“Flow” in bllflow refers to the process of using the Model Specification Worksheet to perform rountine data cleaning and transformation, performance reporting, and model deployment. Go to Workflow to see bllflow’s seven steps to analysing observational data. You can pick and choose to use any steps that fit your own workflow.

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Workflow vignettes

Tne Workflow vignettes use the pbc data available in the suvival package to replicate a survival model for people with primary biliary cirrhosis. What is the pbc data? The name, description and other information is included in the metadata file!

See Example 4 - Helper and utility functions.

Example - Study exclusion criteria

A typical first step when starting a new study is applying inclusion and exclusion criteria to the study data. In our PBC survival model, we will include only participants ages 40 to 70 years.

1) Excluding participatns age < 40 or >70 years using clean.Min() and clean.Max()
## [1] "clean.min.BLLFlow: 418 rows were checked and 69 rows were set to delete. Reason: Rule age min at 40 "
  cleanPbc <- clean.Max(cleanPbc, print = TRUE)
## [1] "clean.max.BLLFlow: 349 rows were checked and 13 rows were set to delete. Reason: Rule age max at 70 "

Within the PBC-variables.csv file there is a column ‘min’ and ‘max’ and a row each variable. The ‘age’ variable has the values for 40 and 70 in the ‘min’ and ‘max’ columns. This example is shown in more detail in the data cleaning and transformation vignette.

Note that executing clean.Max executes min and max criteria for all variables in the pbcModel.