Course Modules | Title | Category #1 | Category #2 | Instructor | Description |
---|---|---|---|---|---|
CTSA Readings | Recommended Readings | ||||
00:24:08 |
Lesson 1: Course Introduction | General | Joy Melnikow, MD, MPH | Course Overview, CER Background | |
00:44:19 |
Lesson 2: History and Basic Elements of Randomized Clinical Trials | Experimental | Daniel J. Tancredi, PhD | Clinical Trials, Confounding in Design, Key advantages of Randomization, Blinding (Masking), A Landmark RCT, etc, no outline and is a mixed history of RCT's. | |
00:19:52 |
Lesson 3: Explanatory and Pragmatic Trials for CER | Experimental | General | Richard L. Kravitz, MD, MSPH | Examine clinical trials within the context of the varied research approaches used in comparative effectiveness research and other forms of clinical research. |
00:23:11 |
Lesson 4: Evidence-Based Medicine, Heterogeneity of Treatment Effects, and the Trouble with Averages | Evidence | General | Richard L. Kravitz, MD, MSPH | Fundamental contradiction of evidence based medicine: Non-uniform treatment effects, EBM estimates for average patients, not individual patients. |
00:20:53 |
Lesson 5: Outcomes Assessment in Clinical Trials | General | Richard L. Kravitz, MD, MSPH | Outcomes in context of CER, Taxonomy of outcomes, Surrogate outcomes, Composite outcomes. | |
00:35:48 |
Lesson 6: Analyzing Data From Randomized Clinical Trials | General | Experimental | Daniel J. Tancredi, PhD | Analysis of Data from large scale phase 3 trials, Essential features of analyses, Review good clinical practices concerning data analysis |
00:40:02 |
Lesson 7: Clinical Trials Data Infrastructure, Management, and Reporting | General | Experimental | Daniel J. Tancredi, PhD | Best practices, Trial policies and practices, Clinical Trials Methods |
00:20:53 |
Module 2 Overview | General | Observational | Patrick Romano, MD MPH | 6 defining characteristics of CER, Introduction of Guest Speakers, Observational Studies |
00:23:02 |
Lesson 8: Using the UC Davis Cohort Discovery Tool, Powered by i2b2, to Facilitate CER | General | Este Geraghty, MD, MS, MPH, FASCP | What is the UCD Cohort Discovery Tool?, Queries using the CDT, Live Demo, Where to get training | |
00:39:36 |
Lesson 9: Use of Observational Data in CER, Use of OSHPD Administrative Data | Observational | Richard H. White MD | Use of observational data in performing CER, Use of Administrative Data from OSHPD | |
00:21:07 |
Lesson 10: Computer-Aided Detection and Screening Mammography | Observational | General | Joshua J. Fenton, MD, MPH | Brief background about CAD technology, Observational Study from 2007 New England Journal of Medicine, How data infrastructures also support CER |
00:20:40 |
Lesson 11: Off-Pump vs. On-Pump Bypass Surgery | General | Zhongmin Li, PhD | Comparison of outcomes dealing with stroke for Off Pump vs. On Pump Bypass Surgery | |
00:18:19 |
Lesson 12: Using National HCUP Data to Evaluate CE of Pediatric Empyema Treatments | General | Su-Ting T. Li, MD, MPH | No Outline, Presentation of effectiveness of Empyema Treatments | |
01:16:50 |
Lesson 13: Managing Dependent & Independent Variables in Observational CER Studies | Observational | General | Peter Franks and Patrick Romano MD, MPH | Dependent and Independent variables in observational studies, Helpful tricks to develop analytic plan |
00:53:07 |
Lesson 14: Observational Studies for CER – How can they be made as good as RCT's | General | Patrick Romano, MD MPH | Role of Missing Data, How can missing data be minimized, Statistical Techniques to compensate for problems caused by missing data | |
00:45:08 |
Lesson 15: Instrumental Variables and Propensity Scores | Observational | Patrick Romano, MD MPH | Where is the confounding, Can it be removed or worked around? | |
00:53:47 |
Lesson 16: Systematic Review and Meta-analysis- Steps 1-4 | Evidence | General | Tonya Fancher, MD MPH | Formulating the Problem and Framing the Question, Searching the Literature, Gathering Information, Evaluating the Quality of Studies |
00:16:04 |
Lesson 17: Pub Med Search Demo | Bruce Abbot | Presentation Appears to be Broken | ||
00:33:39 |
Lesson 18: Analyzing, Interpreting and Presenting the Results of a Systematic Review | General | Evidence | Joy Melnikow, MD, MPH | Analyzie and integrate the outcomes of the studies, Involving statisticians with expertise in meta-analysis |
00:57:35 |
Lesson 19: Rules For Economic Efficiency | General | Paul Leigh | Health Production Function, Concave Downward Curve, Total Product, Marginal Product, Slope, Rule | |
01:12:28 |
Lesson 20: Approaches to Cost-effectiveness Modeling | Decision | Joy Melnikow, MD, MPH | Overview of components involved in cost-effectiveness model, Review of sensitivity analysis | |
