Observational Health Data Sciences and Informatics

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====== Large-Scale Population-Level Evidence Generation ======

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**Objective:** - Generate evidence for the comparative effectiveness for each pairwise comparison of depression treatments for a set of outcomes of interest..

**Rationale:** In current practice, most comparative effectiveness questions are answered individually in a study per question. This is problematic because the slow pace at which evidence is generated, but also invites reporting and publishing only those studies where the result is ‘statistically significant’, leading to an underestimation of the true number of tests performed when correcting for multiple testing. This process is known as publication bias. Moreover, these studies typically do not include the evidence needed to interpret the study results, such as empirical estimates of residual bias inherent to the study design and data used. 
A solution to these problems is to perform a large set of comparative effectiveness analyses in one study, where each analysis adheres to current best practices. One of these best practices that we’ll follow is to use large scale propensity models to adjust for confounding. Another best practice that this study will follow is that each analysis will include a large set of negative and positive control outcomes (outcomes that are respectively not known or known to be cause by one exposure more than the other). 
In this study we would like to demonstrate the feasibility of generating population-level estimates at scale by focusing on on disease: depression. We perform every possible pairwise comparison between depression treatments for a large set of outcomes of interest. Most of these outcomes are generic safety outcomes, but some outcomes are related more specifically to the effectiveness of antidepressant treatment.


**Project Lead(s):** Martijn Schuemie, Patrick Ryan 

**Coordinating Institution(s):** Janssen R&D

** Additional Participants (currently seeking additional collaborators):** 
  * Marc Suchard
  * Peter Rijnbeek
 
Any interested collaborators please contact Martijn Schuemie at schuemie@ohdsi.org and I will be happy to add you to the collaborator list. 

**Participating Institution(s):** Janssen R&D

**Full Protocol:** [[https://github.com/OHDSI/StudyProtocols/blob/master/LargeScalePopEst/extras/OHDSI%20Protocol%20Large-Scale%20Population-Level%20Evidence%20Generation.docx?raw=true|Large-Scale Population-Level Evidence Generation Protocol]]

**Initial Proposal Date:**  1/8/2016

**Launch Date:** 9/121/2016

**Receive Results for Analysis Date:**  TBD

**Study Closure Date:**  TBD

**Results Submission:** Via the OHDSI Sharing module embedded in study.

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===== Requirements =====
**CDM:** V5 only

**Table Accessed:**  CONCEPT, CONCEPT_ANCESTOR, CONDITION_ERA, CONDITION_OCCURRENCE, DRUG_ERA, DRUG_EXPOSURE, MEASUREMENT, OBSERVATION, OBSERVATION_PERIOD, PERSON, PROCEDURE_OCCURRENCE, VISIT_OCCURRENCE

**Database Dialects:** SQL Server, Postgres, Oracle, RedShift, PDW

**Software:** R (>= 3.2.2, RTools), Java

**Hardware:** Recommended 8 cores, 64GB memory, 250GB free space

**How to Run:** Full installation and run instructions are provided on the GitHub page below.


===== Code ===== 
[[https://github.com/OHDSI/StudyProtocols/tree/master/LargeScalePopEst|GitHub code for Proposed Study - Large-Scale Population-Level Evidence Generation]]
 

===== Datasets Run ===== 
  * CCAE (Janssen)
  * MDCD (Janssen)
  * MDCR (Janssen)
  * Optum (Janssen)


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Observational Health Data Sciences and Informatics

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