MO4 - Using AHP weights to fill missing gaps in Markov decision models

OBJECTIVES:\ud We propose to combine the versatility of the analytic hierarchy process (AHP) with the decision-analytic sophistication of health-economic modeling in a new methodology for early technology assessment. As an illustration, we apply this methodology to a new technology to diagnose breast cancer.\ud \ud METHODS:\ud The AHP is a technique for multicriteria analysis, relatively new in the fi eld of technology assessment. It can integrate both quantitative and qualitative criteria in the assessment of alternative technologies. We applied the AHP to prioritize a more versatile set of outcome measures than most Markov models do. These outcome measures include clinical effectiveness and costs, but also weighted estimates of patient comfort and safety. Furthermore, as no clinical data are available for this technology yet, the AHP is applied to predict the performance of the new technology with regard to all these outcome measures. Results of the AHP are subsequently integrated in a Markov model to make an early assessment of the expected incremental cost-effectiveness of alternative technologies.\ud \ud RESULTS:\ud We systematically estimated priors on the clinical effectiveness and wider impacts of the new technology using AHP. In our illustration, AHP estimates for sensitivity and specifi city of the new diagnostic technology were used as probability parameters in the Markov model. Moreover, the prioritized outcome measures including clinical effectiveness (weight = 0.61), patient comfort (weight = 0.09), and safety (weight = 0.30) were integrated into one outcome measure in the Markov model.\ud \ud CONCLUSIONS:\ud Combining AHP and Markov modelling is particularly valuable in early technology assessment when evidence about the effectiveness of health care technology is still limited or missing. Moreover, combining these methods is valuable when decision makers are interested in other patient relevant outcomes measures besides the technology’s clinical effectiveness, and that may not (adequately or explicitly) be captured in mainstream utility measures

Empirical comparison of discrete choice experiment and best-worst scaling to estimate stakeholders' risk tolerance for hip replacement surgery

Objectives Empirical comparison of two preference elicitation methods, discrete choice experiment (DCE) and profile case best-worst scaling (BWS), regarding the estimation of the risk tolerance for hip replacement surgery (total hip arthroplasty and total hip resurfacing arthroplasty). Methods An online survey was constructed, following international guidelines, and consisted of socio-demographic questions and two randomised sections with 12 DCE and 8 BWS questions. The survey was sent to a general population who can be faced with choosing between THA and TRA (males between 45-65 years old) in the US. After an intensive literature search, the following attributes were selected: probability of a first and a second revision in seven years, pain relief, ability to perform moderate daily activities, and hospital stay. In addition, survey respondents rated the difficulty of each method and the time to complete each section was monitored. BWS and DCE data was analysed using conditional logit analysis. The maximum acceptable risk (MAR) for a revision was estimated for four different hypothetical hip replacement scenarios. Results The final data set consisted of 429 respondents. The MARs estimated for four hypothetical hip replacement scenarios differed between both methods, ranging from 0% to 19% difference for a first revision. BWS questions took significantly more time (401 s.) than DCE (228 s.) questions. And respondents found BWS more difficult to complete. Conclusions Both methods to elicit stakeholder preferences produce different results. Yet, both seem to be consistent in predicting risk tolerance if the benefits are changed. However, DCE seems to be more sensitive for a change in benefits and risks while the MAR estimates obtained through BWS have considerably lower uncertainty than DC

The influence of timing of radiation therapy following breast-conserving surgery on 10-year disease-free survival

Background: The Dutch guidelines advise to start radiation therapy (RT) within 5 weeks following breast-conserving surgery (BCS). However, much controversy exists regarding timing of RT. This study investigated its effect on 10-year disease-free survival (DFS) in a Dutch population-based cohort. Methods: All women diagnosed with primary invasive stage I-IIIA breast cancer in 2003 treated with BCS+RT were included. Two populations were studied. Population 1 excluded patients receiving chemotherapy before RT. Analyses were stratified for use of adjuvant systemic therapy (AST). Population 2 included patients treated with chemotherapy, and compared chemotherapy before (BCS-chemotherapy-RT) and after RT (BCS-RT-chemotherapy). DFS was estimated using multivariable Cox regression. Locoregional recurrence-free survival (LRRFS), distant metastasis-free survival (DMFS) and overall survival (OS) were secondary outcomes. Results: Population 1 (n=2759) showed better DFS and DMFS for a time interval of >55 than a time interval of <42 days. Patients treated with AST showed higher DFS for >55 days (hazards ratio (HR) 0.60 (95% confidence interval (CI): 0.38-0.94)) and 42-55 days (HR 0.64 (95% CI: 0.45-0.91)) than <42 days. Results were similar for DMFS, while timing did not affect LRRFS and OS. For patients without AST, timing was not associated with DFS, DMFS and LLRFS, but 10-year OS was significantly lower for 42-55 and >55 days compared to <42 days. In population 2 (n=1120), timing did not affect survival in BCS-chemotherapy-RT. In BCS-RT-chemotherapy, DMFS was higher for >55 than <42 days.Conclusions:Starting RT shortly after BCS seems not to be associated with a better long-term outcome. The common position that RT should start as soon as possible following surgery in order to increase treatment efficacy can be questioned

Incorporating MCDA into HTA: challenges and potential solutions, with a focus on lower income settings

Background: Multicriteria decision analysis (MCDA) has the potential to bring more structure and transparency to health technology assessment (HTA). The objective of this paper is to highlight key methodological and practical challenges facing the use of MCDA for HTA, with a particular focus on lower and middle-income countries (LMICs), and to highlight potential solutions to these challenges. Methodological challenges: Key lessons from existing applications of MCDA to HTA are summarized, including: that the socio-technical design of the MCDA reflect the local decision problem; the criteria set properties of additive models are understood and applied; and the alternative approaches for estimating opportunity cost, and the challenges with these approaches are understood. Practical challenges: Existing efforts to implement HTA in LMICs suggest a number of lessons that can help overcome the practical challenges facing the implementation of MCDA in LMICs, including: adapting inputs from other settings and from expert opinion; investing in technical capacity; embedding the MCDA in the decision-making process; and ensuring that the MCDA design reflects local cultural and social factors. Conclusion: MCDA has the potential to improve decision making in LMICs. For this potential to be achieved, it is important that the lessons from existing applications of MCDA are learned

Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

Early health technology assessment is increasingly being used to support health economic evidence development during early stages of clinical research. Such early models can be used to inform research and development about the design and management of new medical technologies to mitigate the risks, perceived by industry and the public sector, associated with market access and reimbursement. Over the past 25 years it has been suggested that health economic evaluation in the early stages may benefit the development and diffusion of medical products. Early health technology assessment has been suggested in the context of iterative economic evaluation alongside phase I and II clinical research to inform clinical trial design, market access, and pricing. In addition, performing early health technology assessment was also proposed at an even earlier stage for managing technology portfolios. This scoping review suggests a generally accepted definition of early health technology assessment to be “all methods used to inform industry and other stakeholders about the potential value of new medical products in development, including methods to quantify and manage uncertainty”. The present review also aimed to identify recent published empirical studies employing an early-stage assessment of a medical product. With most included studies carried out to support a market launch, the dominant methodology was early health economic modeling. Further methodological development is required, in particular, by combining systems engineering and health economics to manage uncertainty in medical product portfolios

A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions

The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health eco-nomic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appro-priate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles wer

mice: Multivariate Imputation by Chained Equations: 3.11.0

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations