The value of anticancer drugs - a regulatory view.

The high prices of new anticancer drugs and the marginal added benefit perceived by some stakeholders have fuelled a debate on the value of anticancer drugs in the European Union, even though an agreed definition of what constitutes a drug's value does not exist. In this Perspective, we discuss the value of drugs from different viewpoints and objectives of decision makers: for regulators, assessment of the benefit-risk balance of a drug is a cornerstone for approval; payers rely on cost-effectiveness analyses carried out by health technology assessment agencies for reimbursement decisions; for patients, treatment choices are based on personal preferences and attitudes to risk; and clinicians can use several scales (such as the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS)) that have been developed as an attempt to measure value objectively. Although a unique definition that fully captures the concept of value is unlikely to emerge, herein we discuss the importance of understanding different perspectives, and how regulators can help to inform different decision makers.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration.

OBJECTIVES: We sought to explore to what extent the use of Subpopulation Treatment Effect Pattern Plot (STEPP) may help to identify efficient treatment allocation strategy. METHODS: The analysis was based on data from the COACH study, in which 1023 patients with heart failure were randomly assigned to three treatments: care-as-usual, basic support, and intensive support. First, using predicted 18-month mortality risk as the stratification basis, a suitable strategy for assigning different treatments to different risk groups of patients was developed. To that end, a graphical exploration of the difference in net monetary benefit (NMB) across treatment regimens and baseline risk was used. Next, the efficiency gains resulting from this proposed subgroup strategy were quantified by computing the difference in NMB between our stratified approach and the best performing population-wide strategy. RESULTS: The analysis using STEPPs suggested that a differentiated approach, based on offering intensive support to low-risk patients (18-month mortality risk ≤ 0.16) and basic support to intermediate- to high-risk patients (18-month mortality risk > 0.16) would be an economically efficient treatment allocation strategy. This was confirmed in the subsequent cost-effectiveness analysis, where the average gain in NMB resulting from the proposed stratified approach compared to basic support for all was found to be €1312 (95% CI €390-€2346) per patient. CONCLUSIONS: STEPP provides a systematic approach to assess the interaction between baseline risk and the difference in NMB between competing interventions and to identify cutoffs to stratify patients in a health economically optimal manner.

The early economic evaluation of novel biomarkers to accelerate their translation into clinical applications.

BACKGROUND: Translating prognostic and diagnostic biomarker candidates into clinical applications takes time, is very costly, and many candidates fail. It is therefore crucial to be able to select those biomarker candidates that have the highest chance of successfully being adopted in the clinic. This requires an early estimate of the potential clinical impact and commercial value. In this paper, we aim to demonstratively evaluate a set of novel biomarkers in terms of clinical impact and commercial value, using occurrence of cardiovascular disease (CVD) in type-2 diabetes (DM2) patients as a case study. METHODS: We defined a clinical application for the novel biomarkers, and subsequently used data from a large cohort study in The Netherlands in a modeling exercise to assess the potential clinical impact and headroom for the biomarkers. RESULTS: The most likely application of the biomarkers would be to identify DM2 patients with a low CVD risk and subsequently withhold statin treatment. As a result, one additional CVD event in every 75 patients may be expected. The expected downstream savings resulted in a headroom for a point-of-care device ranging from €119.09 at a willingness to accept of €0 for one additional CVD event, to €0 at a willingness to accept of €15,614 or more. CONCLUSION: It is feasible to evaluate novel biomarkers on outcomes directly relevant to technological development and clinical adoption. Importantly, this may be attained at the same point in time and using the same data as used for the evaluation of association with disease and predictive power.

THE EARLY BIRD CATCHES THE WORM: EARLY COST-EFFECTIVENESS ANALYSIS OF NEW MEDICAL TESTS.

OBJECTIVES: There is little specific guidance on performing an early cost-effectiveness analysis (CEA) of medical tests. We developed a framework with general steps and applied it to two cases. METHODS: Step 1 is to narrow down the scope of analysis by defining the test's application, target population, outcome measures, and investigating current test strategies and test strategies if the new test were available. Step 2 is to collect evidence on the current test strategy. Step 3 is to develop a conceptual model of the current and new test strategies. Step 4 is to conduct the early-CEA by evaluating the potential (cost-)effectiveness of the new test in clinical practice. Step 5 involves a decision about the further development of the test. RESULTS: The first case illustrated the impact of varying the test performance on the headroom (maximum possible price) of an add-on test for patients with an intermediate-risk of having rheumatoid arthritis. Analyses showed that the headroom is particularly dependent on test performance. The second case estimated the minimum performance of a confirmatory imaging test to predict individual stroke risk. Different combinations of sensitivity and specificity were found to be cost-effective; if these combinations are attainable, the medical test developer can feel more confident about the value of further development of the test. CONCLUSIONS: A well-designed early-CEA methodology can improve the ability to develop (cost-)effective medical tests in an efficient manner. Early-CEAs should continuously integrate insights and evidence that arise through feedback, which may convince developers to return to earlier steps.

