| Type | Journal Article |
|---|---|
| Author | Stephen J. Ruberg |
| Author | Francois Beckers |
| Author | Rob Hemmings |
| Author | Peter Honig |
| Author | Telba Irony |
| Author | Lisa LaVange |
| Author | Grazyna Lieberman |
| Author | James Mayne |
| Author | Richard Moscicki |
| URL | https://www.nature.com/articles/s41573-023-00638-0 |
| Rights | 2023 Springer Nature Limited |
| Pages | 1-16 |
| Publication | Nature Reviews Drug Discovery |
| ISSN | 1474-1784 |
| Date | 2023-02-15 |
| Extra | Publisher: Nature Publishing Group |
| Journal Abbr | Nat Rev Drug Discov |
| DOI | 10.1038/s41573-023-00638-0 |
| Accessed | 2/16/2023, 2:20:27 PM |
| Library Catalog | www.nature.com |
| Language | en |
| Abstract | The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so. |
| Short Title | Application of Bayesian approaches in drug development |
| Date Added | 2/16/2023, 2:20:27 PM |
| Modified | 2/16/2023, 2:21:02 PM |
| Type | Journal Article |
|---|---|
| Author | Michael O. Harhay |
| Author | Bryan S. Blette |
| Author | Anders Granholm |
| Author | Frank W. Moler |
| Author | Fernando G. Zampieri |
| Author | Ewan C. Goligher |
| Author | Monique M. Gardner |
| Author | Alexis A. Topjian |
| Author | Nadir Yehya |
| URL | https://evidence.nejm.org/doi/10.1056/EVIDoa2200196 |
| Volume | 2 |
| Issue | 1 |
| Pages | EVIDoa2200196 |
| Publication | NEJM Evidence |
| Date | 2022-12-27 |
| Extra | Publisher: Massachusetts Medical Society |
| DOI | 10.1056/EVIDoa2200196 |
| Accessed | 12/30/2022, 3:21:36 AM |
| Library Catalog | evidence.nejm.org (Atypon) |
| Abstract | BACKGROUND Pediatric out-of-hospital cardiac arrest results in high morbidity and mortality. Currently, there are no recommended therapies beyond supportive care. The THAPCA-OH (Therapeutic Hypothermia after Pediatric Cardiac Arrest Out-of-Hospital) trial compared hypothermia (33.0°C) with normothermia (36.8°C) in 295 children. Good neurobehavioral outcome and survival at 1 year were higher in the hypothermia group (20 vs. 12% and 38 vs. 29%, respectively). These differences did not meet the planned statistical threshold of P<0.05. To ensure that a potentially efficacious therapy is not prematurely discarded, we reassessed THAPCA-OH using a Bayesian statistical perspective. METHODS We performed a Bayesian analysis, interpreting the trial in probabilistic terms (i.e., the probability that therapeutic hypothermia had any benefit, and overall absolute improvements greater than 2%, 5%, and 10% for 1-year neurobehavioral outcome and survival). Our primary analyses used noninformative priors, meaning that the analyses were based on the observed trial data without any information added by the priors. In the absence of pediatric trials to derive informative prior distributions, we used: (1) downweighted priors from adult trials; and (2) a previously published set of critical care priors that span benefit, equipoise, and harm. RESULTS In the primary analyses, the probability of any benefit from hypothermia was 94% for both the neurobehavioral outcome and survival at 1 year. For both outcomes, the probability of benefit was >75% for all informative prior integrations with the THAPCA-OH results, except those with the most pessimistic priors. CONCLUSIONS There is a high probability that hypothermia provides a modest benefit in neurobehavioral outcome and survival at 1 year. (ClinicalTrials.gov number, NCT00878644.) |
| Date Added | 12/30/2022, 3:21:36 AM |
| Modified | 12/30/2022, 3:22:54 AM |
| Type | Journal Article |
|---|---|
| Author | Andrew D. Althouse |
| Author | Jennifer E. Below |
| Author | Brian L. Claggett |
| Author | Nancy J. Cox |
| Author | James A. de Lemos |
| Author | Rahul C. Deo |
| Author | Sue Duval |
| Author | Rory Hachamovitch |
| Author | Sanjay Kaul |
| Author | Scott W. Keith |
| Author | Eric Secemsky |
| Author | Armando Teixeira-Pinto |
| Author | Veronique L. Roger |
| Author | null null |
| URL | https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.055393 |
| Volume | 144 |
| Issue | 4 |
| Pages | e70-e91 |
| Publication | Circulation |
| Date | July 27, 2021 |
| DOI | 10.1161/CIRCULATIONAHA.121.055393 |
| Accessed | 1/11/2022, 7:37:12 AM |
| Library Catalog | ahajournals.org (Atypon) |
| Abstract | Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results. |
| Short Title | Recommendations for Statistical Reporting in Cardiovascular Medicine |
| Date Added | 1/11/2022, 7:37:40 AM |
| Modified | 1/11/2022, 7:37:40 AM |
| Type | Journal Article |
|---|---|
| Author | Jennifer Clark |
| Author | Natalia Muhlemann |
| Author | Fanni Natanegara |
| Author | Andrew Hartley |
| Author | Deborah Wenkert |
| Author | Fei Wang |
| Author | Frank E. Harrell |
| Author | Ross Bray |
| Author | The Medical Outreach Subteam of the Drug Information Association Bayesian Scientific Working Group |
| URL | https://doi.org/10.1007/s43441-021-00357-x |
| Publication | Therapeutic Innovation & Regulatory Science |
| ISSN | 2168-4804 |
| Date | 2022-01-03 |
| Journal Abbr | Ther Innov Regul Sci |
| DOI | 10.1007/s43441-021-00357-x |
