Health care professionals increasingly have to make clinical decisions in aging and diverse populations. Also, they have to deal with rising health care costs, fragmented health care supply and advancing medical technologies and IT systems. These developments go beyond every day practice and will require new skills. In this course we will walk you through key steps in designing a research study, from formulating the research question to common pitfalls you might encounter when interpreting your results.
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We will focus primarily on analytical studies used in etiological research, which aims to investigate the causal relationship between putative risk factors (or determinants) and a given disease or other outcome. However, the principles we will discuss hold true for most research questions, and you will also encounter these study designs in prognostic and diagnostic research settings.
This course is part of a Master's program Population Health Management at Leiden University (currently in development), which includes nine courses on Coursera (including this one). If you are interested in learning more about the Population Health Management approach follow the course "Population Health: Fundamentals of Population Health Management" on Coursera.
What You Will Learn
- You will be able to formulate a good research question
- You will be able to interpret and apply different frequency and effect measures
- You will be able to recognize errors and deal with bias and confounding
- You will be able to describe basic principles of causal inference
Course Syllabus
Week 1
Welcome to Study Design
Welcome to Population Health: Study Design! In this module you will get to know what the scope is of this course and you will learn how to be successful at online studying.
Design
In this module you will start to formulate a research question. You will be introduced to the most important study designs in epidemiology, and work out which study design fits your specific research question.
Week 2
Measures
In this module you will calculate frequency and effect measures, and apply them to your own research questions. In addition, you will practice with constructing a life table and drawing a Kaplan-Meier curve.
Week 3
Confounding and bias
In this module different types of error will be discussed, which can be either random or systematic in nature. Subsequently, you will learn to recognize bias and confounding. You will additionally gain hands-on experience with standardisation.
Week 4
Inference
In this module you will calculate with averages, learn basic principles of causal inference, and be introduced to the concepts of regression to the mean and intention to treat.
