P.Mean >> Statistics webinar >> Archives of previously held webinars (created 2009-10-16).

This page will provide descriptions of previously held webinars. Recordings of these past webinars are not available. I do expect to repeat some of these webinars in the future and if there is one you would especially like to see, it can't hurt to send me an email.

2012-10-03. Statistical Literacy for Medical Librarians: Swimming in a Sea of Conflicting Medical Claims.

This is a live seminar, not a webinar, but I thought I'd store the information and handouts here. You can also find details about this class on the web at
--> http://www.mcmla.org/2012CE

This is a four hour short course, taught for the Midcontinental Chapter of the Medical Library Association on October 3, 2012 in Kansas City, MO.

Abstract: In your role as librarian, do you need to know, or want to increase your knowledge about statistical concepts such as confidence intervals, odds ratios, p-values, and measures of risk? Do you strive to interpret the medical literature, and wish you understood more clearly how studies relate to research findings and conclusions? If so, this class is for you. We will use selected articles from the medical literature as examples, and through a combination of lecture, practice, and quizzes to check your knowledge, help to further your understanding of the statistical terms and methods behind randomized trials, observational studies, and meta-analysis.

Handouts:

2012-05-29. Statistical Literacy for Medical Librarians: Swimming in a Sea of Conflicting Medical Claims.

This was a live seminar, not a webinar, but I thought I'd store the information and handouts here. You can also find details about this class on the web at
--> http://mlanet.org/am/am2012/ce/ce103.html

This is an eight hour short course, taught for the Medical Library Association national meeting on May 29, 2012 in Seattle WA. Susan Sander, Marie K. Saimbery, and Jenny Pierce were co-instructors.

Abstract: The class will be taught through a combination of lecture, demonstration, discussion, and hands-on exercises. Learners will gain skills in interpreting statistics in the medical literature through an understanding of concepts such as confidence intervals, odds ratios, p-values, and measures of risk. Participants will learn how different research design choices influence the ability of a research study to inform patients and health care providers about the benefits and risks of new medical therapies. Mini-lectures will build awareness of the history of causality in the health sciences, the hierarchy of evidence, and the librarian's role in the systematic review process. The class will include a discussion of the systematic review process with published authors of book chapters on this topic, librarians Marie K. Saimbert and Jenny Pierce.

Handouts

2010-09-23. A gentle introduction to Bayesian inference.

Abstract: P-values and confidence intervals are the fundamental tools used in most inferential data analyses. They are possibly the most commonly reported statistics in the medical literature. Unfortunately, both p-values and confidence intervals are subject to frequent misinterpretations. In this webinar, you will review the interpretation of p-values and confidence intervals, and see an alternative approach based on Bayesian data analysis.

Registration: To register, please send an email to mail@pmean.com with the words "September 22 webinar" in the title.

Connection: When you register, you should receive two emails, an introductory email that looks something like this and an email with connection information that looks something like this. If you do not get these emails, please let me know.

Handouts: Here is the handout: one slide per page, or six slides per page.

2010-09-22. What is a confidence interval?

Abstract: A confidence interval provides information about the uncertainty associated with a statistical estimate. It is a vital piece of information for assessing whether a clinically important change has occured and if the sample size in the study was sufficiently large. In this presentation, you will learn how to interpret confidence intervals and identify common abuses and misconceptions.

Registration: To register, please send an email to mail@pmean.com with the words "September 22 webinar" in the title.

Connection: When you register, you should receive two emails, an introductory email that looks something like this and an email with connection information that looks something like this. If you do not get these emails, please let me know.

Handouts: Here is a PDF version of the handout (slides and notes).

2010-09-21. What is a p-value?

Abstract: The P-value is the fundamental tools used in most inferential data analyses. It is possibly the most commonly reported statistics in the medical literature. Unfortunately, p-values are subject to frequent misinterpretations. In this presentation, you will learn the proper interpretation of p-values, and the common abuses and misconceptions about this statistic.

Registration: To register, please send an email to mail@pmean.com with the words "September 21 webinar" in the title.

Connection: When you register, you should receive two emails, an introductory email that looks something like this and an email with connection information that looks something like this. If you do not get these emails, please let me know.

Handouts: Here is a PDF version of the handout (slides and notes).

2010-08-24. Data entry and data management issues with examples in IBM SPSS.

Abstract: This training class will give you a general introduction to data management using IBM SPSS software. This class is useful for anyone who needs to enter or analyze research data. There are three steps that will help you get started with data entry for a research project. First, arrange your data in a rectangular format (one and only one number in each intersection of every row and column). Second, create a name for each column of data and provide documentation on this column such as units of measurement. Third, create codes for categorical data and for missing values. This class will show examples of data entry including the tricky issues associated with data entry of a two by two table and entry of dates. No statistical experience is necessary (explain). No special hardware/software is needed (explain).

