The decision-making process and benefit-risk assessment

Making better use of clinical trials: Computational decision support methods for evidence-based drug benefit-risk assessment

This thesis shows how an information system coupled with a database of clinical trial results can help make marketing authorization decisions more transparent and reproducible. Benefit-risk assessment is a key component of marketing authorization decisions. It consists of weighing the favourable effects (benefits) and unfavourable effects (risks) of a new drug in comparison to placebo or competing drugs.

Ideally, benefit-risk assessments are based on the best available evidence, typically meaning randomized controlled trials. However, due to a lack of suitable methods and supporting software, decisions are currently made in an informal manner and not linked explicitly to the underlying evidence. We developed the Aggregate Data Drug Information System (ADDIS), an integrated system for decision support, based on databases of structured clinical trials data. Novel algorithms are presented to automate network meta-analysis to combine clinical trial results and multi-criteria decision models are developed to support benefit-risk assessment. Storing clinical trials data in a structured manner enables applying decision support methods that help regulators explicitly link their decisions to the underlying evidence. These methods can improve the transparency, reproducibility, and predictability of regulatory decision-making.

Results

Tervonen T, Van Valkenhoef G, Buskens E, Hillege HL, Postmus D. A stochastic multicriteria model for evidence-based decision making in medicine benefit-risk analysis. In: Stat Med, 2011; 30(12): 1419-28. (journal publication)
Making better use of clinical trials (thesis)
Van Valkenhoef G, Tervonen T, De Brock B, Postmus D. Quantitative release planning in extreme programming. In: Information and Software Technology, 2011; 53(11): 1227-35. (journal publication)
Tervonen T, Van Valkenhoef G, Baştürk N, Postmus D. Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis. In: European Journal of Operational Research, 2012. (journal publication)
Van Valkenhoef G, Lu G, De Brock B, Hillege HL, Ades AE, Welton NJ. Automating network meta-analysis. In: Research Synthesis Methods, 2012. (journal publication)
Van Valkenhoef G, Tervonen T, De Brock B, Hillege H. Deficiencies in the transfer and availability of clinical trials evidence: a review of existing systems and standards. In: BMC Med Inform Decis Mak, 2012; 12(1): 95. (journal publication)
Van Valkenhoef G, Tervonen T, De Brock B, Hillege HL. Algorithmic parameterization of mixed treatment comparisons. In: Statistics and Computing, 2012; 22(5): 1099-111. (journal publication)
Van Valkenhoef G, Tervonen T, Zhao J, De Brock B, Hillege HL, Postmus D. Multicriteria benefit-risk assessment using network meta-analysis. In: J Clin Epidemiol, 2012; 65(4): 394-403. (journal publication)
Van Valkenhoef G, Tervonen T, Zwinkels T, De Brock B, Hillege H. ADDIS: a decision support system for evidence-based medicine. In: Decision Support Systems, 2012. (journal publication)
Van Valkenhoef G. Product and release planning practices for extreme programming. Chapter in: Agile processes in software engineering and extreme programming. Lecture Notes in Business Information Processing, 2010; 48(Part 2): 238-43. (journal publication)