Understanding data and statistics in the medical literature (The Course)
Understanding data and statistics in the medical literature
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.
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.
- Data Collection - Introduction
- Data Collection - Population and Setting
- Data Collection - Measurement Instruments
- Data Collection - Participant Characteristics
- Data Collection - Preregistration
- Study Design - Randomized Controlled Trials
- Study Design - Observational Study
- Study Design - Meta-analysis
- Reading a flowchart
- Reading Table 1
- Types of Scientific Questions - Descriptive and Exploratory
- Types of Scientific Questions - Inferential and Predictive
- Types of Scientific Questions - Causal and Mechanistic
- Types of Scientific Questions - Common Misunderstandings
- Measures of Association - Estimation
- Measures of Association - Uncertainty
- Measures of Association - Hypothesis Testing
- Measures of Association - Power and Sample Size
- Statistical Graphics - Introduction
- Statistical Graphics - Histograms
- Statistical Graphics - Boxplots
- Statistical Graphics - Dynamine Plots
- Statistical Graphics - Scatterplot
- Statistical Graphics - Forest plot
- Statistical Models - Introduction
- Statistical Models - Linear Outcomes
- Statistical Models - Binary Outcomes
- Statistical Models - Time to Event Outcomes
- Classification Methods - The Data
- Classification Methods - The Model
- Classification Methods - The Application
- Classification Evaluation
- Reproducibility and Replication - Defining Terms
- Reproducibility and Replication - Tools of the Trade
- Reproducibility and Replication - Human Data Interaction
- Final Assessment
Jeff is 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 has 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 Mortimer Spiegelman Award recipient.
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.
This course has a private forum for learners who are taking this course.
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