Continuous-Time Semi-Markov Models in Health Economic Decision Making: An Illustrative Example in Heart Failure Disease Management.

Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity.

Using multicriteria decision analysis to support research priority setting in biomedical translational research projects.

Translational research is conducted to achieve a predefined set of economic or societal goals. As a result, investment decisions on where available resources have the highest potential in achieving these goals have to be made. In this paper, we first describe how multicriteria decision analysis can assist in defining the decision context and in ensuring that all relevant aspects of the decision problem are incorporated in the decision making process. We then present the results of a case study to support priority setting in a translational research consortium aimed at reducing the burden of disease of type 2 diabetes. During problem structuring, we identified four research alternatives (primary, secondary, tertiary microvascular, and tertiary macrovascular prevention) and a set of six decision criteria. Scoring of these alternatives against the criteria was done using a combination of expert judgement and previously published data. Lastly, decision analysis was performed using stochastic multicriteria acceptability analysis, which allows for the combined use of numerical and ordinal data. We found that the development of novel techniques applied in secondary prevention would be a poor investment of research funds. The ranking of the remaining alternatives was however strongly dependent on the decision maker's preferences for certain criteria.

Design of stepwise screening for prediabetes and type 2 diabetes based on costs and cases detected.

OBJECTIVES: To provide insight into the trade-off between cost per case detected (CPCD) and the detection rate in questionnaire-based stepwise screening for impaired fasting glucose and undiagnosed type 2 diabetes. STUDY DESIGN AND SETTING: We considered a stepwise screening in which individuals whose risk score exceeds a predetermined cutoff value are invited for further blood glucose testing. Using individual patient data to determine questionnaire sensitivity and specificity and external sources to determine screening costs and patient response rates, we rolled back a decision tree to estimate the CPCD and the detection rate for all possible cutoffs on the questionnaire. RESULTS: We found a U-shaped relation between CPCD and detection rate, with high costs per case detected at very low and very high detection rates. Changes in patient response rates had a large impact on both the detection rate and the CPCD, whereas screening costs and questionnaire accuracy mainly impacted the CPCD. CONCLUSION: Our applied method makes it possible to identify a range of efficient cutoffs where higher detection rates can be achieved at an additional cost per detected patient. This enables decision makers to choose an optimal cutoff based on their willingness to pay for additional detected patients.

Applying Multiple Criteria Decision Analysis to Comparative Benefit-Risk Assessment: Choosing among Statins in Primary Prevention.

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefit-risk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis.

Assessment of Cotinine Reveals a Dose-Dependent Effect of Smoking Exposure on Long-term Outcomes After Renal Transplantation.

BACKGROUND: Smoking is a risk factor for poor late outcomes in renal transplant recipients (RTR). Smoking exposure can be assessed by self-report and cotinine measurements. We investigated whether use of cotinine as a biomarker for smoking exposure can serve as an alternative for self-report and to compare associations of smoking exposure by self-report and cotinine with outcomes in RTR and assess dose dependency. METHODS: Renal transplant recipients were classified as never, former, light (≤10 cigarettes/day), and heavy smokers (>10 cigarettes/day) according to self-report and analogous categories for urine and plasma cotinine. First, we assessed agreement of self-reported smoking exposure with smoking exposure according urine and plasma cotinine. Second, we compared the associations with graft failure and mortality. RESULTS: Of 603 RTR (age 51.5 ± 12.1 years, 55% men), 36.0% RTR were never, 42.3% former, 10.6% light, and 11.1% heavy smokers according to self-report. The majority (98.6%) of never smokers had nondetectable cotinine. However, 14 and 13 RTR reporting no active smoking had respective urine or plasma cotinine consistent with active smoking. Cotinine-based measurements were dose-dependently associated with mortality and graft failure. CONCLUSIONS: Plasma and urine cotinine can serve as an alternative to self-report and were dose-dependently associated with poor late outcomes in RTR.

Graphical assessment of incremental value of novel markers in prediction models: From statistical to decision analytical perspectives.

