6  Further Reading

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This tutorial should at best be seen as a brief introduction to RCT evaluations, and how they can be implemented using R. Please note that we only covered parallel two-arm randomized controlled trials here, which can be regarded as the most basic RCT design.

In practice, many other “flavors” of RCTs are used to do clinical research, including cluster-randomized, cross-over, or stepped-wedge trials, all of which require their own analytic approach. An accessible introduction to these advanced trial designs and their analysis is provided in Twisk (2021), which also provides hands-on examples using STATA.

To learn more about multiple imputation and how to implement it using R, we refer the reader to the book Flexible Imputation of Missing Data written by Stef van Buuren, the maintainer of the mice package (van Buuren 2018). This book is openly available online. Another accessible overview of applied missing data handling can be found in Enders (2022).

A recommended resource to learn more about R and the tidyverse is R for Data Science by Wickham and Grolemund (2016), which is also openly accessible (see here).

Enders, Craig K. 2022. Applied Missing Data Analysis. 2nd ed. New York City: Guilford Publications.

Twisk, Jos W R. 2021. Analysis of Data from Randomized Controlled Trials: A Practical Guide. 1st ed. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-81865-4.

van Buuren, Stef. 2018. Flexible Imputation of Missing Data. 2nd ed. CRC Press. https://doi.org/10.1201/9780429492259.

Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. O’Reilly Media, Inc.