Time-to-event versus ten-year-absolute-risk in cardiovascular risk prevention - does it make a difference? Results from the Optimizing-Risk-Communication (OptRisk) randomized-controlled trial.

BACKGROUND: The concept of shared-decision-making is a well-established approach to increase the participation of patients in medical decisions. Using lifetime risk or time-to-event (TTE) formats has been increasingly suggested as they might have advantages, e.g. in younger patients, to better show consequences of unhealthy behaviour. In this study, the most-popular ten-year risk illustration in the decision-aid-software arribaTM (emoticons), is compared within a randomised trial to a new-developed TTE illustration, which is based on a Markov model. METHODS: Thirty-two General Practitioners (GPs) took part in the study. A total of 304 patients were recruited and counseled by their GPs with arribaTM, and randomized to either the emoticons or the TTE illustration, followed by a patient questionnaire to figure out the degree of shared-decision-making (PEF-FB9, German questionnaire to measure the participation in the shared decision-making process, primary outcome), as well as the decisional conflict, perceived risk, accessibility and the degree of information, which are all secondary outcomes. RESULTS: Regarding our primary outcome PEF-FB9 the new TTE illustration is not inferior compared to the well-established emoticons taking the whole study population into account. Furthermore, the non-inferiority of the innovative TTE could be confirmed for all secondary outcome variables. The explorative analysis indicates even advantages in younger patients (below 46 years of age). CONCLUSION: The TTE format seems to be as useful as the well-established emoticons. For certain patient populations, especially younger patients, the TTE may be even superior to demonstrate a cardiovascular risk at early stages. Our results suggest that time-to-event illustrations should be considered for current decision support tools covering cardiovascular prevention. TRIAL REGISTRATION: The study was registered at the German Clinical Trials Register and at the WHO International Clinical Trials Register Platform ( ICTRP, ID DRKS00004933 ); registered 2 February 2016 (retrospectively registered).

Cost-effectiveness of PCV20 to Prevent Pneumococcal Disease in the Pediatric Population: A German Societal Perspective Analysis.

INTRODUCTION: Since 2009, a pneumococcal conjugate vaccine (PCV) covering 13 serotypes (PCV13) has been included by Germany's Standing Committee on Vaccinations for infants, resulting in major reductions in pneumococcal disease (PD). Higher-valent vaccines may further reduce PD burden. This cost-effectiveness analysis compared 20-valent PCV (PCV20) under a 3+1 schedule with 15-valent PCV (PCV15) and PCV13, both under 2+1 schedule, in Germany's pediatric population. METHODS: A Markov model with annual cycles over a 10-year time horizon was adapted to simulate the clinical and economic impact of pediatric vaccination with PCV20 versus lower-valent PCVs in Germany. The model used PCV13 clinical effectiveness and impact studies as well as PCV7 efficacy studies for vaccine direct and indirect effect estimates. Epidemiologic, utility, and medical cost inputs were obtained from published sources. Benefits and costs were discounted at 3% from a German societal perspective. Outcomes included PD cases, deaths, costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). RESULTS: In the base case, PCV20 provided greater health benefits than PCV13, averting more cases of invasive pneumococcal disease (IPD; 15,301), hospitalized and non-hospitalized pneumonia (460,197 and 472,365, respectively), otitis media (531,634), and 59,265 deaths over 10 years. This resulted in 904,854 additional QALYs and a total cost saving of €2,393,263,611, making PCV20 a dominant strategy compared with PCV13. Compared to PCV15, PCV20 was estimated to avert an additional 11,334 IPD, 704,948 pneumonia, and 441,643 otitis media cases, as well as 41,596 deaths. PCV20 was associated with a higher QALY gain and lower cost (i.e., dominance) compared with PCV15. The robustness of the results was confirmed through scenario analyses as well as deterministic and probabilistic sensitivity analyses. CONCLUSION: PCV20 3+1 dominated both PCV13 2+1 and PCV15 2+1 over 10 years. Replacing lower-valent PCVs with PCV20 would result in greater clinical and economic benefits, given PCV20's broader serotype coverage.

