projects:workgroups:patient-level_prediction:best-practice
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projects:workgroups:patient-level_prediction:best-practice [2016/04/19 20:40] prijnbeek |
projects:workgroups:patient-level_prediction:best-practice [2016/05/04 15:43] (current) prijnbeek [Best practices] |
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| - | * **Transparency**: others should be able to reproduce your study in every detail using the information you provide. | + | * **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 estimate and how: this will avoid hidden multiple testing (fishing expeditions, p-value hacking). Run your analysis only once. | + | * **Prespecify** what you're going to predict and how. This will avoid fishing expeditions, p-value hacking. |
| - | * **Validation of your analysis**: you should have evidence that your analysis does what you say it does (showing that statistics that are produced have nominal operating characteristics (e.g. p-value calibration), showing that specific important assumptions are met (e.g. covariate balance), using unit tests to validate pieces of code, etc.) | + | * **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 ===== | ||
| - | ===== Best practices (generic) ===== | + | **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. |
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| + | **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. | ||
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| + | **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. | ||
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| + | **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. | ||
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| + | **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.1461098453.txt.gz · Last modified: 2016/04/19 20:40 by prijnbeek
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