Observational Health Data Sciences and Informatics

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====== OHDSI Best Practices for Estimating Population-Level Effects ======

:!: //This document is under development. Changes can be proposed and discussed via the [[http://forums.ohdsi.org/t/population-level-estimation-workgroup-discussing-best-practices|OHDSI Forum]] and in the [[projects:workgroups:est-methods|Population-Level Estimation Workgroup]] meetings.//

===== 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 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) =====
  * **Write a full protocol**, and make it public prior to running the study. This should include
    * Research question + hypotheses to be tested
    * Which method(s), data, cohort definitions. 
    * What is the primary analyses and what are sensitivity analyses?
    * Quality control
    * Amendments and Updates

  * **Validate** all code used to produce estimates. The purpose of validation is to ensure the code is doing what we require it to do. Possible options are:
    * Unit testing
    * Simulation
    * Double coding
    * Code review

  * Include **negative controls** (exposure-outcome pairs where we believe there is no effect) 

  * Produce **calibrated p-values**

  * Make all analysis code available as **open source** so others can easily replicate your study

===== Best practices (new-user cohort design) =====


  * Use **propensity scores** (PS)

  * Build PS model using **regularized regression** and a **large set of candidate covariates** (as implemented in the CohortMethod package)

  * Use either **variable-ratio matching** or **stratification** on the PS

  * **Compute covariate balance** after matching for all covariates, and terminate study if a covariate has standardized difference > 0.1


===== 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)

===== Best practices ((nested) case-control) =====

  * **Don't** do a case-control study