Expanding the use of administrative claims databases in conducting clinical real-world evidence studies in multiple sclerosis

Abstract

Objective:

Administrative claims databases provide a wealth of data for assessing the effect of treatments in clinical practice. Our aim was to propose methodology for real-world studies in multiple sclerosis (MS) using these databases.

Research design and methods:

In three large US administrative claims databases: MarketScan, PharMetrics Plus and Department of Defense (DoD), patients with MS were selected using an algorithm identified in the published literature and refined for accuracy. Algorithms for detecting newly diagnosed (‘incident’) MS cases were also refined and tested. Methodology based on resource and treatment use was developed to differentiate between relapses with and without hospitalization.

Results:

When various patient selection criteria were applied to the MarketScan database, an algorithm requiring two MS diagnoses at least 30 days apart was identified as the preferred method of selecting patient cohorts. Attempts to detect incident MS cases were confounded by the limited continuous enrollment of patients in these databases. Relapse detection algorithms identified similar proportions of patients in the MarketScan and PharMetrics Plus databases experiencing relapses with (2% in both databases) and without (15–20%) hospitalization in the 1 year follow-up period, providing findings in the range of those in the published literature.

Limitation:

Additional validation of the algorithms proposed here would increase their credibility.

Conclusions:

The methods suggested in this study offer a good foundation for performing real-world research in MS using administrative claims databases, potentially allowing evidence from different studies to be compared and combined more systematically than in current research practice.

Keywords: :

• Administrative claims
• Algorithm
• Multiple sclerosis
• Real-world evidence
• Relapse

Transparency

Declaration of funding

This study was funded by Novartis Pharma AG, Basel, Switzerland.

Declaration of financial/other relationships

G.C., R.L., E.V. and F.D. have disclosed that they are paid employees of Novartis Pharma AG, Basel, Switzerland. X.S. and B.H.J. have disclosed that they are paid employees of Truven Health Analytics, Bethesda, MD, USA. B.N., K.F. and J.S. have disclosed that they are paid employees of Evidera, Lexington, MA, USA. W.C. has disclosed that he is a paid employee of Novartis Pharma Co. Ltd, Shanghai, China. J.R.K. has disclosed that he is a paid employee of IMS Health, Waltham, MA, USA. R.F. has disclosed that she is a paid employee of Wellmera AG, Basel, Switzerland. Truven Health Analytics, Evidera, IMS Health and Wellmera AG were paid by Novartis Pharma AG for this study.

CMRO peer reviewer 1 has disclosed that he has no major conflicts of interest relevant to the review of this manuscript. However, he has disclosed that he is on two international project advisory boards for Novartis. CMRO peer reviewer 2 has no relevant financial or other relationships to disclose.

Acknowledgments

The authors take full responsibility for the content of the paper. The authors would like to acknowledge the contribution of Dr Adam Lowy (Novartis Pharma AG) to this manuscript in the development of the relapse detection algorithms. The authors thank Drs Caroline Freeman and Gemma Carter (Oxford PharmaGenesis Ltd) for medical writing support, editorial assistance, and collation and incorporation of comments from all authors.

Previous presentation: Data presented at the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Annual International Meeting, 18–22 May 2013, New Orleans, LA, USA (Poster PSY5); the 29th Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2–5 October 2013, Copenhagen, Denmark (Poster P312); and the ISPOR 16th Annual European Congress, 2–6 November 2013, Dublin, Ireland (Posters PRM41 and PRM43).