Transparency: others should be able to reproduce your study in every detail using the information you provide.
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.
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.1461088223.txt.gz · Last modified: 2016/04/19 17:50 by prijnbeek