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Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
ADDIS
Hans Hillege Douwe Postmus Gert van ValkenhoefBob Goeree Bert de Brock
CvZ, Diemen (NL), 27 March 2014
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
1 Introduction
2 Trials database
3 Network meta-analysis
4 Break
5 MCDA
6 Disease progression
7 ADDIS and CvZ
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Making Better Use of Clinical Trials
Development of ADDIS (Aggregate Data Drug InformationSystem) for aiding Benefit Risk Assessment of new medicines
Hans Hillege (UMCG, MEB)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Outline
Background
ADDIS & the Escher project
Benefit-risk
Regulator / payer interface
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Intraclass differences?
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Dose-response curve-fitting (A-II antagonists)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Dose-response curve-fitting (A-II antagonists)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
ISDB assessed EMA transparency
Lack of a clear and consistent policy on the reporting ofclinical trial data, e.g. in the case of irbesartan:
details about the optimal dose was missingonly 2 of the 3 trials were described in detailthe risk-benefit ratio of irbesartan was not clearly comparedwith that of enalaprilthe adverse effects section was far more detailed than theefficacy section, etc.
ISDB recommended that the EMA should develop a clearpolicy that ensures consistency from EPAR to EPAR.
ISDB Assessment of nine European public assessment reports, June 1998
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
The solution
Clinical assessments using a systematic review format
Systematically organised warehouse of drug information
Web-based drug knowledge network system on an XMLplatform
The system contains information at the level of detail requiredfor publication in scientific medical journalsRelational database systemTabulated and graphical output
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
The solution
Clinical assessments using a systematic review format
Systematically organised warehouse of drug information
Web-based drug knowledge network system on an XMLplatform
The system contains information at the level of detail requiredfor publication in scientific medical journalsRelational database systemTabulated and graphical output
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
http://escher-projects.org/
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
The Escher project
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Project Escher, work package 3.2
Bridging the gap between aggregated clinical data andevidence-based drug regulation using state of the artmethods for benefit risk decision making
Implementing in usable software to be deployed not only in theregulatory domain but also in the decision-making domain ofe.g. HTA agencies, hospital and community pharmacists,medical specialists, general practitioners and patients
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Prototype: global requirements
Interviews with major stakeholders of different domains:
Repository of clinical trials
Based on aggregated data
Should answer on-demand different efficacy/safety questionson a efficient, transparent and accountable way within andacross compounds
Should streamline benefit-risk decision making
Intended use at first for regulatory authorities and at a laterstage for others
Intuitive and user friendly interface
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
ADDIS
ADDIS: Aggregate Data Drug Information System
Key ingredients:
Structured database of clinical trials dataOn-the-fly statistics, evidence synthesisBenefit-risk decision modelling / decision supportBridging efficacy/safety to relative effectiveness
ADDIS 1.x is free/open source software
Download: http://drugis.org/addis
ADDIS 2.0 under development (IMI GetReal)
Collaborative web-based platform
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
http://drugis.org/
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Overview of the initiatives since 2000
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
EMA Benefit Risk Assessment
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
EMA Benefit Risk Assessment
Benefits
Beneficial effectsUncertainty in the knowledge about the beneficial effects
Risks
Unfavourable effectsUncertainty in the knowledge about the unfavourable effects
Balance
Importance of favourable and unfavourable effectsBenefit-risk balanceDiscussion on the benefit-risk assessmentConclusions
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
EMA benefit risk project
Objectives
Improve consistency, transparency and communication ofbenefit-risk assessment
Implicit → Explicit
Five Work Packages
Description of current practice
Applicability of current tools and methods
Field tests of tools and methods
Development of tools and methods for B/R
Pilot and training (ongoing)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
EMAs PrOACT-URL Framework
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Effort versus Precision Trade-off
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Effort versus Precision Trade-off
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Effort versus Precision Trade-off
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Effort versus Precision Trade-off
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Making better use of clinical trials
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
The best approach to demonstrating a medicine’s value
[Bergmann et al., 2014]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Structured clinical trials databases
Enabling efficiency and transparency through automation
Gert van Valkenhoef (UMCG)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Why do we need a structured database of trials?