00:33:54 |
Lesson 21: Estimating Health-Related Quality of Life for CEA | General | Joy Melnikow, MD, MPH | Health-related Quality of life, Direct Measurements, Indirect Measurements, Choice of measurement | |
00:32:31 |
Lesson 22: Comparative Effectiveness Research in Health Policy Formulation | General | Joy Melnikow, MD, MPH | International Perspectives of CER, Review U.S. Examples, Identify QALY's, ICER's and Concerns with those measures. | |
01:27:12 |
Causality, Effect Identification & Statistical Inference | Observational | J. Michael Oakes, PhD | Causation and Counterfactuals, Effect Identification, Statistical Inference | |
01:35:33 |
Research Design in Comparative Effectiveness Research | Experimental | Observational | J. Michael Oakes, PhD | Experimental Designs, Observational Designs |
01:32:27 |
Propensity Score Theory | Observational | J. Michael Oakes, PhD | Confounding, Multiple Regression, Propensity Score Methods | |
01:24:24 |
Propensity Score Application | Observational | General | J. Michael Oakes, PhD | ATE, ACE, ATT, TOT, A Typical Analysis, Propensity Score Methods, Issues and Assumptions |
01:25:03 |
Instrumental Variable Methods | Observational | General | J. Michael Oakes, PhD | The Problem, IV Model Intuition, Examples, LATE, Worked Example |
00:51:49 |
Introduction to Economic Evaluation | Decision | Sean D. Sullivan, BscPharm, Msc, PhD | Cost and Outcomes Evaluation, Important Types of Economic Analysis, Nature of Economic Assessments, Outcomes for Economic Evaluation, Willingness to Pay for Health Gain | |
01:26:29 |
Decision Modeling | Decision | David L. Veenstra, PhD | Errors in Decision Making, Framing a Study, How to Perform Decision Analysis, Sensitivity Analysis, Model Validation, Software | |
01:14:24 |
Working with Health Care Claims and Complex Survey Data | Observational | General | Brook Martin, PhD. M.P. H | Claims and Complex Survey Data, Sources of Data, Stata commands |
01:14:18 |
Simple Linear Regression | General | Brook Martin, PhD. M.P. H | Framework for choosing an analysis, Introduction to Descriptive Analysis of Continuous Data, Applied Introduction Regression Models | |
01:00:55 |
Survival Analysis | General | Brook Martin, PhD. M.P. H | Descriptive Analysis of Survival Data, Cox-Proportional Hazard Regression Models | |
00:59:08 |
An Introduction to Systematic Reviews | Evidence | General | Susan L. Norris, MD, MPH, MS | Forming an Answerable Question, Searching for Evidence, Risk of Bias in Primary Studies, Evidence Thesis, Strength of Evidence |
00:58:32 |
An Introduction to Meta-Analysis | General | Evidence | Susan L. Norris, MD, MPH, MS | Models for Combining Data, Assessing and Exploring Heterogeneity, Publication Bias, Reading and Interpreting Meta-Analyses |
01:06:34 |
Translating CER Evidence into Practice, Policy, and Public Health | Evidence | General | Henry Lee, MD, Ralph Gonzales, MD, MSPH | Conceptual Framework for Translation, Making the Case for Translation, Selecting Evidence, Measuring Quality and its Determinants, Quality Gap, Outcome Gap, Making Change Happen |
00:58:47 |
Translational Toolbox | Decision | General | Ralph Gonzales, MD, MSPH, Henry Lee, MD | Classifying Tools, Exemplars: Patients (Decision Support) Clinicians (Practice Guidelines) and Community (CBPR) |
01:04:26 |
Pragmatic Clinical Trials 1 | Experimental | Christopher Granger, M.D., John P. Vavalle, MD | Theraputic Principals, Understanding Treatment Effects, Current State of Clinical Trials, Randomized Studies RCT, Registry Studies, Barriers to RCT | |
00:44:54 |
Pragmatic Clinical Trials 2 | Experimental | Christopher Granger, M.D., John P. Vavalle, MD | Importance of Large Clinical Trials, Need for Evidence From Randomized Studies, Limitations with Current Large Clinical Trials, Possible Solutions to Current Limitations and the Role for Pragmatic Clinical Trials, Opportunities for Improving Efficiencies in Clinical Trials, Doing More With Less, The Realities of the Current Funding Structure, Examples of Successful Pragmatic Trials | |
00:26:55 |
Introduction to CER Using Observational Data | General | Paul L. Hebert, PhD | Treatments Considered in CER, Outcomes Assesment in CER, Challenges for CER for Patients with Multiple Chronic Conditions, Challenge of CER using CMS Data, Illustration of a CER Study. | |
00:25:13 |
Rubin's Potential Outcome Framework | Experimental | Observational | Paul L. Hebert, PhD | Randomized Controlled Trials, OLS Regression, Propensity Matching, Instrumental Variables |
00:33:49 |
Can Quasi-Experiments Yield Causal Inferences | Experimental | Matthew L. Maciejewski, PhD | Reasons RCT is Gold Standard, Context for Perceived Inferiority of Quasi-Experiments, Differences in Samples for RCT's and Quasi-Experiments, Re-appraising the Value of Quasi-Experiments, Propensity Score Modeling, Conditions Under Which Quasi-Experiments Match RCT Results, | |
