andrigerber

### Model selection
Vorlagestudie:
  * Variables with P < 0.20 from the univariate analyses were selected as candidates for inclusion in the multivariate model and were
retained in the final model if they were significant at P < 0.10 or if they altered any of the other estimates (i.e., odds ratio) by more than 10% 

  * Or from my proposal: Thereby, the predictors are selected based on previous research (Field, 2012). From there, a forward modeling strategy will be used to identify variables with a p-value of under 0,25 from the univariable t-test and included in the multivariable model. They will be included in the preliminary main effects model if they achieve the threshold (p < 0,05) in the partial likelihood ratio test of the nested model or if they change any variables coefficient (𝛥𝛽̂ > 20%) substantially (Behnke, 2014 pp. 105-111; Hosmer, 2013, pp. 90-94).

  * Workflow: Nur variablen mit p<0.25 inkludieren aus "columne K des unadjusted model/univariate model", anschliessend "(Rao-Scott+F) LRT" durchfü

andrigerber

```{r}
###  tableone export helpers
###   Turn tableone output matrix into tidyverse data_frame
tableone_mat_to_data_frame <- function(mat) {
    mat %>%
        as.data.frame() %>%
        tibble::rownames_to_column(var = "Variable") %>%
        tibble::as_data_frame()
}

andrigerber

---
title: "Codebook"
output:
  html_document:
    toc: true
    toc_depth: 4
    toc_float: true
    code_folding: 'hide'
    self_contained: true
  pdf_document:

andrigerber

install.packages('tinytex')
tinytex::install_tinytex()
# to uninstall TinyTeX, run tinytex::uninstall_tinytex() 

- upload RStudio over RStudio site
- or download MikTex for Tex and TexMaker