Online Training Modules

 On this page are 16 modules designed to address the diverse topics in the field of CER. Throughout the modules, examples of analysis methods are presented in a format that allows participants to understand their application and to work through these examples using their own analysis program. Modules will also address the practical issues involved in conducting CER studies both generally and specifically at The Ohio State University.    

Please see the bottom of the page for technical viewing requirements.

The project described was supported by Award Number 8UL1TR000090-05, 8KL2TR000112-05, and 8TL1TR000091-05 from the National Center For Advancing Translational Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center For Advancing Translational Sciences or the National Institutes of Health.


Module 1: Causality, Effect Identification & Statistical Inference

Instructor: J. Michael Oakes, PhD

This series begins with background information on comparative effectiveness research methodology. This discussion includes a summary of the meaning of research in science and a brief historical snapshot of statistics. Dr. Oakes focuses the majority of this lecture on an overview of followed by basic statistical concepts including causation and counterfactuals, effect identification and statistical inference. (87:08)

Module 2: Research Design in Comparative Effectiveness Research

Instructor: J. Michael Oakes, PhD

This module builds the concepts presented in Module 1 and provides a review and comparison of experimental and observational studies. The discussion topics include double blind randomized controlled trials (RCT), non-blind RCTs, cross-over experiments, factorial experiments, group-randomized trials, natural experiments and regression discintunuity designs, observational studies, quasi-observational studies and interrupted time series, and a discussion of assumptions and issues that appear in study design decision making. (107:29)

Module 3: Propensity Score Theory

Instructor: J. Michael Oakes, PhD

This module reviews the theory of propensity score research methodology to give learners a conceptual understanding of propensity score application. It includes a discussion of the purpose of propensity score methodology, matching methods and estimation of error and provides a discussion of more advanced statiscal topics, such as logistic regression, use of calipers and bootstrapping. (92:54)

Module 4: Propensity Score Application

Instructor: J. Michael Oakes, PhD

This module provides instruction in the application of propensity score methods discussed in Module 3 and provides definitions of treatment effects, including average treatment effect (ATE), average treatment effect on the treated (ATT) and treatment effect on the treated (TOT). In addition, a STATA tutorial takes the learner step-by-step through the process of typical analysis and propensity score analysis. (84:36)

Module 5: Instrumental Variable Methods

Instructor: J. Michael Oakes, PhD

This module describes how instrumental variables (IV) can be used to address questions in health sciences research. The lecture includes three examples using IVs, a section on local average treatment effect (LATE) and an example solved using STATA. (85:53)

Module 6: Introduction to Economic Evaluation

Instructor: Sean D. Sullivan, BScPharm, MSc, PhD

This module introduces economic evaluation methods. The lecture discusses the current increase in motivation for economic evaluations of medical technology, describes the outline of a cost-effectiveness evaluation and how the results are used in the field.  Quality-adjusted life-years (QALY) and an example evaluation of implanted cardiac defibrillators are included. (51:50)

Module 7: Decision Modeling

Instructor: David L. Veenstra, PhD

This module discusses the concepts and application of decision modeling. Topics include an overview of heuristics and biases, considerations important to framing a study and steps in conducting a decision analysis. Information is given on statistical analysis validation techniques and decision modeling programs. (86:40)

Module 8: Working with Health Care Claims and Complex Survey Data

Instructor: Brook Martin, Ph.D. M.P.H.

This module describes how to work with health care claims and complex survey data.  Lecture materials outline strengths and limitations of these data types and provide examples of commonly used data sources in health services research. STATA programming typically used with this data are reviewed. (74:24)

Module 9: Simple Linear Regression

Instructor: Brook Martin, Ph.D. M.P.H.

This module reviews descriptive statistics and simple linear regression. Data types, analysis frameworks and construction of multivariate model with appropriate assumption testing are demonstrated in this module. (73:54)

Module 10: Survival Analysis

Instructor: Brook Martin, Ph.D. M.P.H.

This module provides an introduction to survival analysis.  Topics include censoring, the Cox-Proportional Hazard regression model and relevant STATA programming. (59:43)

Module 11: An Introduction to Systematic Reviews

Instructor: Susan L. Norris, MD, MPH, MS

This module introduces the guiding principles for evaluating evidence in high quality systematic reviews. The lecture reviews steps for  creating, appraising and utilizing systematic reviews. (50:41)

Module 12: An Introduction to Meta-Analysis

Instructor: Susan L. Norris, MD, MPH, MS

This module describes the concept of high quality meta-analysis. The lecture includes terminology and context for conducting meta-analyses. Technical information on fixed effects and random effects models is presented; the impact of heterogeneity and a bias assessment guide are discussed. (58:32)

Module 13: Translating CER Evidence into Practice, Policy and Public Health

Instructor: Henry Lee, MD

This module describes the foundation for translation of evidence into practice and policy. Definitions, examples of implementation, a history and taxonomy of translational science and the importance of quality in evidence translation are discussed.  (66:38)


Module 14: Translational Toolbox

Instructor: Ralph Gonzales, MD, MSPH

This module discusses tools used to translate evidence into practice and policy.  Topics include translational science processes, essential translation tools, and examples that highlight the keys to successful improvements. (58:47)

Module 15: Pragmatic Clinical Trials 1

Instructor: Christopher Granger, M.D., John P. Vavalle, MD.

This module discusses the principles, capacity and barriers guiding the use of randomized controlled trials (RCT).  The importance of RCTs in the practice of medicine and the creation of medical guidelines is highlighted.  (64:26)

Module 16: Pragmatic Clinical Trials 2

Instructor: Christopher Granger, M.D., John P. Vavalle, MD.

This module posits solutions to current limitations and inefficiencies of randomized clinical trials and shows examples of successful studies. The balance between validity and generalizability are discussed in this lecture. (44:58)


Module 17: Mixed Methods in CER

Instructor: Shoshanna Sofaer, DR.P.H..

This module gives an overview of mixed methods and their application in comparative effectiveness research. The lecture includes a description of the key differences between quantitative, qualitative, and mixed methods, and appropriate application of mixed methods with clinical and non-clinical examples. The benefits and risks involved with using mixed methods are also discussed. (54:35)


Module 18: STATA Dataset Practice Example- HCUP

Instructor: Brook I. Martin, PhD.

This in-depth module walks the student step-by-step through a STATA analysis of HCUP data. Students may download the educational materials and follow Dr. Martin through learning about HCUP data, data analysis workflow, a working example of analysis and discussion of results. Useful tips include how to work with ICD-9 codes in STATA, acquire HCUP data and combining datasets and write foundational STATA code. (1:49:48)


Module 19: Comparative Effectiveness Research 101

Instructor: Michael A. Stoto, PhD.

This module gives an introduction to comparative effectiveness research (CER), definitions, examples, and basic methodology. The role of CER in initiatives such as PCOR (Patient Centered Outcomes Research) and the dissemination and funding of CER knowledge are covered. (32:42)


To continue exploring Comparative Effectiveness Research Modules,
please visit our 2011 Summer Program Recordings page.

Technical Information

To view a module, click the "View Module" button.

To print the slides corresponding to the lecture by clicking the "Print Slides" button.  

**Please note that to view instructional content, you will need to have the following free plug-ins installed on your computer:

- Microsoft Silverlight for viewing the modules, which is available here

- Adobe Reader for viewing printable slides and supplementary materials, which is available here

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