Evidence-based decision making time-consuming/error-prone
No comprehensive source of trial information existsTrial information is insufficiently structured
Missed opportunities to introduce more structure
Trial registration, regulatory submission and systematic review
With ADDIS, we aim to solve these problems
[van Valkenhoef et al., 2012b, 2013]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Current status: document-based workflow
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
The future: data-based workflow
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Advantages of data-based workflows
Efficiency: automation, data re-use
Transparency: trace results back to underlying data
Flexibility: on-demand analyses without re-extraction
Learning: compare data and decision to past cases
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Enabling data-based workflows
Information must be stored in structured format
Need both data and (at least some of) its semantics
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Structured databases of trials
For example, the following are insufficient:
Meta-analysis data tables
Captures some dataTrial structure and data semantics unclearResult of interpretation of original trials!
ClininicalTrials.gov
Very important step in right direction!Captures most data (AEs often incomplete)Missing structural information (e.g. time dimension)Semantics and internal consistency insufficient
[van Valkenhoef et al., 2012b, 2013]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Data management in ADDIS: demo
Brief demo: Canagliflozin for type II diabetes
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Network meta-analysis
Consistent estimates derived from all relevant trials
Gert van Valkenhoef (UMCG)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Why network meta-analysis?
Often, > 2 treatments must be compared
Traditional (pair-wise) meta-analysis limited
Pair-wise results: insufficient insightConcerns about inconsistencySome comparisons not studied
For n treatments, n(n − 1)/2 comparisons
Indirect evidence?
Network meta-analysis enables
Simultaneous synthesis for ≥ 2 alternativesUsing both direct and indirect evidenceAnd to assess consistency
[Dias et al., 2013b; van Valkenhoef et al., 2012c]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Why network meta-analysis?
Often, > 2 treatments must be compared
Traditional (pair-wise) meta-analysis limited
Pair-wise results: insufficient insightConcerns about inconsistencySome comparisons not studied
For n treatments, n(n − 1)/2 comparisons
Indirect evidence?
Network meta-analysis enables
Simultaneous synthesis for ≥ 2 alternativesUsing both direct and indirect evidenceAnd to assess consistency
[Dias et al., 2013b; van Valkenhoef et al., 2012c]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Why network meta-analysis?
Often, > 2 treatments must be compared
Traditional (pair-wise) meta-analysis limited
Pair-wise results: insufficient insightConcerns about inconsistencySome comparisons not studied
For n treatments, n(n − 1)/2 comparisons
Indirect evidence?
Network meta-analysis enables
Simultaneous synthesis for ≥ 2 alternativesUsing both direct and indirect evidenceAnd to assess consistency
[Dias et al., 2013b; van Valkenhoef et al., 2012c]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
What is network meta-analysis?
Extension of pair-wise meta-analysis
Same assumptions, but applied to entire network of trials
Systematic differences between comparisons (inconsistency)difficult to detectMust be especially careful about (pure) indirect comparisonsExamine trial characteristics: are the trials similar enough?
When comparing multiple treatments, only NMA can provideconsistent basis for decision making
[Dias et al., 2013a; Jansen and Naci, 2013]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Example network 1
Dangerous: no way to check assumptions!
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Example network 1
Dangerous: no way to check assumptions!
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Example network 2
Assumptions can be cross-checked in loops
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Example network 2
Assumptions can be cross-checked in loops
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Network meta-analysis in ADDIS
ADDIS automates network meta-analysis
Supports OR and mean difference
ADDIS 2 uses GeMTC R package: many more outcomes
Methods to detect heterogeneity, inconsistency
Methods to assess convergence (MCMC)
Rank probabilities
[van Valkenhoef et al., 2012a, 2014; van Valkenhoef and van den Heuvel,
2014]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Network meta-analysis in ADDIS: demo
Brief demo: Canagliflozin for type II diabetes
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Break!
