From Individual to Population Preferences:Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs

Introduction. The Dirichlet distribution has been proposed for representing preference heterogeneity, but there is limited evidence on its suitability for modeling population preferences on treatment benefits and risks. Methods. We conducted a simulation study to compare how the Dirichlet and standard discrete choice models (multinomial logit [MNL] and mixed logit [MXL]) differ in their convergence to stable estimates of population benefit-risk preferences. The source data consisted of individual-level tradeoffs from an existing 3-attribute patient preference study (N = 560). The Dirichlet population model was fit directly to the attribute weights in the source data. The MNL and MXL population models were fit to the outcomes of a simulated discrete choice experiment in the same sample of 560 patients. Convergence to the parameter values of the Dirichlet and MNL population models was assessed with sample sizes ranging from 20 to 500 (100 simulations per sample size). Model variability was also assessed with coefficient P values. Results. Population preference estimates of all models were very close to the sample mean, and the MNL and MXL models had good fit (McFadden's adjusted R2 = 0.12 and 0.13). The Dirichlet model converged reliably to within 0.05 distance of the population preference estimates with a sample size of 100, where the MNL model required a sample size of 240 for this. The MNL model produced consistently significant coefficient estimates with sample sizes of 100 and higher. Conclusion. The Dirichlet model is likely to have smaller sample size requirements than standard discrete choice models in modeling population preferences for treatment benefit-risk tradeoffs and is a useful addition to health preference analyst's toolbox

Correction to: A Systematic and Critical Review of Discrete Choice Experiments in Asthma and Chronic Obstructive Pulmonary Disease (Jul, 10.1007/s40271-.021-.00536-w, 2021)

The article “A Systematic and Critical Review of Discrete Choice Experiments in Asthma and Chronic Obstructive Pulmonary Disease”, written by Hannah Collacott, Dian Zhang, Sebastian Heidenreich and Tommi Tervonen1 was originally published electronically on the publisher’s internet portal on 12 July 2021 without open access.</p

A Systematic and Critical Review of Discrete Choice Experiments in Asthma and Chronic Obstructive Pulmonary Disease

BACKGROUND: Regulators have called for greater emphasis on the role of the patient voice to inform medical product development and decision making, and expert guidelines and reports for asthma and chronic obstructive pulmonary disease (COPD) both explicitly recommend the consideration of patient preferences in the management of these diseases. Discrete choice experiments (DCEs) are commonly used to quantify stakeholders’ treatment preferences and estimate the trade-offs they are willing to make between outcomes such as treatment benefits and risks. OBJECTIVE: The aim of this systematic literature review is to provide an up-to-date and critical review of DCEs published in asthma and COPD; specifically, we aim to evaluate the subject of preference studies conducted in asthma and COPD, what attributes have been included, stakeholders’ preferences, and the consistency in reporting of instrument development, testing and reporting of results. METHODS: A systematic review of published DCEs on asthma and COPD treatments was conducted using Embase, Medline and the Cochrane Database of Systematic Reviews. Studies were included if they included a DCE conducted in a relevant population (e.g. patients with asthma or COPD or their caregivers, asthma or COPD-treating clinicians, or the general population), and reported quantitative outcomes on participants’ preferences. Study characteristics were summarised descriptively, and descriptive analyses of attribute categories, consistency in reporting on key criteria, and stakeholder preferences were undertaken. RESULTS: A total of 33 eligible studies were identified, including 28 unique DCEs. The majority (n = 20; 71%) of studies were conducted in a patient sample. Studies focused on inhaler treatments, and included attributes in five key categories: symptoms and treatment benefits (n = 23; 82%), treatment convenience (n = 19; 68%), treatment cost (n = 17; 61%), treatment risks (n = 13; 46%), and other (n = 10; 36%). Symptoms and treatment benefits were the attributes most frequently ranked as important to patients (n = 26, 72%), followed by treatment risks (n = 7, 39%). Several studies (n = 9, 32%) did not qualitatively pre-test their DCE, and a majority did not report the uncertainty in estimated outcomes (n = 18; 64%). CONCLUSIONS: DCEs in asthma and COPD have focused on treatment benefits and convenience, with less evidence generated on participants’ risk tolerance. Quality criteria and reporting standards are needed to promote study quality and ensure consistency in reporting between studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40271-021-00536-w

