic growth nexus: Validity of EKC in oil‐exporting and oil‐importing countries

AbstractThe purpose of this study is to examine and compare the validity of the environmental Kuznets curve and the relationship between carbon dioxide emissions, per capita GDP, fossil fuel consumption, oil prices and foreign direct investment in advanced oil‐importing and oil‐exporting countries from 1970 to 2020. The researchers consider these nations for their dependency on oil resources and their different economic characteristics. The Westerlund (Oxford Bulletin of Economics and Statistics, 2007, 69, 709) co‐integration test shows that the studied variables are co‐integrated in the long run in both panels of the countries. The pooled mean group‐autoregressive distributed lag (PMG‐ARDL) model established by Pesaran et al. (Journal of the American Statistical Association, 1999, 94, 621), which assesses the short‐ and long‐run nexus between the variables of interest, detects statistically significant associations, providing evidence to support the hypothesis of EKC in both groups. Furthermore, foreign direct investment and fossil fuel consumption have long‐term positive effects on CO2 emissions. The main difference between both groups of countries is that oil price has a positive effect on CO2 emissions in oil‐exporting countries, while it has a negative effect on environmental degradation in oil‐importing countries. The study suggests increasing investment in renewable energy infrastructure by encouraging research and development, providing subsidies and tax incentives for renewable energy companies and promoting large‐scale renewable energy projects as they contribute to environmental quality.

Importance of minimal clinically important difference in medical research and guideline development

P-values have posed various challenges in conducting and interpreting medical research. In an endeavor to establish more objective criteria for assessing outcomes in medical care, statistical methods have been utilized to analyze clinical trial results, often leading to a perceived dichotomy: trial outcomes are categorized as either positive or negative based on a p-value. Unfortunately, clinicians began to overly rely on the statistical significance of studies, misinterpreting their findings as clinically meaningful. Recognizing the detrimental effects of p-values, the American Statistical Association advised against their use in scientific publications (1). Instead, emphasis should be placed on the magnitude of difference between intervention and control groups. Prior to conducting a study, and in assessing the results of a body of evidence it is essential to estimate the minimum size of the difference that would be clinically significant. The smallest magnitude of benefit that patients would deem as clinically important is the minimal clinically important difference (MCID) (2). The MCID encapsulates a patient-centered approach, encompassing both the degree of improvement and the value patients attribute to this change.

A Conversation with Stephen M. Stigler

Stephen M. Stigler received his Ph.D. in Statistics from the University of California, Berkeley, with a dissertation on the asymptotic distribution of linear functions of order statistics. Starting in 1967, he taught at the University of Wisconsin, Madison, then in 1979 moved to the University of Chicago where he taught from 1979 to 2021. Stigler has worked on a variety of topics in mathematical statistics, ranging from asymptotic theory to the theory of experimental design, and on applications of statistics including in anthropology, forensic science, paleontology, psychology, information transfer and sports. In recent years, he has concentrated on the history of statistics, with inquiries ranging from the development of statistical methods in astronomy and geodesy and their spread to biological and social sciences, to lotteries, to the modern development of statistical theory. He has published four books, The History of Statistics (1986), Statistics on the Table (1999), The Seven Pillars of Statistical Wisdom (2016) and Casanova’s Lottery (2022). A recent research focus has been upon the way the work of Francis Galton on the statistics of inheritance led to the creation of modern multivariate analysis and made a true Bayesian inference possible, and on how R. A. Fisher’s transformation of Karl Pearson’s path breaking research led to a modern period of statistical enlightenment. Stigler is an elected member of the American Academy of Arts and Sciences and of the American Philosophical Society; he has served as President of the Institute of Mathematical Statistics and of the International Statistical Institute. In 2005, he received the Humboldt Foundation Research Award; in 2010, he was elected Membre Associé of the Académie royale de Belgique, Classe des Sciences. Stigler served as Theory and Methods Editor for the Journal of the American Statistical Association 1979–1982. He was a Guggenheim Fellow in 1977, and received awards for undergraduate teaching at the University of Wisconsin (1971) and University of Chicago (1998). This interview with Stigler was conducted remotely in July 2021.

