Abstract
The outcome of clinical research is, generally, affected by many more factors than a single one, and multiple regression assumes, that these factors act independently of one another, but why should they not affect one another. This chapter is to assess how partial correlation can be used to remove interaction effects from linear data.
Without the partial correlation approach the conclusion from studies might have been: no definitive conclusion about the effects of factors is possible, because of a significant interaction between such factors. The partial correlation analysis allows to conclude that multiple interacting factors have a significant linear relationship with a single outcome variable.
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Cleophas, T.J., Zwinderman, A.H. (2018). Partial Correlations. In: Regression Analysis in Medical Research. Springer, Cham. https://doi.org/10.1007/978-3-319-71937-5_24
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DOI: https://doi.org/10.1007/978-3-319-71937-5_24
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