Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Drug benefit-risk assessment using multi-criteria decision analysis Douwe Postmus 1 , Gert van Valkenhoef, Hans Hillege Department of Epidemiology, University Medical Center Groningen, The Netherlands 1 Corresponding author. Email: d.postmus@umcg.nl
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Pharmaceutical decision making Pharmaceutical decision making Based on assessing benefits and risks of two or more drugs Ideally by considering all available clinical evidence Given outcome of clinical trials, should a new anti-depressant be allowed on the market? Which anti-depressant is most suited for severely depressed patients?
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Challenges in drug benefit-risk assessment Dealing with multiple comparisons and trade-offs Measurement of benefit is closely defined whereas risk is generic Decrease in body weight of 5kg versus 10% increase in the incidence of psychiatric disorders Balancing short and long term effects Changing from probability statements about the data given the truth (Frequentist) to probability statements about the truth given the data (Bayesian)
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Ad-hoc versus rational decision making Source: Baltussen et al., Cost Effectiveness and Resource Allocation 2006, 4:14
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Advantages of the use of MCDA It helps to structure the problem It makes the need for subjective judgments explicit and the process by which they are taken into account transparent It provides a focus and language for discussion, leading to better considered, justifiable, and explainable decisions The analysis serves to complement and challenge intuition; it does not seek to replace intuitive judgment or experience
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Learning objectives To know the different phases in a multi-criteria analysis To be able to identify suitable objectives and to know how these objectives can be organized hierarchically into a value tree To be able to summarize the clinical input data in a format suitable for a multi-criteria analysis To have a basic understanding of multi-attribute value theory (MAVT) and how it can be used to assess the decision maker’s preference structure To know how the preference modeling part of the MCDA process is supported by the ADDIS software
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary The process of MCDA: PrOACT-URL framework Source: EMA benefit-risk methodology project
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Case study: benefit-risk assessment of rimonabant in overweight or obese patients with type 2 diabetes Rimonabant, a selective cannabinoid type 1 receptor blocker, has shown to reduce body weight and improve cardiovascular and metabolic risk factors in non-diabetic overweight or obese patients Can rimonabant, in combination with diet and exercise, produce a clinically meaningful reduction in bodyweight, blood glucose levels, and cardiovascular risk factors in overweight or obese patients with type 2 diabetes? Do these favorable effects outweigh the side effects, such as depressed mood disorders, nausea, and dizziness?
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Alternatives Placebo Rimonabant 5 mg/day Rimonabant 20 mg/day ...
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Identification of objectives The use of MCDA calls for the identification of criteria against which the decision alternatives are to be evaluated These criteria are usually organized hierarchically into a value tree with higher-level constructs at the top of the tree and comprehensive and measurable attributes at the bottom In specifying the value tree, a balance must be found between completeness and conciseness The model should be usable with reasonable effort There should not be two or more criteria measuring the same concept (non-redundancy)
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Example value tree
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Assignments - 1 The following criteria emerged from the problem structuring process for the rimonabant case study Change from baseline in body weight 1 Change from baseline in waist circumference 2 Change from baseline in fasting glucose 3 Change from baseline in HbA1c 4 Anxiety 5 Depressed mood disorders 6 Hypoglycaemia 7 Discontinuations due to adverse events 8 Organize the above criteria hierarchically into a value tree Is the resulting value tree suitable for the purpose of a multi-criteria analysis?
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Value tree for the rimonabant case study
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Clinical evidence Drug benefit-risk assessment is generally based on data collected from randomized controlled trials (phase II and phase III studies) This includes outcome measures such as Incidences (i.e., the fraction of the sample that develops a certain condition over a given period of time) Changes in the levels of a continuous response variable (e.g., blood pressure) Discontinuation rates How to organize and present this data for the purpose of drug benefit-risk assessment?
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Effects table Unit of measurement Placebo Rimonabant Rimonabant 5 mg/day 20 mg/day Change from baseline % of body weight -1.5 in body weight at baseline Change from baseline mmol/L 0.33 in fasting glucose Hypoglycaemia % of patients 2 Anxiety % of patients 3 Discontinuations due to % of patients 5 adverse events
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Assignments - 2 Complete the previously introduced effects table by using the data from the Lancet publication
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Effects table Unit of measurement Placebo Rimonabant Rimonabant 5 mg/day 20 mg/day Change from baseline % of body weight -1.5 -2.4 -5.5 in body weight at baseline Change from baseline mmol/L 0.33 0.30 -0.64 in fasting glucose Hypoglycaemia % of patients 2 1 5 Anxiety % of patients 3 1 5 Discontinuations due to % of patients 5 8 15 adverse events
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Role playing game Consensus meeting to approve or reject market authorization of rimonabant as a complementary therapy in the treatment of type 2 diabetes
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary A simple choice between two drugs Drug A Drug B Weight loss (% of body weight) 7% 4% Anxiety 10% 3% To model the decision maker’s preference structure for the above problem, consider the following value trade-off: Starting at a value of 4%, how large should the increase in weight loss be to just compensate for an increase in the incidence of anxiety from 3% to 10%?
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Indifference curves Suppose that an increase in weight loss from 4% to x% is just sufficient to compensate for an increase in anxiety from 3% to 10% We then say that the decision maker is indifferent between the outcomes (4% WL, 3% Anx) and (x% WL, 10% Anx) The line connecting all outcomes that are indifferent to (4% WL, 3% Anx) is called an indifference curve
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Indifference curves
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Dug A ≻ drug B
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary Problem statement Let A be the set of decision alternatives from which the decision maker has to make a simple choice or which the decision maker has to rank from best to worse Associated with each a ∈ A is a vector of criteria measurements ( x a 1 , . . . , x a n ), where x a k denotes the performance of alternative a on criterion k The objective in MAVT is to construct a value function v : R n → R , such that for any two points x and y in the evaluation space X ⊆ R n v ( x ) = v ( y ) ⇔ x ∼ y v ( x ) > v ( y ) ⇔ x ≻ y
Introduction Problem structuring Consequences Trade-offs Uncertainty Summary The additive value function A value function exists when any two points x and y in X are comparable: x ∼ y , x ≻ y , or 1 y ≻ x the preference relation is transitive: x � y and y � z ⇒ x � z 2 If, in addition, value trade-offs between any two criteria do not depend on the levels of the other criteria ( preferential independence assumption ), the decision makers preference structure can be represented by the additive function v ( x , w ) = w 1 v 1 ( x 1 ) + · · · + w n v n ( x n )
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