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Multiple Criteria Decision Analysis
MCDA for health policy decision making
Douwe Postmus (UMCG)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Determining the reimbursement status of canaglifozin
Indication: the use of canaglifozin as an add-on therapy withmetforin
How does the magnitude of the health benefits and harms ofcanaglifozin compare with existing pharmaceuticals?
The assessment should include a comparison with the mostappropriate healthcare intervention(s)The assessment should primarily focus on data derived fromusual circumstances of health care practiceThe assessment should present the uncertainties affectinginterpretation of reliability and clinical relevance of the results
[EUnetHTA Work Package 5, 2013]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Informal versus formal decision making
[Baltussen and Niessen, 2006]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
The process of MCDA
Problem structuring phase
GoalA clear formulation of objectives, reached through consensus between stakeholders. Identification of all aspects relevant to the decision problem
MethodsStructured discussions, focus group meetings. First stage of divergent thinking, followed by convergent phase aimed at structuring the problem
Intermediate outcomes- Hierarchical structure of problem with decision criteria (decision tree)
- Set of decision alternatives
Scoring phase
New information obtained in the scoring phase may require a re-structuring of the decision problem
Intermediate outcomeTable with every alternative scored on each criterium, either cardinal (values) or ordinal (ranking)
Decision gateDoes the scoring table indicate a dominating alternative, or is further analysis necessary to support a decision?
OutcomeDecision based on information in scoring table
Preference modeling phase
GoalTo formalize the decision maker's preference structure in order to identify the best alternative or to rank them from best to worse
MethodsThe problem is decomposed into a set of smaller subproblems, for which preference information is obtained. Using a mathematical function this is compiled into a preference for the full problem
OutcomeDecision based on preference model
StartDecision maker confronted with decision problem
MethodsSearches for existing data in literature and databases. Interviews to elicit expert opinion
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Using ADDIS to select criteria and alternatives
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
From relative to absolute effect measures
For reasons of statistical robustness, evidence synthesismethods estimate only relative effects
Without an estimate of the baseline risks, these relativeeffects are difficult to compare across criteria
Does a relative risk reduction of 0.5 on criterion A outweigh arelative risk increase of 1.1 on criterion B?
To overcome this problem, we have developed a simpletwo-stage procedure to convert relative effects to absoluteeffects
[van Valkenhoef et al., 2012c]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
From relative to absolute effect measures
For reasons of statistical robustness, evidence synthesismethods estimate only relative effects
Without an estimate of the baseline risks, these relativeeffects are difficult to compare across criteria
Does a relative risk reduction of 0.5 on criterion A outweigh arelative risk increase of 1.1 on criterion B?
To overcome this problem, we have developed a simpletwo-stage procedure to convert relative effects to absoluteeffects
[van Valkenhoef et al., 2012c]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
From relative to absolute effect measures
For reasons of statistical robustness, evidence synthesismethods estimate only relative effects
Without an estimate of the baseline risks, these relativeeffects are difficult to compare across criteria
Does a relative risk reduction of 0.5 on criterion A outweigh arelative risk increase of 1.1 on criterion B?
To overcome this problem, we have developed a simpletwo-stage procedure to convert relative effects to absoluteeffects
[van Valkenhoef et al., 2012c]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
How is this process supported in ADDIS?