| Accessed | 1/8/2022, 7:51:22 AM |
| Library Catalog | Springer Link |
| Language | en |
| Abstract | The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them. |
| Short Title | Why are not There More Bayesian Clinical Trials? |
| Date Added | 1/8/2022, 7:51:55 AM |
| Modified | 1/8/2022, 7:51:55 AM |
| Type | Journal Article |
|---|---|
| Author | Fulvio De Santis |
| Author | Stefania Gubbiotti |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202100035 |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100035 |
| DOI | 10.1002/bimj.202100035 |
| Accessed | 12/11/2021, 3:32:01 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | In Bayesian inference, prior distributions formalize preexperimental information and uncertainty on model parameters. Sometimes different sources of knowledge are available, possibly leading to divergent posterior distributions and inferences. Research has been recently devoted to the development of sample size criteria that guarantee agreement of posterior information in terms of credible intervals when multiple priors are available. In these articles, the goals of reaching consensus and evidence are typically kept separated. Adopting a Bayesian performance-based approach, the present article proposes new sample size criteria for superiority trials that jointly control the achievement of both minimal evidence and consensus, measured by appropriate functions of the posterior distributions. We develop both an average criterion and a more stringent criterion that accounts for the entire predictive distributions of the selected measures of minimal evidence and consensus. Methods are developed and illustrated via simulation for trials involving binary outcomes. A real clinical trial example on Covid-19 vaccine data is presented. |
| Date Added | 12/11/2021, 3:32:01 PM |
| Modified | 12/12/2021, 2:42:29 PM |
| Type | Journal Article |
|---|---|
| Author | Mikael Fink Vallentin |
| Author | Asger Granfeldt |
| Author | Carsten Meilandt |
| Author | Amalie Ling Povlsen |
| Author | Birthe Sindberg |
| Author | Mathias J. Holmberg |
| Author | Bo Nees Iversen |
| Author | Rikke Mærkedahl |
| Author | Lone Riis Mortensen |
| Author | Rasmus Nyboe |
| Author | Mads Partridge Vandborg |
| Author | Maren Tarpgaard |
| Author | Charlotte Runge |
| Author | Christian Fynbo Christiansen |
| Author | Thomas H. Dissing |
| Author | Christian Juhl Terkelsen |
| Author | Steffen Christensen |
| Author | Hans Kirkegaard |
| Author | Lars W. Andersen |
| URL | https://doi.org/10.1001/jama.2021.20929 |
| Publication | JAMA |
| ISSN | 0098-7484 |
| Date | November 30, 2021 |
| Journal Abbr | JAMA |
| DOI | 10.1001/jama.2021.20929 |
| Accessed | 12/2/2021, 11:20:41 AM |
| Library Catalog | Silverchair |
| Abstract | It is unclear whether administration of calcium has a beneficial effect in patients with cardiac arrest.To determine whether administration of calcium during out-of-hospital cardiac arrest improves return of spontaneous circulation in adults.This double-blind, placebo-controlled randomized clinical trial included 397 adult patients with out-of-hospital cardiac arrest and was conducted in the Central Denmark Region between January 20, 2020, and April 15, 2021. The last 90-day follow-up was on July 15, 2021.The intervention consisted of up to 2 intravenous or intraosseous doses with 5 mmol of calcium chloride (n = 197) or saline (n = 200). The first dose was administered immediately after the first dose of epinephrine.The primary outcome was sustained return of spontaneous circulation. The secondary outcomes included survival and a favorable neurological outcome (modified Rankin Scale score of 0-3) at 30 days and 90 days.Based on a planned interim analysis of 383 patients, the steering committee stopped the trial early due to concerns about harm in the calcium group. Of 397 adult patients randomized, 391 were included in the analyses (193 in the calcium group and 198 in the saline group; mean age, 68 [SD, 14] years; 114 [29%] were female). There was no loss to follow-up. There were 37 patients (19%) in the calcium group who had sustained return of spontaneous circulation compared with 53 patients (27%) in the saline group (risk ratio, 0.72 [95% CI, 0.49 to 1.03]; risk difference, −7.6% [95% CI, −16% to 0.8%]; P = .09). At 30 days, 10 patients (5.2%) in the calcium group and 18 patients (9.1%) in the saline group were alive (risk ratio, 0.57 [95% CI, 0.27 to 1.18]; risk difference, −3.9% [95% CI, −9.4% to 1.3%]; P = .17). A favorable neurological outcome at 30 days was observed in 7 patients (3.6%) in the calcium group and in 15 patients (7.6%) in the saline group (risk ratio, 0.48 [95% CI, 0.20 to 1.12]; risk difference, −4.0% [95% CI, −8.9% to 0.7%]; P = .12). Among the patients with calcium values measured who had return of spontaneous circulation, 26 (74%) in the calcium group and 1 (2%) in the saline group had hypercalcemia.Among adults with out-of-hospital cardiac arrest, treatment with intravenous or intraosseous calcium compared with saline did not significantly improve sustained return of spontaneous circulation. These results do not support the administration of calcium during out-of-hospital cardiac arrest in adults.ClinicalTrials.gov Identifier: NCT04153435 |