New markers may improve prediction of diagnostic and prognostic outcomes. We aimed to review options for graphical display and summary measures to assess the predictive value of markers over standard, readily available predictors. We illustrated various approaches using previously published data on 3264 participants from the Framingham Heart Study, where 183 developed coronary heart disease (10-year risk 5.6%). We considered performance measures for the incremental value of adding HDL cholesterol to a prediction model. An initial assessment may consider statistical significance (HR = 0.65, 95% confidence interval 0.53 to 0.80; likelihood ratio p < 0.001), and distributions of predicted risks (densities or box plots) with various summary measures. A range of decision thresholds is considered in predictiveness and receiver operating characteristic curves, where the area under the curve (AUC) increased from 0.762 to 0.774 by adding HDL. We can furthermore focus on reclassification of participants with and without an event in a reclassification graph, with the continuous net reclassification improvement (NRI) as a summary measure. When we focus on one particular decision threshold, the changes in sensitivity and specificity are central. We propose a net reclassification risk graph, which allows us to focus on the number of reclassified persons and their event rates. Summary measures include the binary AUC, the two-category NRI, and decision analytic variants such as the net benefit (NB). Various graphs and summary measures can be used to assess the incremental predictive value of a marker. Important insights for impact on decision making are provided by a simple graph for the net reclassification risk.

A multi-criteria decision analysis perspective on the health economic evaluation of medical interventions.

A standard practice in health economic evaluation is to monetize health effects by assuming a certain societal willingness-to-pay per unit of health gain. Although the resulting net monetary benefit (NMB) is easy to compute, the use of a single willingness-to-pay threshold assumes expressibility of the health effects on a single non-monetary scale. To relax this assumption, this article proves that the NMB framework is a special case of the more general stochastic multi-criteria acceptability analysis (SMAA) method. Specifically, as SMAA does not restrict the number of criteria to two and also does not require the marginal rates of substitution to be constant, there are problem instances for which the use of this more general method may result in a better understanding of the trade-offs underlying the reimbursement decision-making problem. This is illustrated by applying both methods in a case study related to infertility treatment.

Probability elicitation to inform early health economic evaluations of new medical technologies: a case study in heart failure disease management.

OBJECTIVES: Early estimates of the commercial headroom available to a new medical device can assist producers of health technology in making appropriate product investment decisions. The purpose of this study was to illustrate how this quantity can be captured probabilistically by combining probability elicitation with early health economic modeling. The technology considered was a novel point-of-care testing device in heart failure disease management. METHODS: First, we developed a continuous-time Markov model to represent the patients' disease progression under the current care setting. Next, we identified the model parameters that are likely to change after the introduction of the new device and interviewed three cardiologists to capture the probability distributions of these parameters. Finally, we obtained the probability distribution of the commercial headroom available per measurement by propagating the uncertainty in the model inputs to uncertainty in modeled outcomes. RESULTS: For a willingness-to-pay value of €10,000 per life-year, the median headroom available per measurement was €1.64 (interquartile range €0.05-€3.16) when the measurement frequency was assumed to be daily. In the subsequently conducted sensitivity analysis, this median value increased to a maximum of €57.70 for different combinations of the willingness-to-pay threshold and the measurement frequency. CONCLUSIONS: Probability elicitation can successfully be combined with early health economic modeling to obtain the probability distribution of the headroom available to a new medical technology. Subsequently feeding this distribution into a product investment evaluation method enables stakeholders to make more informed decisions regarding to which markets a currently available product prototype should be targeted.

Microsimulation for clinical decision-making in individual patients with established coronary artery disease: a concept.

BACKGROUND: In cardiovascular disease, numerous evidence-based prognostic models have been created, usually based on regression analyses of isolated patient datasets. They tend to focus on one outcome event, based on just one baseline evaluation of the patient, and fail to take the disease process in its dynamic nature into account. We present so-called microsimulation as an attractive alternative for clinical decision-making in individual patients. We aim to further familiarize clinicians with the concept of microsimulation and to inform them about the modeling process. METHODS AND RESULTS: We describe the modeling process, advantages and disadvantages of microsimulation. We illustrate the concept using a hypothetical 60-year-old patient, with several cardiac risk factors, who is hospitalized for myocardial infarction. By using microsimulation, we calculate this patient's probability of death. In our example, this particular patient's estimated life expectancy turns out to be 8.9 years. While calculating this life expectancy, we were able to account for multiple outcome events and changing patient characteristics. CONCLUSIONS: Microsimulation takes into account the dynamic nature of coronary artery disease by estimating most likely outcomes regarding a broad range of clinical events. Moreover, microsimulation can be used to evaluate treatment effects by estimating the event-free life expectancy with and without treatment. Hence, microsimulation has several advantages compared to modeling techniques such as regression.

A method for the early health technology assessment of novel biomarker measurement in primary prevention programs.

