Abstract
In the examples of the first chapters, the outcome variable of the RCT was continuous. In this chapter, the analysis of data from RCTs with a dichotomous outcome is discussed. In general, the same principles can be used for RCTs with a dichotomous outcome than for RCTs with a continuous outcome. The difference is that for dichotomous outcomes, logistic models must be used instead of linear models. However, when there is more than one follow-up measurement, instead of a logistic mixed model analysis, it is advised to use logistic generalized estimating equations to deal with the dependency of the observations and to estimate an appropriate treatment effect. It is also argued that an adjustment for the baseline value is often not necessary, because at baseline mostly all subjects have the same value (e.g., they all have a certain disease). Finally, the problem of non-collapsibility is discussed.
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Twisk, J.W.R. (2021). Dichotomous Outcomes. In: Analysis of Data from Randomized Controlled Trials. Springer, Cham. https://doi.org/10.1007/978-3-030-81865-4_8
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