| Short Title | Effect of Intravenous or Intraosseous Calcium vs Saline on Return of Spontaneous Circulation in Adults With Out-of-Hospital Cardiac Arrest |
| Date Added | 12/2/2021, 11:20:41 AM |
| Modified | 12/2/2021, 11:21:29 AM |
Followed recently published Bayesian re-analysis reporting guideliness of Michael Harhay et al
| Type | Journal Article |
|---|---|
| Author | Thomas E. Bradstreet |
| URL | https://doi.org/10.1080/19466315.2020.1799856 |
| Volume | 13 |
| Issue | 3 |
| Pages | 344-354 |
| Publication | Statistics in Biopharmaceutical Research |
| ISSN | null |
| Date | July 3, 2021 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/19466315.2020.1799856 |
| DOI | 10.1080/19466315.2020.1799856 |
| Accessed | 11/12/2021, 1:45:29 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Abstract | An initial proposal was made to start 30 monkeys in the run-in period of a preclinical translational research study, to have 24 or more animals qualify for randomization in the subsequent treatment period. Based upon data from previous studies, Bayesian posterior prediction indicated that successful enrolment was highly unlikely. At least 67 animals were required to achieve an acceptable posterior predictive probability of success. Importantly, we leveraged these feasibility analyses to introduce our preclinical scientist collaborators to a Bayesian strategy for probability-based decision making. We provided them with a generous helping of graphics to effectively and efficiently illustrate Bayesian concepts and methods. We present our 4P strategy for collaboration with preclinical scientists: patience, persistence, positioning, and privilege. We discuss the alignment of the Bayesian and 4P strategies with goals common to pharmaceutical researchers: scientific innovation; stochastic intelligence and statistical literacy of team members; team collaboration and collegial partnerships; ethical acuity; and fiscal stewardship. Our article is as much about successfully reaching out to preclinical scientists, and introducing them to the Bayesian strategy, as it is about that strategy successfully addressing the animal enrolment question. This article is written at a statistical level accessible to both preclinical scientists and statisticians. |
| Date Added | 11/12/2021, 1:45:29 PM |
| Modified | 11/12/2021, 1:45:50 PM |
| Type | Journal Article |
|---|---|
| Author | Anders Granholm |
| Author | Marie Warrer Munch |
| Author | Sheila Nainan Myatra |
| Author | Bharath Kumar Tirupakuzhi Vijayaraghavan |
| Author | Maria Cronhjort |
| Author | Rebecka Rubenson Wahlin |
| Author | Stephan M. Jakob |
| Author | Luca Cioccari |
| Author | Maj-Brit Nørregaard Kjær |
| Author | Gitte Kingo Vesterlund |
| Author | Tine Sylvest Meyhoff |
| Author | Marie Helleberg |
| Author | Morten Hylander Møller |
| Author | Thomas Benfield |
| Author | Balasubramanian Venkatesh |
| Author | Naomi E. Hammond |
| Author | Sharon Micallef |
| Author | Abhinav Bassi |
| Author | Oommen John |
| Author | Vivekanand Jha |
| Author | Klaus Tjelle Kristiansen |
| Author | Charlotte Suppli Ulrik |
| Author | Vibeke Lind Jørgensen |
| Author | Margit Smitt |
| Author | Morten H. Bestle |
| Author | Anne Sofie Andreasen |
| Author | Lone Musaeus Poulsen |
| Author | Bodil Steen Rasmussen |
| Author | Anne Craveiro Brøchner |
| Author | Thomas Strøm |
| Author | Anders Møller |
| Author | Mohd Saif Khan |
| Author | Ajay Padmanaban |
| Author | Jigeeshu Vasishtha Divatia |
| Author | Sanjith Saseedharan |
| Author | Kapil Borawake |
| Author | Farhad Kapadia |
| Author | Subhal Dixit |
| Author | Rajesh Chawla |
| Author | Urvi Shukla |
| Author | Pravin Amin |
| Author | Michelle S. Chew |
| Author | Christian Aage Wamberg |
| Author | Christian Gluud |
| Author | Theis Lange |
| Author | Anders Perner |
| URL | https://doi.org/10.1007/s00134-021-06573-1 |
| Publication | Intensive Care Medicine |
| ISSN | 1432-1238 |
| Date | 2021-11-10 |
| Journal Abbr | Intensive Care Med |
| DOI | 10.1007/s00134-021-06573-1 |
| Accessed | 11/12/2021, 11:07:59 AM |
| Library Catalog | Springer Link |
| Language | en |
| Abstract | We compared dexamethasone 12 versus 6 mg daily for up to 10 days in patients with coronavirus disease 2019 (COVID-19) and severe hypoxaemia in the international, randomised, blinded COVID STEROID 2 trial. In the primary, conventional analyses, the predefined statistical significance thresholds were not reached. We conducted a pre-planned Bayesian analysis to facilitate probabilistic interpretation. |
| Short Title | Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia |
| Date Added | 11/12/2021, 11:07:59 AM |
| Modified | 11/12/2021, 11:09:06 AM |
| Type | Journal Article |
|---|---|
| Author | ATTACC Investigators |
| URL | https://doi.org/10.1056/NEJMoa2105911 |
| Volume | 0 |
| Issue | 0 |
| Pages | null |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | August 4, 2021 |