Many promising biomarkers for stratifying individuals at risk of developing a chronic disease or subsequent complications have been identified. Research into the potential cost-effectiveness of applying these biomarkers in actual clinical settings has however been lacking. Investors and analysts may improve their venture decision making should they have indicative estimates of the potential costs and effects associated with a new biomarker technology already at the early stages of its development. To assist in obtaining such estimates, this paper presents a general method for the early health technology assessment of a novel biomarker technology. The setting considered is that of primary prevention programs where initial screening to select high-risk individuals eligible for a subsequent intervention occurs, for example, prevention of type 2 diabetes. The method is based on quantifying the health outcomes and downstream healthcare consumption of all individuals who get reclassified as a result of moving from a screening variant based on traditional risk factors to a screening variant based on traditional risk factors plus a novel biomarker. As these individuals form well-defined subpopulations, a combination of disease progression modeling and sensitivity analysis can be used to perform an initial assessment of the maximum increase in screening cost for which the use of the new biomarker technology is still likely to be cost effective.

Multicriteria benefit-risk assessment using network meta-analysis.

OBJECTIVE: To enable multicriteria benefit-risk (BR) assessment of any number of alternative treatments using all available evidence from a network of clinical trials. STUDY DESIGN AND SETTING: We design a general method for multicriteria decision aiding with criteria measurements from Mixed Treatment Comparison (MTC) analyses. To evaluate the method, we apply it to BR assessment of four second-generation antidepressants and placebo in the setting of a published peer-reviewed systematic review. RESULTS: The analysis without preference information shows that placebo is supported by a wide range of possible preferences. Preference information provided by a clinical expert showed that although treatment with antidepressants is warranted for severely depressed patients, for mildly depressed patients placebo is likely to be the best option. It is difficult to choose between the four antidepressants, and the results of the model indicate a high degree of uncertainty. CONCLUSIONS: The designed method enables quantitative BR analysis of alternative treatments using all available evidence from a network of clinical trials. The preference-free analysis can be useful in presenting the results of an MTC considering multiple outcomes.

The COACH risk engine: a multistate model for predicting survival and hospitalization in patients with heart failure.

AIMS: Several models for predicting the prognosis of heart failure (HF) patients have been developed, but all of them focus on a single outcome variable, such as all-cause mortality. The purpose of this study was to develop a multistate model for simultaneously predicting survival and HF-related hospitalization in patients discharged alive from hospital after recovery from acute HF. METHODS AND RESULTS: The model was derived in the COACH (Coordinating Study Evaluating Outcomes of Advising and Counseling in Heart Failure) cohort, a multicentre, randomized controlled trial in which 1023 patients were enrolled after hospitalization because of HF. External validation was attained with the FINN-AKVA (Finish Acute Heart Failure Study) cohort, a prospective, multicentre study with 620 patients hospitalized due to acute HF. The observed vs. predicted 18-month survival was 72.1% vs. 72.3% in the derivation cohort and 71.4% vs. 71.2% in the validation cohort. The corresponding values of the c statistic were 0.733 [95% confidence interval (CI) 0.705-0.761] and 0.702 (95% CI 0.663-0.744), respectively. The model's accuracy in predicting HF hospitalization was excellent, with predicted values that closely resembled the values observed in the derivation cohort. CONCLUSION: The COACH risk engine accurately predicted survival and various measures of recurrent hospitalization in (acute) HF patients. It may therefore become a valuable tool in improving and personalizing patient care and optimizing the use of scarce healthcare resources.

A trial-based economic evaluation of 2 nurse-led disease management programs in heart failure.

BACKGROUND: Although previously conducted meta-analyses suggest that nurse-led disease management programs in heart failure (HF) can improve patient outcomes, uncertainty regarding the cost-effectiveness of such programs remains. METHODS: To compare the relative merits of 2 variants of a nurse-led disease management program (basic or intensive support by a nurse specialized in the management of patients with HF) against care as usual (routine follow-up by a cardiologist), a trial-based economic evaluation was conducted alongside the COACH study. RESULTS: In terms of costs per life-year, basic support was found to dominate care as usual, whereas the incremental cost-effectiveness ratio between intensive support and basic support was found to be equal to €532,762 per life-year; in terms of costs per quality-adjusted life-year (QALY), basic support was found to dominate both care as usual and intensive support. An assessment of the uncertainty surrounding these findings showed that, at a threshold value of €20,000 per life-year/€20,000 per QALY, basic support was found to have a probability of 69/62% of being optimal against 17/30% and 14/8% for care as usual and intensive support, respectively. The results of our subgroup analysis suggest that a stratified approach based on offering basic support to patients with mild to moderate HF and intensive support to patients with severe HF would be optimal if the willingness-to-pay threshold exceeds €45,345 per life-year/€59,289 per QALY. CONCLUSIONS: Although the differences in costs and effects among the 3 study groups were not statistically significant, from a decision-making perspective, basic support still had a relatively large probability of generating the highest health outcomes at the lowest costs. Our results also substantiated that a stratified approach based on offering basic support to patients with mild to moderate HF and intensive support to patients with severe HF could further improve health outcomes at slightly higher costs.