Registration: To register, please send an email to mail@pmean.com with the words "August 24 webinar" in the title.

Connection: When you register, you should receive two emails, an introductory email that looks something like this and an email with connection information that looks something like this. If you do not get these emails, please let me know.

Handouts: Here is the handout for this class.

2010-07-15. The first three steps in a logistic regression analysis with examples in IBM SPSS.

Abstract: This training class will give you a general introduction in how to use IBM SPSS software to compute logistic regression models. Logistic regression models provide a good way to examine how various factors influence a binary outcome. There are three steps in a typical logistic regression analysis: First, fit a crude model. Second, fit an adjusted model. Third, examine the predicted probabilities. These steps may not be appropriate for every logistic regression analysis, but they do serve as a general guideline. In this presentation, you will see these steps applied to data from a breast feeding study, using SPSS software. Objectives: In this class, you will learn how to compute and interpret simple odds ratios; and relate the output of a logistic regression model to these odds ratios. No statistical experience is necessary (explain). No special hardware/software is needed (explain).

Registration: To register, please send an email to mail@pmean.com with the words "July 15 webinar" in the title.

Connection: When you register, you should receive two emails, an introductory email that looks something like this and an email with connection information that looks something like this. If you do not get these emails, please let me know.

Handouts: The final handout is now available, either one slide per page or six slides per page.

2010-07-14. The first three steps in a linear regression analysis with examples in IBM SPSS.

Abstract: This class will give you a general introduction in how to use SPSS software to compute linear regression models. Linear regression models provide a good way to examine how various factors influence a continuous outcome measure. There are three steps in a typical linear regression analysis: fit a crude model, fit an adjusted model, and check your assumptions. These steps may not be appropriate for every linear regression analysis, but they do serve as a general guideline. In this class you will learn how to: interpret the slope and intercept in a linear regression model; compute a simple linear regression model; and make statistical adjustments for covariates. No statistical experience is necessary (explain). No special hardware/software is needed (explain).

Registration: To register, please send an email to mail@pmean.com with the words "July 14 webinar" in the title.

Connection: When you register, you should receive two emails, an introductory email that looks something like this and an email with connection information that looks something like this. If you do not get these emails, please let me know.

Handouts: Draft handouts for this class are available in PDF format, either one slide per page, or six slides per page. The final and official handout should be posted here 24 hours prior to the webinar.

2010-06-10. What do all these numbers mean? Sensitivity, specificity, and likelihood ratios.

Abstract: This one hour training class will give you a general introduction to numeric summary measures for diagnostic testing. You will learn how to distinguish between a diagnostic test that is useful for ruling in a diagnosis and one that is useful for ruling out a diagnosis. You will also see an illustration of how prevalence of disease affects the performance of a diagnostic test. This class is useful for anyone who reads journal articles that evaluate these tests.

Requirements: No statistical experience is necessary (explain). No special hardware/software is needed (explain). Please have a pocket calculator available during this presentation to help with some simple divisions.

Connection: If you registered, you should have received an email that looks something like this. If you have not registered yet, follow these instructions, but please also send me an email as a courtesy.

Handout: I will post a handout for this class at least 24 hours prior to the webinar. Here are handouts from an earlier version of this class, available in PDF format, either one slide per page, or six slides per page.

2010-06-02. How it all fits together: Systematic overviews and meta-analysis.

Abstract: This class helps you assess the quality of a systematic overview or meta-analysis. In this class you will learn how to: recognize sources of heterogeneity in meta-analysis; identify and avoid problems with publication bias; and explain the ethical concerns with failure to publish and with duplicate publication. This material is derived mainly from Chapter 5 of Statistical Evidence in Medical Trials. This webinar is the fifth webinar (out of five) in a series titled Statistical Literacy for Medical Librarians: Swimming in a Whirlpool of Conflicting Medical Claims.

Handout: Here is the final handout, one slide per page or six slides per page.

Reading #1: Wim Van Biesen, Francis Verbeke, Raymond Vanholder. An infallible recipe? A story of cinnamon, souffle and meta-analysis. Nephrol. Dial. Transplant. 2008;23(9):2729-2732. Excerpt: "Meta-analyses certainly do have their place in scientific research. Like herbs, if used in the correct dish, and not too much or too often, they can give that extra bit of flavour that turns ‘food’ into a ‘delicious dish’. However, meta-analyses are like cinnamon: very tasteful in small quantities and in the right dish, but if you use them too much or in the wrong dish, it ruins all other flavours and you get nausea. Just as for the cinnamon, it requires skills and insight to know when and how to use a meta-analysis." [Accessed May 27, 2010]. Available at: http://ndt.oxfordjournals.org/cgi/content/full/23/9/2729.