| Extra | Publisher: Massachusetts Medical Society _eprint: https://doi.org/10.1056/NEJMoa2105911 |
| DOI | 10.1056/NEJMoa2105911 |
| Accessed | 8/7/2021, 2:31:29 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Date Added | 8/7/2021, 2:31:29 PM |
| Modified | 8/7/2021, 2:33:06 PM |
| Type | Journal Article |
|---|---|
| Author | Fernando G. Zampieri |
| Author | Jonathan D. Casey |
| Author | Manu Shankar-Hari |
| Author | Frank E. Harrell |
| Author | Michael O. Harhay |
| URL | https://www.atsjournals.org/doi/10.1164/rccm.202006-2381CP |
| Volume | 203 |
| Issue | 5 |
| Pages | 543-552 |
| Publication | American Journal of Respiratory and Critical Care Medicine |
| ISSN | 1073-449X |
| Date | December 3, 2020 |
| Extra | Publisher: American Thoracic Society - AJRCCM |
| Journal Abbr | Am J Respir Crit Care Med |
| DOI | 10.1164/rccm.202006-2381CP |
| Accessed | 3/1/2021, 2:54:29 PM |
| Library Catalog | atsjournals.org (Atypon) |
| Abstract | Most randomized trials are designed and analyzed using frequentist statistical approaches such as null hypothesis testing and P values. Conceptually, P values are cumbersome to understand, as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called “priors”). These priors are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis. |
| Date Added | 3/1/2021, 2:54:29 PM |
| Modified | 3/1/2021, 2:55:01 PM |
| Type | Journal Article |
|---|---|
| Author | REMAP-CAP Investigators |
| URL | https://dx.doi.org/10.1056/nejmoa2100433 |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | 2021 |
| Extra | Publisher: Massachusetts Medical Society |
| Journal Abbr | New England Journal of Medicine |
| DOI | 10.1056/nejmoa2100433 |
| Date Added | 2/27/2021, 7:28:40 AM |
| Modified | 2/27/2021, 7:30:16 AM |
| Type | Journal Article |
|---|---|
| Author | George Casella |
| Author | Edward I. George |
| Volume | 46 |
| Pages | 167-174 |
| Publication | Am Statistician |
| Date | 1992 |
| Extra | Citation Key: cas92exp tex.citeulike-article-id= 13263865 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 1/19/2021, 8:04:49 AM |
| Type | Book |
|---|---|
| Author | Peter F. Thall |
| URL | https://www.springer.com/gp/book/9783030437138 |
| Series | Springer Series in Pharmaceutical Statistics |
| Publisher | Springer International Publishing |
| ISBN | 978-3-030-43713-8 |
| Date | 2020 |
| Extra | DOI: 10.1007/978-3-030-43714-5 |
| Accessed | 1/9/2021, 7:48:50 AM |
| Library Catalog | www.springer.com |
| Language | en |
| Abstract | This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community. |
| Date Added | 1/9/2021, 7:48:50 AM |
| Modified | 1/9/2021, 7:50:00 AM |
| Type | Journal Article |
|---|---|
| Author | David Rindskopf |
| URL | https://doi.org/10.1177/0193841X20977619 |
| Pages | 0193841X20977619 |
| Publication | Evaluation Review |
| ISSN | 0193-841X |
| Date | December 30, 2020 |
| Extra | Publisher: SAGE Publications Inc |
| Journal Abbr | Eval Rev |
| DOI | 10.1177/0193841X20977619 |
| Accessed | 1/5/2021, 9:15:07 AM |
| Library Catalog | SAGE Journals |
| Language | en |
| Abstract | Because of the different philosophy of Bayesian statistics, where parameters are random variables and data are considered fixed, the analysis and presentation of results will differ from that of frequentist statistics. Most importantly, the probabilities that a parameter is in certain regions of the parameter space are crucial quantities in Bayesian statistics that are not calculable (or considered important) in the frequentist approach that is the basis of much of traditional statistics. In this article, I discuss the implications of these differences for presentation of the results of Bayesian analyses. In doing so, I present more detailed guidelines than are usually provided and explain the rationale for my suggestions. |
| Date Added | 1/5/2021, 9:15:07 AM |
| Modified | 1/5/2021, 9:15:56 AM |
| Type | Journal Article |
|---|---|
| Author | Tony O'Hagan |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1740-9713.2004.00050.x |
| Rights | © 2004 The Royal Statistical Society |
| Volume | 1 |
| Issue | 3 |
| Pages | 132-133 |
| Publication | Significance |
| ISSN | 1740-9713 |
| Date | 2004 |
| Extra | _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2004.00050.x |
| DOI | https://doi.org/10.1111/j.1740-9713.2004.00050.x |
| Accessed | 11/22/2020, 3:49:11 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | There are many things that I am uncertain about, says Tony O'Hagan. Some are merely unknown to me, while others are unknowable. This article is about different kinds of uncertainty, and how the distinction between them impinges on the foundations of Probability and Statistics. |
| Date Added | 11/22/2020, 3:49:11 PM |
| Modified | 11/22/2020, 3:49:53 PM |
| Type | Journal Article |
|---|---|
| Author | Stephen J. Ruberg |
| URL | https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.2004 |
| Rights | This article is protected by copyright. All rights reserved. |
| Volume | n/a |
| Issue | n/a |
