From the course: Designing Big Data Healthcare Studies, Part One

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Operationalizing the exposure, outcome, and confounders

Operationalizing the exposure, outcome, and confounders - R Tutorial

From the course: Designing Big Data Healthcare Studies, Part One

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Operationalizing the exposure, outcome, and confounders

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- [Instructor] Hello there. In this video, we will continue our discussion about operationalizing, and this time, we will focus on the exposure, outcome, and confounders. Let's start with thinking about the exposure. This is your hypothesized cause, so you really need to make sure whatever variable you operationalize represents an accurate and honest measurement of your exposure. It might be accurate, but does it really represent the exposure? I want to remind you that the exposure represents an independent variable, or variables if you have multiple exposure levels, that remain in every regression model you run later, when you are answering your hypothesis. In the end, when you are done fitting the model, the exposure variable slopes will be the ones you interpret to see if they are statistically significant, yes or no, and therefore, if your hypothesis is supported, yes or no. So, it's helpful to imagine your final…

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