Reading #2: C David Naylor. Meta-analysis and the meta-epidemiology of clinical research. BMJ. 1997;315:617-9. Excerpt: "This week's BMJ contains a pot-pourri of materials that deal with the research methodology of meta-analysis. Meta-analysis in clinical research is based on simple principles: systematically searching out, and, when possible, quantitatively combining the results of all studies that have addressed a similar research question. Given the information explosion in clinical research, the logic of basing research reviews on systematic searching and careful quantitative compilation of study results is incontrovertible. However, one aspect of meta-analysis as applied to randomised trials has always been controversial —combining data from multiple studies into single estimates of treatment effect." [Accessed May 19, 2010]. Available at: http://www.bmj.com/cgi/content/extract/315/7109/617.

2010-05-26. The first three steps in a linear regression analysis with examples in IBM SPSS.

Abstract: This class will give you a general introduction in how to use SPSS software to compute linear regression models. Linear regression models provide a good way to examine how various factors influence a continuous outcome measure. There are three steps in a typical linear regression analysis: fit a crude model, fit an adjusted model, and check your assumptions These steps may not be appropriate for every linear regression analysis, but they do serve as a general guideline. In this class you will learn how to: interpret the slope and intercept in a linear regression model; compute a simple linear regression model; and make statistical adjustments for covariates. No statistical experience is necessary (explain). No special hardware/software is needed (explain). I will post a handout for this class at least 24 hours prior to the webinar.

Connection: If you registered, you should have received an email that looks something like this.

Handouts: Here are handouts for this class, available in PDF format, either one slide per page, or six slides per page.

2010-05-19. It's just what the doctor ordered. Observational studies.

Abstract: An observational study is a study where the researchers do not directly intervene, but instead let the patients and/or their doctors choose the treatment. Observational studies also arise when a group is intact at the start of the study. There are four types of observational studies: cohort studies, case-control studies, cross-sectional studies, and historical control studies. While observational studies are generally considered to be less authoritative than randomized studies, with careful selection of the control subjects, observational studies can still provide persuasive results. This webinar is the fourth webinar (out of five) in a series titled Statistical Literacy for Medical Librarians: Swimming in a Whirlpool of Conflicting Medical Claims.

Handout: You can download a PDF version of the handout, one slide per page, or six slides per page. If you have the time, please skim over these four abstracts, which I will use in my talk

Reading #1: von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Annals of Internal Medicine, 147(8), 573-577. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17938396

Reading #2: Concato, J., Shah, N., & Horwitz, R. I. (2000). Randomized, controlled trials, observational studies, and the hierarchy of research designs. The New England Journal of Medicine, 342(25), 1887-1892. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10861325

Reading #3: Mann, C. J. (2003). Observational research methods. Research design II: cohort, cross sectional, and case-control studies. Emergency Medicine Journal: EMJ, 20(1), 54-60. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12533370

2010-05-05. Putting your life in the hands of a coin: Randomized trials.

Abstract: In research studies that compare a treatment group and a control group, you need to assess whether the comparison is a fair comparison— an apples to apples comparison. Randomization is a simple method that insures that patients assigned to the treatment group are comparable to patients assigned to the control group. There are, however, practical and ethical constraints that can sometimes prevent the use of randomization. This webinar is the third webinar (out of five) in a series titled Statistical Literacy for Medical Librarians: Swimming in a Whirlpool of Conflicting Medical Claims.

Learning objectives: In this class you will learn how to: describe how covariate imbalance can threaten the validity of a research study, explain how randomization prevents covariate imbalance, and understand the practical and ethical limitations to randomized studies.

Handouts: You can download a PDF version of the handout, one slide per page, or six slides per page.

Reading #1: Richard Peto, Colin Baigent. Trials: the next 50 years. BMJ. 1998;317(7167):1170-1171. Excerpt: "Over the past half century there has been a vast proliferation first of randomised trials and now of meta-analyses, both of which (if appropriately analysed) can avoid bias. But to get medically reliable answers to previously unanswered questions about life or death treatment decisions it isn't enough just to avoid bias. We must also ensure that we are not seriously misled by the play of chance, and often the only way to do this reliably is to get appropriate analyses of really large scale randomised evidence." [Accessed April 30, 2010]. Available at: http://www.bmj.com/cgi/content/full/317/7167/1170.