| Publication | Clinical Pharmacology & Therapeutics |
| ISSN | 1532-6535 |
| Date | 2020 |
| Extra | _eprint: https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/cpt.2004 |
| DOI | 10.1002/cpt.2004 |
| Accessed | 8/6/2020, 8:31:38 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Null hypothesis significance testing (NHST) with its benchmark p-value<0.05 has long been a stalwart of scientific reporting and such statistically significant findings have been used to imply scientifically or clinically significant findings. Challenges to this approach have arisen over the past six decades, but they have largely been unheeded. There is a growing movement for using Bayesian statistical inference to quantify the probability that a scientific finding is credible. There have been differences of opinion between the frequentist (i.e. NHST) and Bayesian schools of inference, and warnings about the use or misuse of p-values have come from both schools of thought spanning many decades. Controversies in this arena have been heightened by the American Statistical Association statement on p-values and the further denouncement of the term “statistical significance” by others. My experience has been that many scientists, including many statisticians, do not have a sound conceptual grasp of the fundamental differences in these approaches, thereby creating even greater confusion and acrimony. If we let A represent the observed data, and B represent the hypothesis of interest, then the fundamental distinction between these two approaches can be described as the frequentist approach using the conditional probability pr(A|B), i.e. the p-value, and the Bayesian approach using pr(B|A), the posterior probability. This article will further explain the fundamental differences in NHST and Bayesian approaches and demonstrate how they can co-exist harmoniously to guide clinical trial design and inference. |
| Short Title | Détente |
| Date Added | 8/6/2020, 8:31:39 AM |
| Modified | 8/6/2020, 8:32:23 AM |
| Type | Journal Article |
|---|---|
| Author | Marcus Bendtsen |
| URL | https://www.jmir.org/2018/10/e10873/ |
| Volume | 20 |
| Issue | 10 |
| Pages | e10873 |
| Publication | Journal of Medical Internet Research |
| Date | 2018 |
| Extra | Company: Journal of Medical Internet Research Distributor: Journal of Medical Internet Research Institution: Journal of Medical Internet Research Label: Journal of Medical Internet Research Publisher: JMIR Publications Inc., Toronto, Canada |
| DOI | 10.2196/10873 |
| Accessed | 3/16/2020, 3:09:03 PM |
| Library Catalog | www.jmir.org |
| Language | en |
| Abstract | The debate on the use and misuse of P values has risen and fallen throughout their almost century-long existence in scientific discovery. Over the past few years, the debate has again received front-page attention, particularly through the public reminder by the American Statistical Association on how P values should be used and interpreted. At the core of the issue lies a fault in the way that scientific evidence is dichotomized and research is subsequently reported, and this fault is exacerbated by researchers giving license to statistical models to do scientific inference. This paper highlights a different approach to handling the evidence collected during a randomized controlled trial, one that does not dichotomize, but rather reports the evidence collected. Through the use of a coin flipping experiment and reanalysis of real-world data, the traditional approach of testing null hypothesis significance is contrasted with a Bayesian approach. This paper is meant to be understood by those who rely on statistical models to draw conclusions from data, but are not statisticians and may therefore not be able to grasp the debate that is primarily led by statisticians. [J Med Internet Res 2018;20(10):e10873] |
| Short Title | A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis |
| Date Added | 3/16/2020, 3:09:03 PM |
| Modified | 3/16/2020, 3:09:43 PM |
Using priors forces us to be more specific and explicit about what we mean when we say that something is unknown... the Bayesian approach does not attempt to identify a fixed value for the parameters and dichotomize the world into significant and nonsignificant, but rather relies on the researcher to do the scientific inference and not to delegate this obligation to the statistical model... the NHST approach is rooted in the idea of being able to redo the experiment many times (so as to get a sampling distribution). Even if we can rely on theoretical results to get this sampling distribution without actually going back in time and redoing the experiment, the underlying idea can be somewhat problematic. What do we mean by redoing an experiment? Can we redo a randomized controlled trial while keeping all things equal and recruiting a new sample from the study population?... Once we remove ourselves from the dichotomization of evidence, other things start to take precedence: critically assessing the models chosen, evaluating the quality of the data, interpreting the real-world impact of the results, etc.
