Transparency: others should be able to reproduce your study in every detail using the information you provide.
Prespecify what you're going to predict and how: this will avoid fishing expeditions, p-value hacking. Run your analysis only once against the test set.
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.)
Best practices (generic)
projects/workgroups/patient-level_prediction/best-practice.1462299909.txt.gz · Last modified: 2016/05/03 18:25 by prijnbeek