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CERTaIN

Comparative Effectiveness Research Training and Instruction (CERTaIN),

While randomized controlled trials are considered to be the "gold standard" in health research, they cannot always be performed, for ethical or practical reasons. Observational studies gather information from data that has already been collected, or by observing and measuring patients' changes in health status and their response to interventions outside of a clinical trial. In this course, you will learn to identify the characteristics of observational studies, to interpret the results of observational studies, and to describe the use of health registries in comparative effectiveness research (CER).

This course includes the following 11 lectures:

Overview of Using Observational Data in Comparative Effectiveness Research (CER)

Cancer Registries and Data Linkage

SEER-Medicare and Other Data Sources

Overview of Analytic Methods I

Overview of Analytic Methods II

Longitudinal Data Analysis

Advanced Methods in CER I

Advanced Methods in CER II

Survival Analysis

Analysis of Medical Cost Data in Observational Studies

Healthcare Policy Research

This course is intended for anyone interested in comparative effectiveness research (CER) and patient-centered outcomes research (PCOR) methods.

This course is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality.

What you'll learn

  • Define several types of observational studies and describe their characteristics
  • Interpret observational study results
  • Describe how health registries can be used for CER
  • When to use observational studies in CER
  • Observational study biases
  • Observational study data sources
  • Data linkage definition
  • Why cancer registries link their data
  • Basics of data linkage methods
  • Uses of cancer registry data
  • Correlation definition
  • Statistical methods for continuous outcome variables
  • Simple linear regression and multiple linear regression
  • Statistical methods for categorical data
  • Mantel Haenszel Method
  • Logistic regression
  • Longitudinal data situations and the problems of repeated measures
  • Common approaches to measure change over time
  • Understanding basic results obtained from longitudinal analysis of linear and logistic regression
  • Limitations of randomized clinical trials and advantages of observational studies
  • Propensity score definition and estimation
  • Checking the proper use of propensity scores
  • Endogeneity bias definition
  • Conditions and properties of instrumental variable estimation and models
  • Censoring and person-time
  • Application of life tables
  • Kaplan Meier estimator
  • Using the log-rank test to compare survival curves
  • Hazard function definition and hazard ratio computation
  • Applying the Cox proportional hazards model
  • Importance of medical cost analysis
  • Basic elements of medical cost data
  • Types of medical cost studies and their analytical methods
  • Objectives and stages of the policy-making process
  • Comparison and contrast of CER and policy research
  • Policy changes and evaluation results of a case study

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Length 3 weeks
Effort 4 - 5 hours per week
Starts On Demand (Start anytime)
Cost $249
From MDAndersonX, The University of Texas MD Anderson Cancer Center in Houston via edX
Instructors Maria E. Suarez-Almazor, MD, PhD, Sharon H. Giordano, MD, MPH, FASCO
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Business
Tags Data Analysis & Statistics Economics & Finance Health & Safety

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Rating Not enough ratings
Length 3 weeks
Effort 4 - 5 hours per week
Starts On Demand (Start anytime)
Cost $249
From MDAndersonX, The University of Texas MD Anderson Cancer Center in Houston via edX
Instructors Maria E. Suarez-Almazor, MD, PhD, Sharon H. Giordano, MD, MPH, FASCO
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Business
Tags Data Analysis & Statistics Economics & Finance Health & Safety

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