00:45:04 |
Study Designs Appropriate for Comparative Effectiveness Research | Experimental | General | Matthew L. Maciejewski, PhD | Incident vs. Prevalent User Cohorts, Counterfactuals, Understand Definition of and Threats to Internal Validity and External Validity, Appreciate Most Rigorous Quasi-Experimental Study Designs |
00:19:07 |
Defining the Treatment | General | Paul L. Hebert, PhD | Challenges in Defining the Treatment, where to find data on treatments in observational data, challenges in identifying the treatment | |
00:16:53 |
Does X Really Cause Y? | Observational | Matthew L. Maciejewski, PhD | Can true causal mechanisms ever be established, Treatment vs. Selection | |
00:42:43 |
Risk Adjustment | Observational | General | Paul L. Hebert, PhD | Measuring Covariates, Demographis, Socio-Demographics, Comorbidity, Severity |
01:07:32 |
Propensity Score Analysis for CER | Observational | Matthew L. Maciejewski, PhD | Illustrate challenge of causal inference in quasi-experiments, outline general principals and steps in propensity score modeling, Discuss trade offs in different approaches, Illustrate propensity score excecution with worked example, Interpretation with results differ by method | |
01:18:42 |
Missing Data | General | Paul L. Hebert, PhD | Types of missing data, missing data mechanisms, solutions to the missing data problem, paper by engels | |
01:08:06 |
Putting It All Together in a CER Analysis | Observational | General | Paul L. Hebert, PhD | Illustrate Study Design, outcomes, covariates and methods principles in CER using Medicare Data, Set Up Problem, Present Preliminary results |
00:41:48 |
Instrumental Variable Exercise | Observational | Paul L. Hebert, PhD | Creating a Geographic Based Instrument, Testing the correlation with treatment, test the correlation with observed confounders,Getting the IV estimate. | |
00:40:26 |
Unobserved Confounding Part 1 | Observational | Paul L. Hebert, PhD | Causes of selection bias in observational CER, Methods for dealing with selction bias | |
00:44:36 |
Unobserved Confounding Part 2 | Observational | Paul L. Hebert, PhD | Causes of selection bias in observational CER, Methods for dealing with selction bias | |
00:50:57 |
Unobserved Confounding Part 3 | Observational | Paul L. Hebert, PhD | Causes of selection bias in observational CER, Methods for dealing with selction bias | |
00:51:25 |
Unobserved Confounding Part 4 | Observational | sean D. Sullivan, BscPharm, Msc, PhD | Causes of selection bias in observational CER, Methods for dealing with selction bias | |
01:25:29 |
Comparative Effectiveness Research: Recent History and Role in Healthcare Reform | General | Harry P. Selker, MD, MSPH | What is CER, What does it do, Making CER Happen, CER roles for CTSA's, Integration of CER into routine clinical care. | |
01:16:58 |
A Review of Evidence Based Medicine and a Framework for Understanding the CER Agenda | Evidence | Stakeholder | Thomas W. Concannon, PhD | Principles of EBM, How CER extends principles of EBM, Describe a framework for understanding the CER agenda, Identify the stakeholders of CER |
01:37:02 |
Comparative Effectiveness Trials | Experimental | General | David M. Kent, MD, Msc | Need for Comparative Effectiveness Trials, differences between pragmatic/effectiveness trials and explanatory efficacy trials, strengths and limitations of pragmatic vs. explanatory designs, state the strengths and limitations of various types of outcome measures including surrogates clinical outcomes |
01:31:06 |
Personalized Medicine, Heterogeneity of Treatment Effect, and Implications for Comparative Effectiveness | Experimental | General | David M. Kent, MD, Msc | Identify the limitations of applying the overall results of clinical trials to individual patients, Discuss how summary results of individual trials might not even reflect the benefits of typical patients in the trial, Explain how subgroup analyses are prone both to false-positive and false negative results, Illustrate approaches that might lead to more credible and actionable subgroup results, Express why Multidimensional risk models may have advantages over conventional “one-variable-at-a-time” subgroup analysis, Determine some of the limitations of using genetic information as a basis for exploring heterogeneity of treatment effect |
01:58:29 |
Observational Methods in Comparative Effectiveness Research | Observational | Experimental | Peter K. Lindenauer, MD, Msc | Review the limitations of RCTs and the settings in which observational CER may be helpful to clinicians and policymakers, Explain the methodological challenges in conducting observational CER using existing/secondary sources of data, Describe model-based and other approaches to reduce the effects of confounding in observational CER |