| Type | Journal Article |
|---|---|
| Author | John E. Brush |
| Author | Mark Lee |
| Author | Jonathan Sherbino |
| Author | Judith C. Taylor-Fishwick |
| Author | Geoffrey Norman |
| URL | https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2757877 |
| Volume | 2 |
| Issue | 12 |
| Pages | e1918023-e1918023 |
| Publication | JAMA Network Open |
| Date | 2019/12/02 |
| Journal Abbr | JAMA Netw Open |
| DOI | 10.1001/jamanetworkopen.2019.18023 |
| Accessed | 12/21/2019, 7:23:26 AM |
| Library Catalog | jamanetwork.com |
| Language | en |
| Abstract | <h3>Importance</h3><p>Clinicians use probability estimates to make a diagnosis. Teaching students to make more accurate probability estimates could improve the diagnostic process and, ultimately, the quality of medical care.</p><h3>Objective</h3><p>To test whether novice clinicians can be taught to make more accurate bayesian revisions of diagnostic probabilities using teaching methods that apply either explicit conceptual instruction or repeated examples.</p><h3>Design, Setting, and Participants</h3><p>A randomized clinical trial of 2 methods for teaching bayesian updating and diagnostic reasoning was performed. A web-based platform was used for consent, randomization, intervention, and testing of the effect of the intervention. Participants included 61 medical students at McMaster University and Eastern Virginia Medical School recruited from May 1 to September 30, 2018.</p><h3>Interventions</h3><p>Students were randomized to (1) receive explicit conceptual instruction regarding diagnostic testing and bayesian revision (concept group), (2) exposure to repeated examples of cases with feedback regarding posttest probability (experience group), or (3) a control condition with no conceptual instruction or repeated examples.</p><h3>Main Outcomes and Measures</h3><p>Students in all 3 groups were tested on their ability to update the probability of a diagnosis based on either negative or positive test results. Their probability revisions were compared with posttest probability revisions that were calculated using the Bayes rule and known test sensitivity and specificity.</p><h3>Results</h3><p>Of the 61 participants, 22 were assigned to the concept group, 20 to the experience group, and 19 to the control group. Approximate age was 25 years. Two participants were first-year; 37, second-year; 12, third-year; and 10, fourth-year students. Mean (SE) probability estimates of students in the concept group were statistically significantly closer to calculated bayesian probability than the other 2 groups (concept, 0.4%; [0.7%]; experience, 3.5% [0.7%]; control, 4.3% [0.7%];<i>P</i> < .001). Although statistically significant, the differences between groups were relatively modest, and students in all groups performed better than expected, based on prior reports in the literature.</p><h3>Conclusions and Relevance</h3><p>The study showed a modest advantage for students who received theoretical instruction on bayesian concepts. All participants’ probability estimates were, on average, close to the bayesian calculation. These findings have implications for how to teach diagnostic reasoning to novice clinicians.</p><h3>Trial Registration</h3><p>ClinicalTrials.gov identifier:NCT04130607</p> |
| Short Title | Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis |
| Date Added | 12/21/2019, 7:23:26 AM |
| Modified | 12/21/2019, 7:23:56 AM |
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| Volume | 5 |
| Pages | 27-36 |
| Publication | Nat Rev |
| Date | 2006 |
| Extra | Citation Key: ber06bay tex.citeulike-article-id= 13265478 tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0 Editorial, p. 3 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
excellent review of Bayesian approaches in clinical trials; "The greatest virtue of the traditional approach may be its extreme rigour and narrowness of focus to the experiment at hand, but a side effect of this virtue is inflexibility, which in turn limits innovation in the design and analysis of clinical trials. ... The set of `other possible results' depends on the experimental design. ... Everything that is known is taken as given and all probabilities are calculated conditionally on known values. ... in contrast to the frequentist approach, only the probabilities of the observed results matter. ... The continuous learning that is possible in the Bayesian approach enables investigators to modify trials in midcourse. ... it is possible to learn from small samples, depending on the results, ... it is possible to adapt to what is learned to enable better treatment of patients. ... subjectivity in prior distributions is explicit and open to examination (and critique) by all. ... The Bayesian approach has several advantages in drug development. One is the process of updating knowledge gradually rather than restricting revisions in study design to large, discrete steps measured in trials or phases."
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| Volume | 51 |
| Pages | 241-246 |
| Publication | Am Statistician |
| Date | 1997 |
| Extra | Citation Key: ber97tea tex.citeulike-article-id= 13263759 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | David Malakoff |
| URL | http://dx.doi.org/10.1126/science.286.5444.1460 |
| Volume | 286 |
| Pages | 1460-1464 |
| Publication | Science |
| Date | 1999 |
