User Tools

Site Tools


This is an old revision of the document!

Learning Effective Clinical Treatment Pathways from Data

Objective: Treatment guidelines for the management of type-2 diabetes mellitus (T2DM) are controversial because existing evidence from randomized clinical trials do not address many important clinical questions. An earlier investigation led by Observational Health Data Science (OHDSI) group reveled heterogeneity in the practice of both first and second line treatment choices in T2D with respect to established clinical guidelines. The choice of an optimal second-line drug among available options (Sulfonylureas, DPP4-Inhibitors, Thiazolidinediones) remains ambiguous. In this study, we seek to compare Sulfonylurea, DPP4-Inhibitors, and Thiazolidinediones when prescribed after Metformin for outcomes related to reduction in HbA1c < 7%, events related to Myocardial Infarction, Kidney and Eye related disorders within OHDSI network.

Rationale: <summarize study rationale>

Project Lead(s): Dr. Nigam Shah

Coordinating Institution(s): Stanford University

Additional Participants: <usually blank initially, list will grow as individuals are added who are not project leads>

Full Protocol: <if available, a link to protocol. not necessary for initial planning>

Initial Proposal Date:

Launch Date: <fill out once finalized>

Study Closure Date: <fill out once finalized>

Results Submission: <method of sumission, eg. Email or SFTP>


CDM: <V4 or V5 or both>

Table Accessed: <e.g., person, drug_exposure, observations>

Database Dialects: SQL Server, Postgres, Oracle

Software: «e.g., R>



Post a thread letting everyone know about this new proposed study at

Datasets Run

  • <list your own datasets or leave blank>
research/learning_effective_clinical_treatment_pathways_from_data.1509115121.txt.gz · Last modified: 2017/10/27 14:38 by rohit_vashisht