Data Science in Science: An Open-Access Journal

This interview delves into the world of open-access science journals through an insightful conversation with David Matteson, the editor of Data Science in Science (DSiS) and Kathy Ensor, 2022 president of the American Statistical Association (ASA). They share their vision for DSiS, a journal recently added to the ASA’s publications portfolio. Open access journals are transforming scientific publishing by fostering global collaboration among scientists. In this discussion, Matteson and Ensor shares their perspectives on the importance of the DSiS and how it supports the broader mission of the ASA.

Interview with Peter Hotez, Influencing Discovery Exploration & Action Forum Plenary Presenter

As part of the 2023 IDEA Forum, Kathy Ensor, 2022 president of the American Statistical Association (ASA) conducts an enlightening conversation with Dr. Peter Hotez, an eminent voice in the scientific and medical community. Dr. Hotez, a distinguished tropical disease and vaccine researcher and advocate, shares his profound insights on the paramount importance of open science in today's scientific landscape. In the discussion Dr. Hotez discusses the benefits of open science, encompassing its role in accelerating scientific progress, addressing critical societal challenges, and confronting anti-science rhetoric.

The Journal of the American Statistical Association 2023 Associate Editors

The Journal of the American Statistical Association 2023 Associate Editors

Exploring the Ethical Landscape of AI

A lot of attention has been focused on the promising innovation, efficiency, and transformative potential of artificial intelligence. In this issue’s One Thing About column, members of the American Statistical Association’s Committee on Professional Ethics introduce a critical conversation about the ethical considerations within the realm of AI and explore the multifaceted ethical landscape that accompanies its evolution. They want us to know that, beyond the lines of code and machine learning algorithms, lie complex issues of bias, accountability, and privacy and the need to have an ethical framework for the AI era.

Worthy winner: Why Rao deserved the “Nobel Prize for statistics”

Abstract Ron Wasserstein, executive director of the American Statistical Association and director of the International Prize Foundation, explains why C. R. Rao was awarded the 2023 International Prize in Statistics

Data viz whiz kids

Abstract Taylor Swift albums, basketball stars and homework all inspired high-schoolers to show off their data visualisation skills for a colourful American Statistical Association contest this summer

Statistically significant differences versus convincing evidence of real treatment effects: an analysis of the false positive risk for single-centre trials in anaesthesia

BackgroundThe American Statistical Association has highlighted problems with null hypothesis significance testing and outlined alternative approaches that may ‘supplement or even replace P-values’. One alternative is to report the false positive risk (FPR), which quantifies the chance the null hypothesis is true when the result is statistically significant. MethodsWe reviewed single-centre, randomised trials in 10 anaesthesia journals over 6 yr where differences in a primary binary outcome were statistically significant. We calculated a Bayes factor by two methods (Gunel, Kass). From the Bayes factor we calculated the FPR for different prior beliefs for a real treatment effect. Prior beliefs were quantified by assigning pretest probabilities to the null and alternative hypotheses. ResultsFor equal pretest probabilities of 0.5, the median (inter-quartile range [IQR]) FPR was 6% (1–22%) by the Gunel method and 6% (1–19%) by the Kass method. One in five trials had an FPR ≥20%. For trials reporting P-values 0.01–0.05, the median (IQR) FPR was 25% (16–30%) by the Gunel method and 20% (16–25%) by the Kass method. More than 90% of trials reporting P-values 0.01–0.05 required a pretest probability >0.5 to achieve an FPR of 5%. The median (IQR) difference in the FPR calculated by the two methods was 0% (0–2%). ConclusionsOur findings suggest that a substantial proportion of single-centre trials in anaesthesia reporting statistically significant differences provide limited evidence of real treatment effects, or, alternatively, required an implausibly high prior belief in a real treatment effect. Clinical trial registrationPROSPERO (CRD42023350783).

Estimands in Real-World Evidence Studies

A Real-World Evidence (RWE) Scientific Working Group (SWG) of the American Statistical Association Biopharmaceutical Section (ASA BIOP) has been reviewing statistical considerations for the generation of RWE to support regulatory decision-making. As part of the effort, the working group is addressing estimands in RWE studies. Constructing the right estimand—the target of estimation—which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes—population, treatment, endpoints, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (RWD), that is, RWE studies, requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. This article reviews the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for RWE studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies.

