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Statistics

A broad range of statistics-related courses are available online. Some of these courses have been adapted from the STAT: Statistical Techniques Amicably Taught classroom series.

CME: College of Medicine, Mayo Clinic, designates this educational activity for a maximum of one AMA PRA Category 1 Credit.

General concepts

"Study Designs Commonly Used in Clinical Research"

  • This workshop is designed for the investigator needing assistance in determining the design of a research study. Participants will learn the fundamental concepts of a variety of study designs used in research studies, as well as the strengths and weaknesses of the designs. Participants explore the different randomization techniques and when it's best to use each. Released July 1, 2012; credit expires Dec. 31, 2013.
  • Complexity: Introductory
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Avoiding Statistical Pitfalls"

  • Statistical tests are often abused and misused, which can result in study findings that are incorrect and sometimes misleading. This module reviews common pitfalls and explains how to avoid them. Released Dec. 1, 2010; credit expires Dec. 31, 2013.
  • Complexity: Introductory
  • Presenter: Felicity T. Enders, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Basics of Statistics"

  • Many research studies involve the use of quantitative statistical tests. Researchers who understand how statistical tests work — including what they can and cannot do — will be better poised to critically review medical literature and design and execute studies. Released Dec. 1, 2010; credit expires Dec. 31, 2013.
  • Complexity: Introductory
  • Presenter: Felicity T. Enders, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Clinical Data Management"

  • This module explores the dynamic relationship among people, processes and technology in clinical data management. This module is helpful for anyone with an interest in improving data quality on research studies, particularly anyone involved in the conduct of a study under an investigational new drug (IND) application or investigational device exemption (IDE).

    Participants will develop techniques to better ensure quality data while gaining an appreciation for how federal regulations may affect decisions made with respect to the chosen data management plan. Released Jan. 1, 2011; credit expires Dec. 31, 2013.

  • Complexity: Introductory
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Data Basics: Understanding and Illustrating Research Data"

  • This module introduces various data types commonly seen in research studies. Proper identification of the data type is essential to summarize and analyze the data appropriately. Summary measures, including measures of central tendency ("averages") and variation (the "spread" of the data), are discussed for qualitative and quantitative data.

    In addition, this module illustrates ways that data can be graphically displayed to uncover important attributes or limitations of the data. The target audience is students with minimal training in statistics who are new to working with data. Released July 1, 2012; credit expires Dec. 31, 2013.

  • Complexity: Introductory
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Working With a Statistician"

  • Most researchers rely on statisticians to help them analyze their results. What information should you take to your statistical consultation? You and your statistician will both be glad you learned the answer in this module. Released Dec. 1, 2010; credit expires Dec. 31, 2013.
  • Complexity: Introductory
  • Presenter: Felicity T. Enders, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

Hypothesis testing, power and sample size

"Sample Size and Power Considerations: Precision and Hypothesis Testing"

  • This module outlines basic principles for estimating sample size calculations for research studies. It combines concepts of statistical precision with statistical assumption to yield a foundation that can be generalized to more-complex research settings. Released Jan. 1, 2011; credit expires Dec. 31, 2013.
  • Complexity: Introductory
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"T-Tests and ANOVA Models"

  • T-tests and ANOVA models are statistical methods commonly used in research. As is detailed in this module, they are routinely used to compare mean values between two or more groups.

    This module introduces each test by providing the rationale for the test and the proper interpretation of the results. Students should already be familiar with basic calculations such as means, standard deviations and proportions. Released July 1, 2012; credit expires Dec. 31, 2013.

  • Complexity: Introductory
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Common Statistics to Compare Two Proportions"

  • This module is intended for researchers who seek a greater understanding of the statistics that are used to summarize the difference in two proportions. These statistics, which are collectively known as measures of association, are readily calculated from summary data provided the data are tabulated as described in the module.

    Relative risk and odds ratio — two of the most common measures of association for independent groups — are the focus of this module. The proper interpretation of each measure is provided along with examples illustrating the necessary calculations. Released Jan. 1, 2011; credit expires Dec. 31, 2013.

  • Complexity: Moderate
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Role of Nonparametric Statistics in Medical Research"

  • Nonparametric statistics is a branch of statistics that addresses the need for statistical methodology that's robust to assumptions regarding the underlying distribution from which sample data are drawn. In the context of medical literature, the ubiquitous "bell-shaped curve" of the normal distribution may not match the data on hand.

    Special nonparametric tests have been developed to allow for such deviations and alleviate the need for complicated transformations of the data. In this module, several commonly reported nonparametric tests are developed and discussed as they pertain to actual data.

    The module also introduces the concept of "exact" statistical tests and describes how they can be used to support the analysis of research studies, particularly those with small sample sizes. Students should already have a basic understanding of hypothesis testing, p values and common statistical tests (such as the t-test). Released March 1, 2011; credit expires Dec. 31, 2013.

  • Complexity: Moderate
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

Multivariable methods

"Correlations and Partial Correlations"

  • This module presents the concept of "statistical adjustment" in the context of correlations and partial correlations. A partial correlation, as is detailed in the module, is a measure of the linear association of two variables after statistically controlling (adjusting) for the effects of one or more additional variables.

    These partial correlations serve as the basis for interpreting the importance of predictors in a regression model and are related to a useful measure of effect in regression analyses, namely the partial coefficient of determination. This module develops these concepts using simple graphs and motivating examples. Students should already have a working understanding of regression techniques and hypothesis testing. Released March 1, 2011; credit expires Dec. 31, 2013.

  • Complexity: Moderate
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

Advanced methods

"Assessing Diagnostic Accuracy"

  • The ability to assess and interpret diagnostic accuracy is essential in both research and clinical settings. This module examines a variety of statistical measures that one encounters in the literature pertaining to screening and diagnostic tests. Emphasis is placed on correctly interpreting the statistical measures that summarize the diagnostic accuracy of a screening test. In addition, the relationship between sensitivity and specificity is discussed as it relates to interpreting these results. Released July 1, 2012; credit expires Dec. 31, 2013.
  • Complexity: Moderate
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Mechanics of Statistical Monitoring"

  • This module emphasizes the potential pitfalls associated with analyzing clinical trial data as it accrues during the course of the study. The concepts of "multiplicity" and the potential for an increased probability of a type I error (false-positive result) serve as the foundation for the module.

    Statistically oriented terminology that is associated with interim monitoring (such as information fraction, stopping boundaries and alpha spending functions) is defined and illustrated through examples. The module concludes with specific recommendations for determining the number and frequency of interim analyses in the course of a study, guidance on selecting the stopping boundary, and unique considerations for interim monitoring of safety data. Released Jan. 1, 2011; credit expires Dec. 31, 2013.

  • Complexity: Moderate
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now

"Using Propensity Scores for the Analysis of Observational Studies"

  • Propensity scores are an important statistical advancement in the analysis of observational studies. The rationale for propensity scoring stems from the ideas of a counterfactual experiment. This module explores the motivation for — and estimation and use of — propensity scores.

    The propensity score approach will be contrasted to other approaches that can be considered for the analysis of observational data. Students should already have an understanding of basic study designs, use of logistic regression and common measures of association (such as the odds ratio). Released March 1, 2011; credit expires Dec. 31, 2013.

  • Complexity: Advanced
  • Presenter: Rickey E. Carter, Ph.D.
  • Mayo Clinic employees: Enroll now
  • Non-Mayo participants: Enroll now
  • Nov 2, 2012
  • ART579322