Understanding data and statistics in the medical literature (The Course)
Course Info
This course includes 2 attempts.
Whether you are a medical student reading their first journal article or a healthcare professional trying to use the latest research to improve patient care, understanding data and statistics has never been more fundamental for extracting accurate information from the medical literature. This is a high-level, introduction to the concepts you need to know. It is targeted at busy students and professionals, and therefore efficient, with short lessons that can be completed in 5 to 10 minutes, with the whole course-time totaling around 4 hours. We leave out the mathematical detail and focus on the conceptual ideas. Anyone can pick this course up and gain a better understanding of the medical literature.
Learning objectives
After taking this course you will be able to:
- Identify potential statistical problems and biases in the medical literature
- Interpret commonly seen statistical methods and graphics in the medical literature
- Determine whether a medical study was conducted in a statistically rigorous manner
How you will be graded
The course has a series of short quizzes, one for each section, and a final assessment. You will get two attempts at each assessment and your best score for each one will count toward your final score. If you receive more than 70% of the points across all assessments you will pass. If you receive more than 90% of the points across all assessments you will pass with honors. You get two attempts at the class with each class purchase.
Course Material
- Introduction
- Data Collection - Introduction
- Data Collection - Population and Setting
- Data Collection - Population and Setting Quiz2 attempts allowed
- Data Collection - Measurement Instruments
- Data Collection - Measurement Instruments Quiz2 attempts allowed
- Data Collection - Participant Characteristics
- Data Collection - Participant Characteristics Quiz2 attempts allowed
- Data Collection - Preregistration
- Data Collection - Preregistration Quiz2 attempts allowed
- Study Design - Randomized Controlled Trials
- Study Design - Randomized Controlled Trials Quiz2 attempts allowed
- Study Design - Observational Study
- Study Design - Observational Studies Quiz2 attempts allowed
- Study Design - Meta-analysis
- Study Design - Meta Analysis Quiz2 attempts allowed
- Reading a flowchart
- Reading a Flowchart Quiz2 attempts allowed
- Reading Table 1
- Reading a Table 1 Quiz2 attempts allowed
- Types of Scientific Questions - Descriptive and Exploratory
- Types of Scientific Questions - Descriptive and Exploratory Quiz2 attempts allowed
- Types of Scientific Questions - Inferential and Predictive
- Types of Scientific Questions - Inferential and Predictive Quiz2 attempts allowed
- Types of Scientific Questions - Causal and Mechanistic
- Types of Scientific Questions - Causal and Mechanistic Quiz2 attempts allowed
- Types of Scientific Questions - Common Misunderstandings
- Types of Scientific Questions - Common Misunderstandings Quiz2 attempts allowed
- Measures of Association - Estimation
- Measures of Association - Estimation Quiz2 attempts allowed
- Measures of Association - Uncertainty
- Measures of Association - Uncertainty Quiz2 attempts allowed
- Measures of Association - Hypothesis Testing
- Measures of Association - Hypothesis Testing Quiz2 attempts allowed
- Measures of Association - Power and Sample Size
- Measures of Association - Power and Sample Size Quiz2 attempts allowed
- Statistical Graphics - Introduction
- Statistical Graphics - Histograms
- Statistical Graphics - Histograms Quiz2 attempts allowed
- Statistical Graphics - Boxplots
- Statistical Graphics - Boxplots Quiz2 attempts allowed
- Statistical Graphics - Dynamine Plots
- Statistical Graphics - Dynamite Plots Quiz2 attempts allowed
- Statistical Graphics - Scatterplot
- Statistical Graphics - Scatterplots Quiz2 attempts allowed
- Statistical Graphics - Forest plot
- Statistical Graphics - Forest Plots Quiz2 attempts allowed
- Statistical Models - Introduction
- Statistical Models - Linear Outcomes
- Statistical Models - Linear Outcome Quiz2 attempts allowed
- Statistical Models - Binary Outcomes
- Statistical Models - Binary Outcome Quiz2 attempts allowed
- Statistical Models - Time to Event Outcomes
- Statistical Models - Time to Event Outcome Quiz2 attempts allowed
- Classification Methods - The Data
- Classification Methods - The Data Quiz2 attempts allowed
- Classification Methods - The Model
- Classification Methods - The Model Quiz2 attempts allowed
- Classification Methods - The Application
- Classification Methods - The Application Quiz2 attempts allowed
- Classification Evaluation
- Classification Evaluation Quiz2 attempts allowed
- Reproducibility and Replication - Defining Terms
- Reproducibility and Replication - Defining Terms Quiz2 attempts allowed
- Reproducibility and Replication - Tools of the Trade
- Reproducibility and Replication - Tools of the Trade Quiz2 attempts allowed
- Reproducibility and Replication - Human Data Interaction
- Reproducibility and Replication - Human Data Interaction Quiz2 attempts allowed
- Final Assessment
- Final Assessment2 attempts allowed
Instructors
Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.
Lucy is an assistant professor at Wake Forest University. She is passionate about data science, causal inference, and human-data interaction. Lucy writes for the Live Free or Dichotomize Blog and can be found on Twitter @LucyStats.
Elizabeth Matsui is a Professor of Population Health and Pediatrics at Dell Medical School at UT Austin and an Adjunct Professor of Pediatrics at Johns Hopkins University. She is also a practicing pediatric allergist/immunologist and epidemiologist and directs a research program focused on environmental exposures and lung health. Elizabeth can be found on Twitter @elizabethmatsui.
Community
This course has a private forum for learners who are taking this course.
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