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
ADDIS 2: implementation of EMA’s effects table
[European Medicines Agency, 2012]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Preference modeling
Objective: to construct a formal model of the decisionmaker’s preferences so that the alternative technologies canbe compared relative to each other in a systematic andtransparent way
Within ADDIS, we make use of the additive value function
v(a,w) =n∑
i=1
wivi (a)
vi (a) is the value score reflecting alternative a’s performanceon criterion iwi is the weight assigned to reflect the importance of criterion i
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Preference modeling
Objective: to construct a formal model of the decisionmaker’s preferences so that the alternative technologies canbe compared relative to each other in a systematic andtransparent way
Within ADDIS, we make use of the additive value function
v(a,w) =n∑
i=1
wivi (a)
vi (a) is the value score reflecting alternative a’s performanceon criterion iwi is the weight assigned to reflect the importance of criterion i
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Interpretation of the weights
The weights indicate how much more important the swingfrom worst to best on one criterion is compared to the swingfrom worst to best on the other criteria
wk > wl implies that if the decision maker had to choosebetween improving either criterion k or criterion l from theworst to the best value, he would improve criterion k
An ordinal ranking of the weights can be obtained by askingthe decision maker to rank order the swings from worst tobest on all criteria
Given an ordinal ranking of the weights, different techniqueshave been developed to assign exact values to them
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Interpretation of the weights
The weights indicate how much more important the swingfrom worst to best on one criterion is compared to the swingfrom worst to best on the other criteria
wk > wl implies that if the decision maker had to choosebetween improving either criterion k or criterion l from theworst to the best value, he would improve criterion k
An ordinal ranking of the weights can be obtained by askingthe decision maker to rank order the swings from worst tobest on all criteria
Given an ordinal ranking of the weights, different techniqueshave been developed to assign exact values to them
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Interpretation of the weights
The weights indicate how much more important the swingfrom worst to best on one criterion is compared to the swingfrom worst to best on the other criteria
wk > wl implies that if the decision maker had to choosebetween improving either criterion k or criterion l from theworst to the best value, he would improve criterion k
An ordinal ranking of the weights can be obtained by askingthe decision maker to rank order the swings from worst tobest on all criteria
Given an ordinal ranking of the weights, different techniqueshave been developed to assign exact values to them
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Demonstration ADDIS 2 MCDA web service
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease progression models
Extrapolating short-term and surrogate outcomes
Bob Goeree (University of Groningen)
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling
Definition A mathematical representation of diseaseprogression
Goal To evaluate long term effects of a treatment. E.g. lifeyears gained, QALY gained, costs-effectiveness.
Predominant approach in cost-effectiveness analysis, which isthe focus of this presentation
Each model is different, however the underlying methodologyis always the same, with deviating modeling choices based onavailable data
For my master thesis I developed a prototype that supportsperforming a cost-effectiveness analysis, it remains work in progress
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Disease states
No
Diabetes Diabetes
Dead
Figure : Simple disease state model
[Postmus et al., 2012]
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Transition probabilities
Goal Transition probabilities are used to simulate diseaseprogression
Disease state models can be used to extrapolate for futureeffects, traditionally done in discrete time events (e.g. cyclesof one year)
Each cycle a patient can ’travel’ from one state to another,which represents disease progression
These rates are obtained from clinical trials
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Utility weights and costs
Goal To approximate the achieved effects based on theamount of cycles a patient spends in a disease state
We award states with different utility rates. E.g. the ’Nodiabetes’ state has an utility weight of 0.84 (which couldrepresent effect on quality of life)
Similarly, costs are just an achieved effect (e.g. one year instate diabetes costs EUR 1805).
Often effects are also discounted for future effects, e.g. 1.5%per year.
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Simulation
Goal Once we have a mathematical approximation to thedisease progression and the effects each alternative achieves,this approximation needs to be evaluated
E.g. We suppose 500 patients start in state no diabetes andwe simulate them for 20 years.
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Prototype
Current integration into ADDIS, live demonstration
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Limitations
Limitations of current prototype:
Does not address patient heterogeneity
Only a select set of modeling choices available
All inputs are from the decision maker. Ideally inputs arederived, in an automated way, from the available clinicalevidence
For the prototype I assumed that the data available is from asingle clinical trial. However using multiple clinical trials toinform the disease state model provides more meaningfulresults
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Disease state modelling: Future work
I would like to conduct research into creating a formal approach tocreate, and evaluate, a disease state model (in an automatedfashion).
