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How Can We Mitigate Against Noncausal Associations in Design and Analysis? 

How Can We Mitigate Against Noncausal Associations in Design and Analysis?
How Can We Mitigate Against Noncausal Associations in Design and Analysis?

Katherine M. Keyes

and Sandro Galea

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date: 20 October 2020

Other chapters in this book explicated how one thinks of causes in epidemiology, how comparability between exposed and unexposed is an important component in the ability to make strong conclusions from data, and the central ways in which non-comparability arises in conducting epidemiologic studies. This chapter details three foundational ways in which comparability is achieved in epidemiologic studies: randomize individuals to receive the exposure or not, match individuals in the study to each other on variables that are potential causes of non-comparability, and stratify data to determine whether there is an association between exposure and outcome holding constant variables contributing to non-comparability. The chapter provides a comprehensive overview of randomization and randomized controlled trials in epidemiology, matching and methods for analyzing matched data, and quantitative approaches to stratification for the assessment and removal of non-comparability after the data has been collected.

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