Reading #2: Merrick Zwarenstein, Shaun Treweek. What kind of randomized trials do we need? CMAJ. 2009;180(10):998-1000. Excerpt: "In 1967, Daniel Schwartz and Joseph Lellouch, 2 French statisticians, and their British colleague and translator Michael Healy wrote "[M]ost therapeutic trials are inadequately formulated, and this from the earliest stages of their conception." The seminal paper from which this dramatic assertion is drawn is reprinted in the May 2009 issue of the Journal of Clinical Epidemiology as part of a joint focus with CMAJ on making randomized controlled trials (RCTs) more useful." [Accessed April 30, 2010]. Available at: http://www.cmaj.ca/cgi/content/full/180/10/998.

Reading #3: Kenneth Schulz, Douglas Altman, David Moher, the CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMC Medicine. 2010;8(1):18. Abstract: "The CONSORT statement is used worldwide to improve the reporting of randomised controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience.To encourage dissemination of the CONSORT 2010 Statement, this article is freely accessible on bmj.com and will also be published in the Lancet, Obstetrics and Gynecology, PLoS Medicine, Annals of Internal Medicine, Open Medicine, Journal of Clinical Epidemiology, BMC Medicine, and Trials." [Accessed April 30, 2010]. Available at: http://www.biomedcentral.com/1741-7015/8/18.

Reading #4: Benjamin Djulbegovic. The Paradox of Equipoise: The Principle That Drives and Limits Therapeutic Discoveries in Clinical Research. Cancer Control. 2009;16(4):342-347. Excerpt: "Progress in clinical medicine relies on the willingness of patients to take part in experimental clinical trials, particularly randomized controlled trials (RCTs). Before agreeing to enroll in clinical trials, patients require guarantees that they will not knowingly be harmed and will have the best possible chances of receiving the most favorable treatments. This guarantee is provided by the acknowledgment of uncertainty (equipoise), which removes ethical dilemmas and makes it easier for patients to enroll in clinical trials." Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2782889/.

2010-04-28. The first three steps in selecting a sample size.

Abstract: One of your most critical choices in designing a research study is selecting an appropriate sample size. A sample size that is either too small or too large will be wasteful of resources and will raise ethical concerns. In this class, you will learn how to: identify the information you need to produce a power calculation; justify an appropriate sample size for your research; and examine the sensitivity of the sample size to changes in your research design. No statistical experience is necessary (explain). No special hardware/software is needed (explain).

Connection: If you registered, you should have received an email that looks something like this. If all else fails, go to http://pmean.webex.com and look for meeting number: 804 025 209. Then, enter the meeting password: webinar4386. Last minute attendee? Drop me an email as a courtesy, and follow the instructions posted above.

Handouts: Handouts are available one slide per page or six slides per page.

2010-04-21. How bad is it, really? Measures of risk.

Abstract: The odds ratio and the relative risk are both measures of risk used for binary outcomes, but sometimes they can differ markedly from one another. The relative risk offers a more natural interpretation, but certain research designs preclude its computation. Another measure of risk, the number needed to treat, provides comparisons on an absolute rather than relative scale and allow you to assess the trade-offs between effects and harms. This webinar is the second webinar (out of five) in a series titled Statistical Literacy for Medical Librarians: Swimming in a Whirlpool of Conflicting Medical Claims.

Handout: You can download a PDF version of the handout, one slide per page, or six slides per page. A last minute revision with a few extra tutorial slides on odds and with some of the more technical material moved to the end, is available in one slide per page format only.

Reading #1: Simon, S. D. (2001). Understanding the odds ratio and the relative risk. Journal of Andrology, 22(4), 533-536. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11451349.

Reading #2: Barratt, A., Wyer, P. C., Hatala, R., McGinn, T., Dans, A. L., Keitz, S., Moyer, V., et al. (2004). Tips for learners of evidence-based medicine: 1. Relative risk reduction, absolute risk reduction and number needed to treat. CMAJ: Canadian Medical Association Journal = Journal De l'Association Medicale Canadienne, 171(4), 353-358. Retrieved from http://www.cmaj.ca/cgi/content/full/171/4/353.

Reading #3: Jaeschke, R., Guyatt, G., Shannon, H., Walter, S., Cook, D., & Heddle, N. (1995). Basic statistics for clinicians: 3. Assessing the effects of treatment: measures of association. CMAJ: Canadian Medical Association Journal = Journal De l'Association Medicale Canadienne, 152(3), 351-357. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7828099

2010-04-07. Show me your proof: Confidence intervals and p-values.