01:52:24 |
Systematic Review | Evidence | Joseph Lau, MD | List the reasons for conducting systematic reviews, Appreciate the role of systematic review in CER, Describe the components of a systematic review, State the role of analytic frameworks in systematic review and the approach to formulate answerable systematic review questions, Identify the users and producers of systematic reviews | |
01:38:01 |
Decision Analysis | Decision | General | Stephen G. Pauker, MD, MACP | Show how the probability of a diagnosis is affected by a test result, sensitivity, and specificity, Describe how evidence can be integrated using decision trees, Illustrate the concept of threshold probabilities and their implications, Discuss how sensitivity analyses are performed and what they mean, Explain how patient preferences can be integrated into patient-centered choices using decision analysis. |
01:41:18 |
Simulation Models and Value of Information Analysis | Decision | Joshua T. Cohen, PhD | Explain the use of simulation models to characterize disease history and outcomes, Discuss quantifying simulation model uncertainty, Define what “value of information” means | |
01:34:04 |
Community Engagement and Input inot Comparative Effectiveness Research | Stakeholder | Laurel K. Leslie, MD, MPH | Review where we have been in the course and where we are going, Define communities and community engagement within the context of CER, Delineate key points where community engagement may enhance CER, Identify examples of research strategies and methodologies employed to engage communities | |
01:45:07 |
Clinical Practice Guidelines | General | Evidence | Katrin Uhlig, MD, MS | Growth of Clinical Practice Guidlines, Process of CPG development, and Challenges in evidence synthesis and guideline development |
01:08:25 |
Predictive Instruments as Decision Support for Diagnostic and Theraputic Decisions: Development and Testing in Clinical Effectiveness Trials | General | Experimental | Harry P. Selker, MD, MSPH | Explain the use of predictive instruments are incorporated into the use of medications and other treatments, Discuss the use of comparative effectiveness trials for comparing strategies of care, Discuss the possible use of predictive instruments for efficient and ethical conduct of clinical effectiveness trials |
01:46:19 |
Drug Development in the CER Era | Decision | General | Kenneth I. Kaitin, PhD | Asses the economic, regulatory, and political pressures affecting pharmaceutical and biopharmaceutical developers today, Discuss current drug development metrics, including the time, cost, and risk of development, Examine how companies, in response to competitive and economic pressures, are adopting new strategies and practices to improve R&D performance |
01:22:03 |
Using Comparative Effectiveness Research to Reach Employers and Employees | Decision | Evidence | Debra J. Lerner, MS, PhD | Identify current and projected healthcare and cost issues facing employers and employees, List workplace policies, practices, and programs that are being used to address health and cost trends, Describe the evidence base for workplace health programs, Identify methodological problems and solutions in developing the evidence base and the role of comparative effectiveness studies, Identify issues related to the dissemination and implementation of evidence from comparative effectiveness studies |
01:39:52 |
Economic and Policy Implications of CER | Decision | Observational | Christopher P. Tompkins, PhD | Discuss how performance assesment and financial incentive models have been used to impact transformational changes in healthcare policy, Using specific examples, explain how comparative effectiveness research and outcomes measurement impact value-priced purchasing and the cost of healthcare delivery, Describe how a multidimensional framework for measuring outcomes of care and efficiency assist policymakers to make informed decisions that improve healthcare, Discuss opportunities and roles for use of insurance data to improve healthcare, Identify the differences between chart based clinical process-of-care measures vs clincal outcomes measurement and comparative effectiveness research in healthcare delivery systems |
01:50:38 |
Future Directions in CER | General | Thomas W. Concannon, PhD | WHY CER, Describe existing CER programs, Describe the IOM priority-setting process and identify key features of the current list of 100 priorities, Describe the role of PCORI in setting the future course for a national CER agenda |