| Extra | Citation Key: mal99bay tex.citeulike-article-id= 13265096 tex.citeulike-linkout-0= http://dx.doi.org/10.1126/science.286.5444.1460 tex.posted-at= 2014-07-14 14:09:49 tex.priority= 0 |
| DOI | 10.1126/science.286.5444.1460 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book |
|---|---|
| Author | John K. Kruschke |
| URL | http://www.sciencedirect.com/science/book/9780124058880 |
| Edition | Second Edition |
| Place | Waltham MA |
| Publisher | Academic Press |
| ISBN | 978-0-12-405888-0 |
| Date | 2015 |
| Extra | Citation Key: kru15doi tex.citeulike-article-id= 14172337 tex.citeulike-linkout-0= http://www.sciencedirect.com/science/book/9780124058880 tex.posted-at= 2016-10-26 21:46:24 tex.priority= 4 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book |
|---|---|
| Author | Richard McElreath |
| URL | http://www.worldcat.org/isbn/9781482253443 |
| ISBN | 978-1-4822-5344-3 |
| Date | 2016 |
| Extra | Citation Key: mce16sta tex.citeulike-article-id= 14255283 tex.citeulike-linkout-0= http://www.worldcat.org/isbn/9781482253443 tex.citeulike-linkout-1= http://books.google.com/books?vid=ISBN9781482253443 tex.citeulike-linkout-2= http://www.amazon.com/gp/search?keywords=9781482253443&index=books&linkCode=qs tex.citeulike-linkout-3= http://www.librarything.com/isbn/9781482253443 tex.citeulike-linkout-4= http://www.worldcat.org/oclc/920672225 tex.posted-at= 2017-01-15 19:24:57 tex.priority= 4 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Eric-Jan Wagenmakers |
| Author | Maarten Marsman |
| Author | Tahira Jamil |
| Author | Alexander Ly |
| Author | Josine Verhagen |
| Author | Jonathon Love |
| Author | Ravi Selker |
| Author | Quentin F. Gronau |
| Author | Martin ̌Sḿıra |
| Author | Sacha Epskamp |
| Author | Dora Matzke |
| Author | Jeffrey N. Rouder |
| Author | Richard D. Morey |
| URL | http://dx.doi.org/10.3758/s13423-017-1343-3 |
| Pages | 1-23 |
| Date | 2017 |
| Extra | Citation Key: wag17bay1 tex.booktitle= Psychonomic Bulletin & Review tex.citeulike-article-id= 14438461 tex.citeulike-linkout-0= http://dx.doi.org/10.3758/s13423-017-1343-3 tex.citeulike-linkout-1= http://link.springer.com/article/10.3758/s13423-017-1343-3 tex.posted-at= 2017-09-26 18:41:53 tex.priority= 0 tex.publisher= Springer US |
| DOI | 10.3758/s13423-017-1343-3 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Dennis V. Lindley |
| URL | http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x |
| Volume | 15 |
| Issue | 1 |
| Pages | 22-25 |
| Publication | Teaching Statistics |
| Date | 1993-03 |
| Extra | Citation Key: lin93ana tex.citeulike-article-id= 10418027 tex.citeulike-attachment-1= lin93ana.pdf; /pdf/user/harrelfe/article/10418027/1121742/lin93ana.pdf; 243d4fbea879999e1f76b707d0e2502d5aca542f tex.citeulike-linkout-0= http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x tex.day= 1 tex.posted-at= 2017-10-31 12:04:19 tex.priority= 0 tex.publisher= Blackwell Publishing Ltd |
| DOI | 10.1111/j.1467-9639.1993.tb00252.x |
| Abstract | A classical experiment on the tasting of tea is used to show that many standard methods of analysis of the resulting data are unsatisfactory. A similar experiment with wine is used to show how a more sensible method may be developed. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | William M. Briggs |
| URL | http://dx.doi.org/10.1080/01621459.2017.1311264 |
| Volume | 112 |
| Issue | 519 |
| Pages | 897-898 |
| Publication | JASA |
| Date | 2017-07 |
| Extra | Citation Key: bri17sub tex.citeulike-article-id= 14479856 tex.citeulike-attachment-1= bri17sub.pdf; /pdf/user/harrelfe/article/14479856/1123078/bri17sub.pdf; e2946ca2518f20e15d607a0bccb9accb149c2c19 tex.citeulike-linkout-0= http://dx.doi.org/10.1080/01621459.2017.1311264 tex.citeulike-linkout-1= http://www.tandfonline.com/doi/abs/10.1080/01621459.2017.1311264 tex.day= 3 tex.posted-at= 2017-11-21 14:33:28 tex.priority= 0 tex.publisher= Taylor & Francis |
| DOI | 10.1080/01621459.2017.1311264 |
| Abstract | If it was not obvious before, after reading McShane and Gal, the conclusion is that p-values should be proscribed. There are no good uses for them; indeed, every use either violates frequentist theory, is fallacious, or is based on a misunderstanding. A replacement for p-values is suggested, based on predictive models. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | John K. Kruschke |
| URL | http://dx.doi.org/10.1037/a0029146 |
| Volume | 142 |
| Issue | 2 |
| Pages | 573-603 |
| Publication | J Exp Psych |
| ISSN | 1939-2222 |
| Date | 2013-05 |
| Extra | Citation Key: kru13bay tex.citeulike-article-id= 11639960 tex.citeulike-attachment-1= kru13bay.pdf; /pdf/user/harrelfe/article/11639960/1136836/kru13bay.pdf; dea60927efbd1f284b4132eae3461ea7ce0fb62a tex.citeulike-linkout-0= http://dx.doi.org/10.1037/a0029146 tex.citeulike-linkout-1= http://view.ncbi.nlm.nih.gov/pubmed/22774788 tex.citeulike-linkout-2= http://www.hubmed.org/display.cgi?uids=22774788 tex.day= 9 tex.pmid= 22774788 tex.posted-at= 2018-05-18 03:54:13 tex.priority= 4 |
| DOI | 10.1037/a0029146 |
| Abstract | Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms. PsycINFO Database Record (c) 2013 APA, all rights reserved. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | H. P. Lehmann |
| Author | M. R. Wachter |
| URL | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232964/ |
| Pages | 433-437 |