Abstract: P-values and confidence intervals are the fundamental tools used in most inferential data analyses. They are possibly the most commonly reported statistics in the medical literature. Unfortunately, both p-values and confidence intervals are subject to frequent misinterpretations. In this presentation, you will learn the proper interpretation of p-values and confidence intervals, and the common abuses and misconceptions about these statistics. This webinar is the first webinar (out of five) in a series titled Statistical Literacy for Medical Librarians: Swimming in a Whirlpool of Conflicting Medical Claims.

Learning objectives: distinguish between statistical significance and clinical significance; define and interpret p-values; explain the ethical issues associated with inadequate sample sizes.

Handouts: You can download a PDF version of the handout, one slide per page, or six slides per page.

Reading #1: Dallal, G. (1999). Confidence Intervals. In The Little Handbook of Statistical Practice. Retrieved from http://www.jerrydallal.com/LHSP/ci.htm.

Reading #2: Guyatt, G., Jaeschke, R., Heddle, N., Cook, D., Shannon, H., & Walter, S. (1995). Basic statistics for clinicians: 1. Hypothesis testing. CMAJ: Canadian Medical Association Journal = Journal De l'Association Medicale Canadienne, 152(1), 27-32. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7804919.

Reading #3: Guyatt, G., Jaeschke, R., Heddle, N., Cook, D., Shannon, H., & Walter, S. (1995). Basic statistics for clinicians: 2. Interpreting study results: confidence intervals. CMAJ: Canadian Medical Association Journal = Journal De l'Association Medicale Canadienne, 152(2), 169-173. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7820798.

Postscript: There are two webpages, P.Mean: Interpreting p-values in a published abstract, part 1 (created 2010-04-14) and P.Mean: Quiz about p-values (created 2010-04-14) that I wrote afterwards to elaborate a bit on topics raised during the webinar.

2010-03-31. Putting it all together: meta-analyses and systematic overviews.

Abstract: This class helps you assess the quality of a systematic overview or meta-analysis. In this class you will learn how to: recognize sources of heterogeneity in meta-analysis; identify and avoid problems with publication bias; and explain the ethical concerns with failure to publish and with duplicate publication. No statistical experience is necessary (explain). No special hardware/software is needed (explain).

Handouts: Handouts are available one slide per page or six slides per page.

Connection: If you registered, you should have received an email that looks something like this. If all else fails, go to http://pmean.webex.com and look for meeting number: 809 609 888, and enter the meeting password: webinar9149. Last minute attendee? Drop me an email as a courtesy, and follow the instructions posted above.

2010-02-17. What do all these numbers mean? Sensitivity, specificity, and likelihood ratios

Abstract: This one hour training class will give you a general introduction to numeric summary measures for diagnostic testing. You will learn how to distinguish between a diagnostic test that is useful for ruling in a diagnosis and one that is useful for ruling out a diagnosis. You will also see an illustration of how prevalence of disease affects the performance of a diagnostic test. Please have a pocket calculator available during this presentation. This class is useful for anyone who reads journal articles that evaluate these tests. No statistical experience is necessary (explain). No special hardware/software is needed (explain).

Handouts: The handout for this webinar is available in PDF format, either one slide per page, or six slides per page.

Connection: Last minute registration? Spots are still open. Please send me an email as a courtesy. Details for connecting can be found in this PDF file.

2010-02-02. Jumpstart Statistics: Data entry and data management issues

Abstract: This is the first of several in a series, Jumpstart Statistics, that is intended to help beginning researchers restart a stalled research project. This class will show how to enter and document data in IBM SPSS. You will learn the importance of including documentation with your data during data entry. Special attention will be given to the tricky issues associated with data in two by two tables and data that has calendar dates. Click here to find out why I am limiting enrolment in this webinar. Find out more about the Jumpstart Statistics series. To register, send an email to with the words "February 2 webinar" in the subject line. A draft handout is available. A final version of the handout is available, one slide per page.

2010-01-21. The first three steps in a descriptive data analysis, with applications in IBM SPSS.

Abstract: There are three steps that will help you get started with descriptive data analysis: 1. Know your count, how much data you have and how much data is missing. 2. Compute ranges and frequencies for individual variables. 3. Examine relationships among pairs of variables using crosstabs, boxplots, and scatterplots.

Handouts: The handout for this webinar is available in PDF format, either one slide per page, or six slides per page.

2009-12-17. What do all these numbers mean? Odds ratios, relative risks, and number needed to treat.

2009-11-04. The first three steps in data entry, with examples in PASW/SPSS.

2009-10-14. P-values, confidence intervals, and the Bayesian alternative.

I have also archived a copy of the instructions about signing up.

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Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-12-30.