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As a function of the distribution of the response variables, we used different regression models: in the case of prescribing costs, we built a normal regression model (Proc MIXED, RMLE), while for the visits and referrals we used negative binomial regression models (Proc GLIMMIX, LAPLACE).
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We built a logistic regression model using the same features in Pan et al. (see Methods).
We built a hierarchical regression model using demographic variables as the first block.
We built a multiple regression model that included a term for the detection-mode-by-tumour-size interaction.
Next we built a multivariate logistic regression model using manual forward selection.
We built a weighted Cox regression including inferred haplogroup as an explicative variable.
Thus, we built a regression model to predict the PROCAM score based on routine data.
For completeness, we also built a forth logistic regression model for the whole biopsy population (Additional file 1).
We built a multivariate Cox-regression model to analyze the prognostic impact of SBP.
build a univariate regression for each independent variable, build a multivariate regression model including all variables with p < 0.05.
build a multivariate regression model including all variables with p < 0.05. .
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com