CE Workshop 09: Cast Aside Traditional Notions of Statistical Significance, and Focus Instead on Characterizing the Magnitude of Effects that are Clinically or Scientifically Relevant

Abstract &amp; Learning ObjectivesThere is an ongoing debate among statisticians and discipline scientists about the consequences of our persistent, dogmatic reliance on evaluating all statistical results as meaningful if and only if "p&lt;0.05," regardless of context. This was never the intended goal of Ronald Fisher, nevertheless scientists have adopted it as a convenience, and the decades long dependence on "p&lt;0.05" has had important negative consequences. In this presentation, I review common misconceptions about interpreting p-values, why we should consider de-emphasizing p-values, and why scientists should rely more on practical, clinical, or scientifically meaningful differences over arbitrary cut-offs. I will present several different metrics for evaluating and reporting effect magnitude, and whether or not data support the null vs. alternative hypothesis, under the frequentist paradigm, how Bayesian methods can augment or replace frequentist analyses, and a few options that help to clarify how important a finding may be. Throughout this talk, I advocate that discipline scientists take charge of sharing scientific results that are not based merely on arbitrary p-value cutoffs and other default logic, but instead based on their content expertise, in light of all of the specific relevant aspects of experimental design and experimental data, balancing the consequences of Type I vs Type II errors appropriately, and focusing on characterizing effects, rather than dichotomizing research into only two categories of importance (significant vs. not).Upon conclusion of this course, learners will be able to: 1.Discuss what p-values mean and how they are commonly misinterpreted.2.Explain the leading arguments promoted by the American Statistical Association with regard to why science should carefully reconsider if and how p-values should continue to dominate our decisions about what research should be published, and how scientists should be evaluating its worth.3.Apply new practices in how to evaluate and publish their own research, as well as how to evaluate research appearing in peer-reviewed journals, whether as consumers, reviewers, or editors.

Interview With Adrian Raftery

SummaryProfessor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a BA in Mathematics (1976) and an MSc in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France, under the supervision of Paul Deheuvels. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then an associate (1986–1990) and full (1990‐present) professor of statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999–2009). Professor Raftery has published over 200 articles in peer‐reviewed statistical, sociological and other journals. His research focuses on Bayesian model selection and Bayesian model averaging, model‐based clustering, inference for deterministic simulation models, and the development of new statistical methods for demography, sociology, and the environmental and health sciences. He is a member of the United States National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, an Honorary Member of the Royal Irish Academy, a member of the Washington State Academy of Sciences, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected Member of the Sociological Research Association. He has won the Population Association of America's Clifford C. Clogg Award, the American Sociological Association's Paul F. Lazarsfeld Award for Distinguished Contribution to Knowledge, the Jerome Sacks Award for Outstanding Cross‐Disciplinary Research from the National Institute of Statistical Sciences, the Parzen Prize for Statistical Innovation, and the Science Foundation Ireland St. Patrick's Day Medal. He is also a former Coordinating and Applications Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He was identified as the world's most cited researcher in mathematics for the decade 1995–2005 by Thomson‐ISI. Thirty‐three students have obtained PhD's working under Raftery's supervision, of whom 21 hold or have held tenure‐track university faculty positions. He has over 150 academic descendants. This interview took place over two sessions in March 2023.

Tumor-Agnostic Approvals: Insights and Practical Considerations.

Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.

A Conversation with Ray Waller

Ray A. Waller had a distinguished career in statistical education, government statistics, and statistical leadership. He and his wife Carolyn established the American Statistical Association’s Waller Education Award in 2002 and the Waller Distinguished Teaching Career Award in 2014, recognizing excellence in teaching introductory statistics. He is the recipient of the 2004 American Statistical Association’s Founders Award. This interview with his son Lance was recorded on September 13, 2019. Ray Waller passed away peacefully at his home on December 28, 2022.

The 2019 data challenge expo of the American Statistical Association

The 2019 data challenge expo of the American Statistical Association