Selected article for: "model random intercept and random intercept"
Author: Carly Adams; David Young; Paul A Gastañaduy; Prabasaj Paul; Zach Marsh; Aron J Hall; Benjamin A Lopman
Title: Quantifying the roles of vomiting, diarrhea, and residents vs. staff in norovirus transmission in U.S. nursing home outbreaks
  • Document date: 2019_7_18
    • ID: ghihvjj3_16
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/707356 doi: bioRxiv preprint 170 resident vs. staff, diarrhea (yes or no), and vomiting (yes or no) were rarely missing (1%, 1%, 171 and 0%, respectively) and were considered explanatory variables in our model. To account for 172 clustering induced by correlation of R Ei 's within the six outbreaks, outbreak number was 173 included.....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/707356 doi: bioRxiv preprint 170 resident vs. staff, diarrhea (yes or no), and vomiting (yes or no) were rarely missing (1%, 1%, 171 and 0%, respectively) and were considered explanatory variables in our model. To account for 172 clustering induced by correlation of R Ei 's within the six outbreaks, outbreak number was 173 included in the model as a random intercept. The full model, with log R Ei as the outcome, 174 included the following explanatory variables: diarrhea, vomiting, resident. The model was 175 assessed for collinearity and no issues were found. We considered including 'time' in the model 176 and adjusting for it as a potential confounder, as R Ei inevitably declines over time. However, we 177 determined that time cannot be a confounder, since it cannot affect diarrhea, vomiting, or 178 resident vs. staff, our explanatory variables of interest. The final model is shown below:

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