Main challenges:
Using multiple clinical trials, in an automated fashion, toinform a disease state model (Network meta-analysis)
How can we extrapolate the observed, short term, effects tolong term effects / ’hard’ clinical results like life years / QALYgained?
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
What can ADDIS do for you?
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Thank you!
Thank you very much for your attention!The ADDIS team:
Researchers: Gert van Valkenhoef, Bert de Brock, HansHillege, Tommi Tervonen, Douwe Postmus, Hans vanLeeuwen, Joel Kuiper
ADDIS 2 developers: Daan Reid, Connor Stroomberg
ADDIS 1 developers: Maarten Jacobs, Ahmad Kamal, HannoKoeslag, Joel Kuiper, Wouter Reckman, Daniel Reid, FlorinSchimbinschi, Tijs Zwinkels
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
Baltussen, R. and Niessen, L. (2006). Cost Effectiveness andResource Allocation, 4(1):14.
Bergmann, L., Enzmann, H., Broich, K., Hebborn, A., Marsoni, S.,Goh, L., Smyth, J. F., and Zwierzina, H. (2014). Actualdevelopments in european regulatory and health technologyassessment of new cancer drugs: what does this mean foroncology in europe? Annals of Oncology, 25(2):303–306.
Dias, S., Sutton, A. J., Ades, A. E., and Welton, N. J. (2013a). Ageneralized linear modeling framework for pairwise and networkmeta-analysis of randomized controlled trials. Medical DecisionMaking, 33(5):607–617.
Dias, S., Welton, N. J., Sutton, A. J., and Ades, A. E. (2013b).Evidence synthesis for decision making 1: Introduction. MedicalDecision Making, 33(5):597–606.
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
EUnetHTA Work Package 5 (2013). Hta core model for rapidrelative effectiveness assessment of pharmaceuticals - version3.0. EUnetHTA report.
European Medicines Agency (2012). Benefit-risk methodologyproject work package 4 report: Benefit-risk tools and processes.European Medicines Agency Report No. EMA/297405/2012 -Revision 1.
Jansen, J. P. and Naci, H. (2013). Is network meta-analysis asvalid as standard pairwise meta-analysis? it all depends on thedistribution of effect modifiers. BMC Medicine, 11(1):159.
Postmus, D., de Graaf, G., Hillege, H. L., Steyerberg, E. W., andBuskens, E. (2012). A method for the early health technologyassessment of novel biomarker measurement in primaryprevention programs. Statistics in Medicine, 31(23):2733–2744.
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
van Valkenhoef, G., Dias, S., Ades, A. E., and Welton, N. J.(2014). Automated generation of node-splitting models for theassessment of inconsistency in network meta-analysis.(Submitted manuscript).
van Valkenhoef, G., Lu, G., de Brock, B., Hillege, H., Ades, A. E.,and Welton, N. J. (2012a). Automating network meta-analysis.Research Synthesis Methods, 3(4):285–299.
van Valkenhoef, G., Tervonen, T., de Brock, B., and Hillege, H.(2012b). Deficiencies in the transfer and availability of clinicalevidence in drug development and regulation. BMC MedicalInformatics and Decision Making, 12:95.
van Valkenhoef, G., Tervonen, T., Zhao, J., de Brock, B., Hillege,H. L., and Postmus, D. (2012c). Multi-criteria benefit-riskassessment using network meta-analysis. Journal of ClinicalEpidemiology, 65(4):394–403.
Introduction Trials database Network meta-analysis Break MCDA Disease progression ADDIS and CvZ References
van Valkenhoef, G., Tervonen, T., Zwinkels, T., de Brock, B., andHillege, H. (2013). ADDIS: a decision support system forevidence-based medicine. Decision Support Systems,55(2):459–475.
van Valkenhoef, G. and van den Heuvel, E. R. (2014). Modelinginconsistency as heterogeneity in newtork meta-analysis. (Draftmanuscript).