Algorithmic parameterization of mixed treatment comparisons

Mixed Treatment Comparisons (MTCs) enable the simultaneous meta-analysis (data pooling) of networks of clinical trials comparing ≥2 alternative treatments. Inconsistency models are critical in MTC to assess the overall consistency between evidence sources. Only in the absence of considerable inconsistency can the results of an MTC (consistency) model be trusted. However, inconsistency model specification is non-trivial when multi-arm trials are present in the evidence structure. In this paper, we define the parameterization problem for inconsistency models in mathematical terms and provide an algorithm for the generation of inconsistency models. We evaluate running-time of the algorithm by generating models for 15 published evidence structures

Evaluation of scalarization methods and NSGA-II/SPEA2 genetic algorithms for multi-objective optimization of green supply chain design

This paper considers supply chain design in green logistics. We formulate the choice of an environmentally conscious chain design as a multi-objective optimization (MOO) problem and approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods as well as with two popular genetic algorithms, NSGA-II and SPEA2. We extend an existing case study of green supply chain design in the South Eastern Europe region by optimizing simultaneously costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. The results show that in the considered case the scalarization methods outperform genetic algorithms in finding efficient solutions and that the CO2 and PM emissions can be lowered by accepting a marginal increase of costs over their global minimum

Quantifying preference in drug benefit-risk decisions

Benefit-risk assessment is used in various phases along the drug lifecycle, such as marketing authorization and surveillance, health technology assessment (HTA), and clinical decisions, to understand whether, and for which patients, a drug has a favorable or more valuable profile with reference to one or more comparators. Such assessments are inherently preference-based as several clinical and nonclinical outcomes of varying importance might act as evaluation criteria, and decision makers must establish acceptable trade-offs between these outcomes. Different healthcare stakeholder perspectives, such as those from patients and healthcare professionals, are key for informing benefit-risk trade-offs. However, the degree to which such preferences inform the decision is often unclear as formal preference-based evaluation frameworks are generally not used for regulatory decisions, and, if used, rarely communicated in HTA decisions. We argue that for better decisions, as well as for reasons of transparency, preferences in benefit-risk decisions should more often be quantified and communicated explicitly

Net clinical benefit of antiplatelet therapy was affected by patient preferences:A personalized benefit-risk assessment

Objectives: To assess the effect of patient preferences on the net clinical benefit (NCB) of an antiplatelet therapy for the secondary prevention of cardiovascular complications. Study Design and Setting: Risk equations were developed to estimate the individual predicted risk of key outcomes of antiplatelet treatment in patients with a prior myocardial infarction using the Clinical Practice Research Datalink linked to the Hospital Episode Statistics and UK Office of National Statistics databases. Patient preferences for outcomes of antiplatelet therapies were elicited in a separate discrete choice experiment survey. Trial hazard ratios, relative to placebo, were used to calculate the per-patient NCB using equal or preference weighting of outcomes. Results: Risk equations were estimated using 31,941 adults in the Clinical Practice Research Datalink population, of which 22,125 were included in the benefit-risk assessment. The mean NCB was lower in the preference-weighted than in the equal-weighted analysis (0.040 vs. 0.057; P < 0.0001), but the direction of effect was unchanged by the weighting. In analyses stratified by the presence of bleeding risk factors, including preference weighting altered the ranking of subgroups by NCB. Conclusion: Patient preference weighting may have a significant effect on NCB and should be included in personalized benefit-risk assessments

Maintenance inhaler therapy preferences of patients with asthma or chronic obstructive pulmonary disease:a discrete choice experiment

Background A variety of maintenance inhaler therapies are available to treat asthma and COPD. Patient-centric treatment choices require understanding patient preferences for the alternative therapies. Methods A self-completed web-based discrete choice experiment was conducted to elicit patient preferences for inhaler device and medication attributes. Selection of attributes was informed by patient focus groups and literature review. Results The discrete choice experiment was completed by 810 patients with asthma and 1147 patients with COPD. Patients with asthma most valued decreasing the onset of action from 30 to 5 min, followed by reducing yearly exacerbations from 3 to 1. Patients with COPD most and equally valued decreasing the onset of action from 30 to 5 min and reducing yearly exacerbations from 3 to 1. Both patients with asthma and patients with COPD were willing to accept an additional exacerbation in exchange for a 15 min decrease in onset of action and a longer onset of action in exchange for a lower risk of adverse effects from inhaled corticosteroids. Patients with asthma and COPD valued once-daily over twice-daily dosing, pressurised inhalers over dry powder inhalers and non-capsule priming over single-use capsules, although these attributes were not valued as highly as faster onset of action or reduced exacerbations. Conclusions The most important maintenance inhaler attributes for patients with asthma and COPD were fast onset of symptom relief and a lower rate of exacerbations. Concerns about safety of inhaled corticosteroids and device convenience also affected patient preferences but were less important