projects:workgroups:patient-level_prediction:best-practice
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projects:workgroups:patient-level_prediction:best-practice [2016/05/03 18:59] prijnbeek [Best practices] |
projects:workgroups:patient-level_prediction:best-practice [2016/05/04 08:23] jreps [Best practices] |
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| ===== Best practices ===== | ===== 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. | + | **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 recommend, e.g. remove outliers in lab values. |
| **Dealing with missing values **: A best practice still needs to established. | **Dealing with missing values **: A best practice still needs to established. | ||
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| **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. | **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**: All inclusion and exclusion criteria should be made explicit. It is recommended to do sensitivity analyses. Visualisation tools could help here, this will be further explored. | + | **Inclusion and exclusion criteria** should be made explicit. It is recommended to do sensitivity analyses not he 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. | **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. | ||
| - | **Model validation** is done only once on the holdout set. The following performance measures should be added: To be added! | + | **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. | ||
projects/workgroups/patient-level_prediction/best-practice.txt · Last modified: 2016/05/04 15:43 by prijnbeek
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