Observational Health Data Sciences and Informatics

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====== OHDSI Best Practices for Patient Level Prediction ======

:!: //This document is under development. Changes can be proposed and discussed via the  [[projects:workgroups:patient-level_prediction|Patient-Level Prediction Workgroup]] meetings.//

===== General principles =====


  * **Transparency**: others should be able to reproduce your study in every detail using the information you provide. Make sure all analysis code is available as open source.
  * **Prespecify** what you're going to predict and how. This will avoid fishing expeditions, p-value hacking. 
  * **Code validation**: it is important to add unit tests, code review, or double coding steps to validate the developed code base. We recommend to test the code on benchmark datasets.
===== Best practices =====

**Data characterisation and cleaning**: Before modelling it is important to characterize the cohorts, for example by looking at the prevalence of certain covariates. Tools are being developed in the community to facilitate this. A data cleaning step is recommended, e.g. remove outliers in lab values.

**Dealing with missing values **: A best practice still needs to established.

**Feature construction and selection**: Both feature construction and selection should be completely transparent using a standardised approach to be able repeat the modelling but also to enable application of the model on unseen data.

**Inclusion and exclusion criteria** should be made explicit. It is recommended to do sensitivity analyses on the choices made. Visualisation tools could help and this will be further explored in the WG. 

**Model development** is done using a split-sample approach. The percentage used for training could depend on the number of cases, but as a rule of thumb 80/20 split is recommended. Hyper-parameter training should only be done on the training set. 

**Internal validation** is done only once on the holdout set. The following performance measures should be calculated:   
  . Overall performance: Brier score (unscaled/scaled)
  . Discrimination: Area under the ROC curve (AUC)
  . Calibration: Intercept + Gradient of the line fit on the observed vs predicted probabilities
We recommend box plots of the predicted probabilities for the outcome vs non-outcome people, the ROC plot and a scatter plot of the observed vs predicted probabilities with the line fit to that data and the line x=y added.
Observational Health Data Sciences and Informatics

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