| Publication | Proceedings : a conference of the American Medical Informatics Association / ... AMIA Annual Fall Symposium. AMIA Fall Symposium |
| ISSN | 1091-8280 |
| Date | 1996 |
| Extra | Citation Key: leh96imp tex.citeulike-article-id= 13346740 tex.citeulike-attachment-1= leh96imp.pdf; /pdf/user/harrelfe/article/13346740/983544/leh96imp.pdf; b5a59f8e18230cb4ddc17759b426db8f88cb2e69 tex.citeulike-linkout-0= http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232964/ tex.citeulike-linkout-1= http://view.ncbi.nlm.nih.gov/pubmed/8947703 tex.citeulike-linkout-2= http://www.hubmed.org/display.cgi?uids=8947703 tex.pmcid= PMC2232964 tex.pmid= 8947703 tex.posted-at= 2014-09-04 12:57:17 tex.priority= 0 |
| Abstract | For decades, statisticians, philosophers, medical investigators and others interested in data analysis have argued that the Bayesian paradigm is the proper approach for reporting the results of scientific analyses for use by clients and readers. To date, the methods have been too complicated for non-statisticians to use. In this paper we argue that the World-Wide Web provides the perfect environment to put the Bayesian paradigm into practice: the likelihood function of the data is parsimoniously represented on the server side, the reader uses the client to represent her prior belief, and a downloaded program (a Java applet) performs the combination. In our approach, a different applet can be used for each likelihood function, prior belief can be assessed graphically, and calculation results can be reported in a variety of ways. We present a prototype implementation, BayesApplet, for two-arm clinical trials with normally-distributed outcomes, a prominent model for clinical trials. The primary implication of this work is that publishing medical research results on the Web can take a form beyond or different from that currently used on paper, and can have a profound impact on the publication and use of research results. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Pamela E. Scott |
| Author | Gregory Campbell |
| Volume | 32 |
| Pages | 213-220 |
| Publication | Drug Info J |
| Date | 1998 |
| Extra | Citation Key: sco98int tex.citeulike-article-id= 13264822 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lewis B. Sheiner |
| URL | http://dx.doi.org/10.1038/clpt.1991.97 |
| Volume | 50 |
| Pages | 4-9 |
| Publication | Clin Pharm Ther |
| Date | 1991 |
| Extra | Citation Key: she91int tex.citeulike-article-id= 13264842 tex.citeulike-linkout-0= http://dx.doi.org/10.1038/clpt.1991.97 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| DOI | 10.1038/clpt.1991.97 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
problems with traditional statistical approaches to drug evaluation;problems with under-emphasis of type II error
| Type | Journal Article |
|---|---|
| Author | A. F. M. Smith |
| Author | A. E. Gelfand |
| Volume | 46 |
| Pages | 84-88 |
| Publication | Am Statistician |
| Date | 1992 |
| Extra | Citation Key: smi92bay tex.citeulike-article-id= 13264874 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Harold P. Lehmann |
| Author | Bach Nguyen |
| URL | http://www.ncbi.nlm.nih.gov/pubmed/9308343 |
| Volume | 14 |
| Issue | 5 |
| Pages | 353-359 |
| Publication | M.D. Computing |
| Date | 1997 |
| Extra | Citation Key: leh97bay tex.citeulike-article-id= 13264497 tex.citeulike-linkout-0= http://www.ncbi.nlm.nih.gov/pubmed/9308343 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | R. J. Lilford |
| Author | D. Braunholtz |
| Volume | 313 |
| Pages | 603-607 |
| Publication | BMJ |
| Date | 1996 |
| Extra | Citation Key: lil96sta tex.citeulike-article-id= 13264515 tex.posted-at= 2014-07-14 14:09:37 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | R. D. Etzioni |
| Author | J. B. Kadane |
| Volume | 16 |
| Pages | 23-41 |
| Publication | Ann Rev Pub Hlth |
| Date | 1995 |
| Extra | Citation Key: etz95bay tex.citeulike-article-id= 13264055 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Peter M. Fayers |
| Author | Deborah Ashby |
| Author | Mahesh K. Parmar |
| Volume | 16 |
| Pages | 1413-1430 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: fay97bay tex.citeulike-article-id= 13264065 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Laurence Freedman |
| Volume | 313 |
| Pages | 569-570 |
| Publication | BMJ |
| Date | 1996 |
| Extra | Citation Key: fre96bay tex.citeulike-article-id= 13264103 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | James H. Albert |
| Volume | 47 |
| Pages | 182-191 |
| Publication | Am Statistician |
| Date | 1993 |
| Extra | Citation Key: alb93tea tex.citeulike-article-id= 13263681 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | John K. Kruschke |
| Author | Torrin M. Liddell |
| URL | http://dx.doi.org/10.3758/s13423-017-1272-1 |
| Pages | 1-23 |
| Date | 2017 |
| Extra | Citation Key: kru17bay tex.booktitle= Psychonomic Bulletin & Review tex.citeulike-article-id= 14379017 tex.citeulike-attachment-1= kru17bay.pdf; /pdf/user/harrelfe/article/14379017/1112234/kru17bay.pdf; 667a350e04440965997f085062e0249269d20ce3 tex.citeulike-linkout-0= http://dx.doi.org/10.3758/s13423-017-1272-1 tex.citeulike-linkout-1= http://link.springer.com/article/10.3758/s13423-017-1272-1 tex.posted-at= 2017-06-19 02:27:08 tex.priority= 0 tex.publisher= Springer US |
| DOI | 10.3758/s13423-017-1272-1 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
Excellent for teaching Bayesian methods and explaining the advantages