• Statistical Remedies for Medical Researchers

    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

    Tags:

    • basic
    • bayes
    • teaching-mds
  • Reporting Bayesian Results

    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

    Tags:

    • rct
    • bayes
    • teaching-mds
    • reporting-statistical-results
    • reporting
    • reporting-guidelines
    • reporting-clinical-trials
  • Dicing with the unknown

    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

    Tags:

    • teaching
    • bayes
    • teaching-mds
    • probability
  • Détente: A Practical Understanding of P-values and Bayesian Posterior Probabilities

    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

    Tags:

    • p-value
    • bayes
    • teaching-mds
  • A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials

    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

    Tags:

    • teaching
    • bayes
    • teaching-mds

    Notes:

    • 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.

  • Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis: A Randomized Clinical Trial

    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> &lt; .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

    Tags:

    • teaching
    • bayes
    • teaching-mds
    • diagnosis
  • Bayesian clinical trials

    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

    Tags:

    • rct
    • bayesian-methods
    • teaching-mds
    • review

    Notes:

    • 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."

  • Teaching elementary Bayesian statistics with real applications in science

    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

    Tags:

    • teaching
    • bayesian-inference
    • scientific-approach
  • Bayes offers a `New' way to make sense of numbers

    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

    Tags:

    • teaching
    • rct
    • bayes
    • clinical-trials
  • Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

    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

    Tags:

    • bayesian-inference
    • bayes
    • bayesian-methods
    • teaching-mds
  • Statistical rethinking : a Bayesian course with examples in R and Stan

    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

    Tags:

    • bayesian-inference
    • bayes
    • bayesian-methods
    • teaching-mds
  • Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications

    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

    Tags:

    • bayesian-inference
    • bayes
    • excellent-for-teaching-bayesian-methods-and-explaining-the-advantages
  • The Analysis of Experimental Data: The Appreciation of Tea and Wine

    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

    Tags:

    • bayes
    • teaching-statisticians
    • teaching-mds
  • The Substitute for p-Values

    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

    Tags:

    • bayesian-inference
    • p-values
    • teaching-statisticians
    • teaching-mds
  • Bayesian estimation supersedes the t test.

    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

    Tags:

    • bayesian-inference
    • bayes
    • teaching-mds
    • tutorial
    • basic
  • Implementing the Bayesian paradigm: reporting research results over the World-Wide Web.

    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

    Tags:

    • rct
    • bayes
    • teaching-mds
  • Interpretation of subgroup analyses in medical device clinical trials

    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

    Tags:

    • teaching
    • shrinkage
    • empirical-bayes
    • subgroup-analysis
    • differential-treatment-effects
  • The intellectual health of clinical drug evaluation

    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

    Tags:

    • bayesian-inference
    • rct
    • teaching-mds
    • statistical-significance
    • reporting
    • compliance
    • clinical-trials
    • hypothesis-testing
    • review

    Notes:

    • problems with traditional statistical approaches to drug evaluation;problems with under-emphasis of type II error

  • Bayesian statistics without tears: A sampling-resampling perspective

    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

    Tags:

    • teaching
    • bayesian-inference
    • changing-prior
    • sampling-importance-resampling
    • weighted-bootstrap
  • Bayesian communication of research results over the World Wide Web

    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

    Tags:

    • bayesian-methods
    • varying-prior-using-www
    • web-based-teaching
  • The statistical basis of public policy: A paradigm shift is overdue

    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

    Tags:

    • bayesian-inference
    • teaching-mds
    • excellent-for-teaching-bayesian-methods-and-explaining-the-advantages
    • adjusting-for-study-bias-or-quality
  • Bayesian statistical methods in public health and medicine

    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

    Tags:

    • teaching
    • bayesian-inference
  • Tutorial in Biostatistics: Bayesian data monitoring in clinical trials

    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

    Tags:

    • bayesian-inference
    • sequential-monitoring
    • study-design
    • rct
    • choice-of-prior-distribution
    • convincing-clinicians-to-alter-medical-practice
    • skeptical-prior
    • teaching-paper
  • Bayesian statistical methods

    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

    Tags:

    • bayesian-inference
    • teaching-mds
  • Explaining the Gibbs sampler

    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 11/8/2019, 8:01:59 AM

    Tags:

    • teaching
    • bayesian-inference
    • markov-chain
    • resampling
    • data-augmentation
    • gibbs-sampler
    • monte-carlo
  • Teaching Bayesian statistics using sampling methods and MINITAB

    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

    Tags:

    • teaching
    • bayesian-inference
    • sampling-importance-resampling
    • weighted-bootstrap
  • Bayesian data analysis for newcomers

    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

    Tags:

    • teaching
    • bayesian-inference
    • teaching-mds

    Notes:

    • Excellent for teaching Bayesian methods and explaining the advantages