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Other Outcome Variables

In: Basic Principles of Applied Medical Statistics

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  • Jos W. R. Twisk

    (Amsterdam UMC, Department of Epidemiology and Data Science)

Abstract

In the first chapters of this book, the analysis of a continuous outcome variable, a dichotomous outcome variable and survival data was discussed. This chapter dealt with the analysis of other outcome variables, such as a categorical outcome variable, a count outcome variable, an outcome variable with a floor or ceiling effect and correlated outcome variables. It is shown that for the analysis of a categorical outcome variable, multinomial logistic regression analysis can be used, while for a count outcome variable, either Poisson regression analysis or negative binomial regression analysis can be used. For the analysis of an outcome variable with a floor or ceiling effect, it is possible to use tobit regression analysis. Regarding the analysis of correlated outcome variables, mixed model analysis and generalised estimating equations are briefly introduced. One of the important conclusions of this chapter is that all regression analyses are basically the same regarding the interpretation of the regression coefficients, the evaluation of linearity of the relationship with a continuous independent variable, the adjustment for confounding and the investigation of effect modification.

Suggested Citation

  • Jos W. R. Twisk, 2025. "Other Outcome Variables," Springer Books, in: Basic Principles of Applied Medical Statistics, chapter 0, pages 209-226, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-86278-6_9
    DOI: 10.1007/978-3-031-86278-6_9
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