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Making Better Use of Clinical Trials Making better use of clinical trials Computational decision support methods for evidence-based drug benefit-risk assessment ZOL OFT PAXIL PLACEBO 75 PROZAC 20mg Gert van Valkenhoef Making better use of clinical trials Computational decision support methods for evidence-based drug benefit-risk assessment Proefschrift ter verkrijging van het doctoraat in de Medische Wetenschappen aan de Rijksuniversiteit Groningen op gezag van de Rector Magnificus, dr. E. Sterken, in het openbaar te verdedigen op woensdag 19 december 2012 om 14:30 uur door Gerardus Hendrikus Margondus van Valkenhoef geboren op 25 juli 1985 te Amersfoort Promotores: Prof. dr. J.L. Hillege Prof. dr. E.O. de Brock Copromotor: Dr. T.P. Tervonen Beoordelingscommissie: Prof. dr. A.E. Ades Prof. dr. E.R. van den Heuvel Prof. dr. M.J. Postma ISBN 978-90-367-5884-0 (PDF e-book) iii This thesis was produced in the context of the Escher Project (T6-202), a project of the Dutch Top Institute Pharma. The Escher Project brings together university and pharmaceutical partners with the aim of energizing pharmaceutical R & D by iden- tifying, evaluating, and removing regulatory and methodological barriers in order to bring efficacious and safe medicines to patients in an efficient and timely fashion. The project focuses on delivering evidence and credibility for regulatory reform and policy recommendations. The work was performed at the Faculty of Economics and Business, University of Groningen (2009 – 2010), at the Department of Epidemiology, University Medical Center Groningen (2010-2012), and during a series of research visits to the Depart- ment of Community Based Medicine, University of Bristol. The work was supported by the GUIDE institute of the Graduate School of Medical Sciences, University Medi- cal Center Groningen. Abstract Drug regulators and other strategic health care decision makers assess the health im- pact of alternative treatment options (e.g. drug A, drug B, or no treatment at all), in addition to other potential factors such as costs. The health impact of a drug is as- sessed in terms of its favorable effects, or benefits and its unfavorable effects, or risks. Whether the benefits of a treatment outweigh its risks (relative to the other options), i.e. the benefit-risk balance, is essential to this assessment. Thus, we refer to it as bene- fit-risk assessment. In drug regulation, the benefit-risk assessment is generally based on pivotal evidence provided by clinical trials. Although various information sys- tems store and process such evidence, there are still large gaps in the transfer of this information from the individual studies to the relevant decision makers. First, clinical trials data are primarily disseminated in text-based reports with no formal structure. This makes the information impossible to process automatically and thus leads to difficulty in finding and making use of the relevant data (data acquisi- tion). Second, current decision making in drug regulation relies entirely on the expert judgment of the assessors. Reliance on subjective assessment hides the reasoning that supports the decision and causes the regulatory process to be insufficiently transpa- rent and traceable. Furthermore, the benefit-risk balance is seldomly made explicit, least quantified. Formal methods and computer models can facilitate the decision ma- king process (decision aiding) as well as make it more explicit and transparent. Finally, while current regulatory decision making is based on the clinical trials data delive- red by the company, general benefit-risk decision making requires the assessment of all viable alternatives. Such relative benefit-risk assessments are expected to gain im- portance for regulators as well. Thus benefit-risk assessment is a problem involving multiple alternatives as well as multiple criteria, and the evidence supporting it is provided by complex networks of clinical trials. Appropriate statistical methods are required to coherently integrate the available data (evidence synthesis). Moreover, the evidence synthesis method(s) must be compatible with the decision aiding model(s) in order to be useful. vi In order to increase the transparency and quality of decision making in benefit- risk assessment all three problems must be addressed by a single integrated decision support system. The evidence synthesis and decision aiding models must fit together in an integrated work flow, and constructing them should be straightforward so that models can be created and revised on the fly for the decision at hand. This requires straightforward data acquisition, which is only possible if the evidence is available in a sufficiently structured format to enable computer support. In this thesis we address these problems by the development