OHDSI Best Practices for Estimating Population-Level Effects
This document is under development. Changes can be proposed and discussed via the OHDSI Forum.
General principles
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 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)
Make all analysis code available as open source
Include negative controls (exposure-outcome pairs where we believe there is no effect)
Produce calibrated p-values
Best practices (new-user cohort design)
Best practices (self-controlled case series)
Include a risk window just prior to start of exposure to detect time-varying confounding (e.g. contra-indications, protopathic bias)
development/best_practices_estimation.1459324094.txt.gz · Last modified: 2016/03/30 07:48 by schuemie