drugis.org - Drug Information Systems

Our Research

Drug regulators assess the benefits and risks of a new drug based on the evidence provided by the company seeking market approval. The benefits and risks of a drug should be assessed in comparison with the benefits and risks of drugs already on the market (it would not make sense to approve a drug that is worse than an existing drug). Ideally, such decisions should take into account "all available evidence."

Current decision making in drug regulation relies entirely on the expert judgment of the assessors. This often doesn't take into account all available evidence to systematically assess the benefit-risk profile. Reliance on subjective assessment hides the reasoning supporting the decision and causes the regulatory process to be insufficiently transparent and traceable. Furthermore, the trade-offs between benefits and risks are seldom made explicit, least quantified.

Our project aims to develop a prototype of a decision support system that resolves these problems. We believe the solution lies in combining the MCDA methods for quantitative benefit-risk assessment with a structured database of evidence from clinical research. This will allow explicitly linking decisions back to their supporting evidence and will also make explicit the ranges of value judgments supporting the decisions made by the regulatory assessors.

Research Interests

The current drug development and regulation process and the existing information systems and standards in this area. We look at this both to identify opportunities to improve the existing process and to find systems and standards that we can reuse.
How to best combine methods for the synthesis of clinical evidence with methods for MCDA benefit-risk assessment to support regulatory decision making.
Analysis of the decision workflow supported by our software with the aim of identifying the minimal data model required for the assisted analysis of clinical trials data in a benefit-risk model.
Agile software development using Extreme Programming (XP) in an academic project.
(future) How to retrieve as much information as possible about clinical studies from "legacy" information sources. The information should be machine understandable in the sense that it can be used directly by our decision support software. The retrieval should require as little user intervention as possible.

Suggested Literature

Multi-Criteria Decision Analysis (MCDA)

MCDA methods help to make the evidence and (ranges of) value judgments supporting a decision explicit, as well as the uncertainty surrounding the evidence.

Tervonen, T. JSMAA: an open-source software for SMAA decision analysis. Newsletter of the European working Group "Multicriteria Aid for decisions", Series 20, Number 20, Fall 2009.
Tervonen, T. and Figueira, J.R. A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi-Criteria Decision Analysis, 15 (1-2): 1-14, 2008.
Belton, V. and Stewart, T. Multiple Criteria Decision Analysis: an integrated approach. Springer, 2002.
Lahdelma, R. and Salminen, P. SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research, 49 (3): 444-454, 2001.
Keeney, R.L. and H. Raiffa. Decisions with multiple objectives: preferences and value tradeoffs. Cambridge University Press, 1993.

For more information on MCDA and, specifically, Stochastic Multicriteria Acceptability Analysis (SMAA) see www.smaa.fi, a website by Tommi Tervonen.

Evidence Synthesis

In order to take into account all relevant evidence, effect estimates from many studies need to be combined into an overall estimate of the effect of a treatment in comparison to another (all other) treatment(s).

Cipriani, A., Furukawa, T.A., Salanti, G., Geddes, J.R., Higgins, J.P.T., Churchill, R., Watanabe, N., Nakagawa, A., Omori, I.M., McGuire, H., Tansella, M. and Barbui, C. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. The Lancet, 373 (9665): 746-758, 2009.
Salanti, G., Higgins, J.P.T., Ades, A.E. and Ioannidis, J.P.A. Evaluation of networks of randomized controlled trials. Statistical methods in medical research, 17 (3): 279-301, 2008.
Sutton, A.J. and Higgins, J.P.T. Recent developments in meta-analysis. Statistics in Medicine, 27 (5): 625-650, 2008.
Normand, S-L. Meta-analysis: formulating, evaluating, combining, and reporting. Statistics in Medicine, 18 (3): 321-359, 1999.
Hansen, R.A., Gartlehner, G., Lohr, K.N., Gaynes, B.N. and Cary, T.S. Efficacy and safety of second-generation anti-depressants in the treatment of major depressive disorder. Annals of internal medicine, 143 (6): 415-426, 2005.

Trial Registration

The recent establishment of trial registries and legislation requiring clinical studies carried out in humans to be registered, has lead to a wealth of (systematically reported) information on clinical trials. More recently, some registries also provide information on results, making them an important potential source of evidence.

Dickersin, K. and Rennie, D. Registering Clinical Trials. Journal of the American Medical Association, 290 (4): 516-523, 2003.
Sim, I., Owens, D.K., Lavori, P.W. and Rennels, G.D. Electronic Trial Banks: A complementary method far reporting randomized trials. Medical Decision Making, 20 (4): 440-450, 2000.
Zarin, D.A., Ide, N.C., Tse, T., Harlan, W.R., West, J.C., Lindberg, D.A.B. Issues in the registration of clinical trials. Journal of the American Medical Association, 297 (19): 2112-2120, 2007.
Tse, T., Williams, R.J., Zarin, D.A. Reporting "Basic Results" in ClinicalTrials.gov. Chest, 136 (1): 295-303, 2009.

Clinical Data Exchange Standards

Much work has been done (especially in the US) to automate and streamline data collection and data exchange during drug development. Here, we refer to some of the standards and standards bodies we feel are important. These standards model an ever larger portion of the clinical research domain.

Fridsma, D.B., Evans, J., Hastak, S. and Mead, C.N. The BRIDG project: A technical report. Journal of the American Medical Association, 25 (2): 130-137, 2008.
Clinical Data Interchange Standards Consortium (CDISC)

Agile & Extreme Programming

We develop our software using the Extreme Programming methodology.

Beck, K. and Andres, C. Extreme Programming Explained: Embrace Change, 2nd Edition. Addison-Wesley Professional, 2004.
Cohn, M. Agile Estimating and Planning. Robert C. Martin Series. Prentice Hall PTR, 2005.