Step 1 – developing the study

• Design the study: target/outcome cohort logic, concept sets for medical definitions, settings for developing new model or validation of adding existing models to framework. Suggestion: look in literature for validated definitions.
• Write a protocol that motivates the study and provides full details (sufficient for people to replicate the study in the future).
• Write an R script to create the json analysis specification file for the study. A useful study repo template can be found here. For a PLP model development study this means creating a modelDesign object. But first we need a target and outcome cohort defitinion. For this example we will use the following prediction problem:

Among patients who have just started on an ACE inhibitor for the first time, who will experience angioedema in the following year?

This is the same problem as example 2 in the vignette Building Predictive Models.

The cohorts to use can be fetched from the OHDSI Demo atlas. The target cohort is defined as patients who have started on an ACE inhibitor for the first time, and the outcome cohort is defined as patients who have experienced angioedema within one year of starting the ACE inhibitor. The target cohort is this one, and the outcome cohort is this one. First thing that needs to be done is fetch those cohorts. This is done by pressing the link for the cohort, going to the Export tab, and within that tab going to the JSON sub-tab. There you will see a box with the JSON defining the cohort. Below that on the left side is a copy to clipboard button. If you copy the JSON from there, you can paste it into an empty file and add the json extension.

library(PatientLevelPrediction)

# Create a model design object
modelDesign <- createModelDesign(
  targetId = 1,
  outcomeId = 2,
  populationSettings = createStudyPopulationSettings(
    requireTimeAtRisk = FALSE,
    riskWindowEnd = 3*365
  ),
  covariateSettings = FeatureExtraction::createCovariateSettings(
    useDemographicsGender = TRUE,
    useDemographicsAge = TRUE,
    useConditionOccurrenceLongTerm = TRUE,
    useDrugEraLongTerm = TRUE,
    useCharlsonIndex = TRUE,
    longTermStartDays = -365,
    endDays = 0
  ),
  preprocessSettings = createPreprocessSettings(), # default settings used
  modelSettings = setLassoLogsticRegression(seed = 42)
  splitSettings = createDefaultSplitSettings(splitSeed = 42)
)

Next we need to create the json analysis specification file using Strategus.

Step 2 – implementing the study part 1

• Get contributors to install the package and dependencies. Ensure the package is installed correctly for each contributor by asking them to run the checkInstall functions (as specified in the InstallationGuide).
• Get contributors to run the createCohort function to inspect the target/outcome definitions. If the definitions are not suitable for a site, go back to step 1 and revise the cohort definitions.

Step 3 – implementing the study part 2 (make sure the package is functioning as planned and the definitions are valid across sites)

• Get contributors to run the main.R with the settings configured to their environment
• Get the contributors to submit the results

Step 4 – Publication

The study creator has the first option to be first author, if he/she does not wish to be first author then he/she can pick the most suitable person from the contributors. All contributors will be listed as authors on the paper. The last author will be the person who lead/managed the study, if this was the first author then the first author can pick the most suitable last author. All authors between the first and last author will be alphabetical by last name.