of the Aggregate Data Drug Information System (ADDIS), a decision support system for evidence ba- sed strategic health care decision making. The development of the ADDIS software was carried out simultaneously with research into the appropriate data model, evi- dence synthesis methods and decision support models. ADDIS was developed ite- ratively according to agile software development principles, meaning that a working system was available throughout the project and frequent (quarterly) releases were made to the project stakeholders and the general public to ensure rapid feedback. ADDIS is working, freely available, and open source software. The evidence synthesis problem was solved by developing algorithms to auto- matically generate the complex statistical models that underly network meta-analy- sis, a method to evaluate any number of treatments using a network of clinical trials. Previously, network meta-analysis required the manual specification of these models. We developed Multiple Criteria Decision Analysis (MCDA) methods to support drug regulatory decisions, and showed how they can be used in combination with net- work meta-analysis. Specifically, we used the Stochastic Multicriteria Acceptability Analysis (SMAA) method to take into account the uncertainty inherent in clinical trials information. In addition, SMAA enables decision aiding when the decision ma- ker is not able or willing to commit to precise trade-off valuations (preferences) by allowing them to be completely or partially unspecified. Finally, we developed the ADDIS decision support system and data model, which are the result of iterative de- velopment focused on delivering evidence synthesis and decision aiding functiona- lity rather than top-down design. While other data models for summary-level clinical trials data are in development, they have thus far not led to useful applications. On the other hand, statistical software exists for evidence synthesis of clinical trials, but their data formats are analysis-specific so extracted data can not be reused. In con- trast, our data model enables data analysis and decision aiding, and that data have to be extracted only once in order to support many different analyses and decision aiding scenarios. Samenvatting Beleidsmakers in de gezondheidszorg, zoals registratieautoriteiten die bepalen of een geneesmiddel op de markt komt, beoordelen geregeld de effecten van verschillende behandelingsstrategieen.¨ Hiervoor vergelijken ze een nieuw middel met een of meer bestaande middelen en/of een placebo. Bij dergelijke beslissingen staan de gezond- heidseffecten van het middel centraal. Andere beleidsmakers, zoals vergoedingsau- toriteiten, nemen ook andere factoren zoals de kosten en de maatschappelijke effecten van behandeling met de verschillende middelen mee in hun beslissing. Over het alge- meen heeft een geneesmiddel zowel wenselijke als schadelijke gezondheidseffecten. De vraag die beleidsmakers beantwoorden is of de wenselijke effecten zwaarder we- gen dan de schadelijke effecten: de balans werkzaamheid-schadelijkheid. De beoordeling van werkzaamheid-schadelijkheid is altijd relatief tot een of meer andere opties. Zo zal de balans voor een nieuw geneesmiddel ten minste beter moeten uitvallen dan wanneer er niet behandeld wordt. Bij markttoegang is deze beoordeling primair ge- baseerd op bewijs uit gerandomiseerde klinische studies met een controlegroep (ran- domized controlled trials, RCTs). Hoewel verschillende informatiesystemen deze in- formatie opslaan en verwerken is de overdracht van informatie uit individuele RCTs naar de beleidsmakers niet efficient,¨ gemakkelijk, of transparant. De beslissingen zelf zijn ook niet altijd op transparante wijze genomen of duidelijk onderbouwd. Een aantal problemen liggen ten grondslag aan deze situatie (zie hoofdstuk 2 en appendix A). Ten eerste vindt de informatieoverdracht plaats door middel van tek- stuele documenten zonder formele structuur, waardoor de informatie niet goed door computers te verwerken is. Het is dus moeilijk om de juiste informatie terug te vinden en te gebruiken (informatieverwerving). Ten tweede is de besluitvorming bij markttoe- gang grotendeels informeel: er wordt vertrouwd op het (subjectieve) oordeel van de groep experts die de beslissing neemt. Hierdoor is de onderbouwing van beslissin- gen soms onduidelijk en is niet transparant welke rol het bewijs uit RCTs gespeeld heeft bij de beslissing. Doordat niet expliciet gemaakt wordt hoe de wenselijke en schadelijke effecten gewogen worden in de balans werkzaamheid-schadelijkheid zijn viii de beslissingen onvoldoende reproduceerbaar en voorspelbaar. Formele methoden en computermodellen kunnen de besluitvorming ondersteunen en deze tegelijker- tijd explicieter en transparanter maken (beslissingsondersteuning).
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