Pneumococcal diseases (e.g., ear infections, pneumonia, bloodstream infections) are among the leading causes of illness and death in children worldwide. The pneumococcal conjugate vaccine protects against pneumococcal diseases and has significantly reduced the number of newly diagnosed cases. Higher-valent vaccines (which provide coverage for a greater number of disease-causing serotypes) have recently received European Commission approval for use in adults and  children. This study examined costs and health benefits associated with the 20-valent pneumococcal conjugate vaccine (PCV20) under a 3+1 (i.e., three primary doses and one booster dose) schedule in Germany's childhood vaccination program compared with 13-valent pneumococcal conjugate vaccine (PCV13) and the 15-valent pneumococcal conjugate vaccine (PCV15), both under a 2+1 (two primary doses, one booster) schedule. PCV20 was estimated to result in greater health benefits from avoiding more cases in pneumococcal diseases and lower costs compared with both PCV13 and PCV15. PCV20, therefore, is considered the best option among the three vaccines for children in Germany.

Cost-effectiveness of use of 20-valent pneumococcal conjugate vaccine among adults in Germany.

OBJECTIVES: Despite national recommendations for use of pneumococcal vaccines, rates of community-acquired pneumonia (CAP) and invasive pneumococcal disease (IPD) remain high in Germany. New pneumococcal conjugate vaccines (PCVs) with expanded coverage have the potential to reduce the pneumococcal disease burden among adults. METHODS: Using a Markov model, we evaluated the lifetime outcomes/costs comparing 20-valent PCV (PCV20) with standard of care (SC) vaccinations for prevention of CAP and IPD among adults aged ≥60 years and at-risk adults aged 18-59 years in Germany. PCV20 also was compared with sequential vaccination with 15-valent PCV (PCV15) followed by PPSV23 in a scenario analysis. RESULTS: Over the course of a lifetime (82 years), use of PCV20vs. SC would prevent 54,333 hospitalizations, 26368 outpatient CAP cases, 10946 disease-related deaths yield 74,694 additional life-years (LYs), while lowering total medical costs by 363.2 M €. PCV20 remained cost saving (i.e. dominant) versus SC even in numerous sensitivity analyses, including a sensitivity analysis assuming moderate effectiveness of the SC pneumococcal polysaccharide vaccine against noninvasive pneumococcal CAP. In several scenario analyses and a probabilistic sensitivity analysis, PCV20 was also cost-saving compared toPCV15 PPSV23 vaccination. CONCLUSIONS: One dose of PCV20 among adults aged ≥60 years and adults aged 18-59 years with moderate- and high-risk conditions wouldsubstantially reduce pneumococcal disease, save lives, and be cost saving compared with SC.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review.

Objective: The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques. Methods: For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary. Results: Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data. Conclusion: Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science.

Objectives: Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience. Methods: To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends. Results: Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased. Conclusions: Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Familial hypercholesterolemia: A systematic review of modeling studies on screening interventions.

BACKGROUND AND AIMS: FH is still underdiagnosed. Cost-effectiveness results of preventive screening strategies vary. We aimed at systematically assessing the benefits, harms and cost effectiveness of screening for familial hypercholesterolemia (FH) and at providing an overview of the main characteristics and methodological approaches of applied decision-analytic models. METHODS: A systematic literature search was conducted in MEDLINE, EconLit, CRD-databases and the CEA-registry for FH screening starting 2012. Earlier studies were included from a published systematic review. Results were reported in standardized semi-quantitative evidence tables. Costs were converted to current euros. Incremental cost-effectiveness ratios (ICERs) were recalculated according to economic guidelines. RESULTS: Out of our 211 retrieved studies, eight were included in the review in addition to six studies from an earlier review. Studies were conducted in Europe (UK, The Netherlands, Spain, Poland), USA and Australia evaluating cascade (CS), opportunistic (OS), universal screening (UniS), or combinations using genetic testing, clinical criteria or combinations. Studies evaluating only CS identified strategies with an ICER of up to 37,100 EUR/quality-adjusted life-year (QALY) but some strategies were dominated depending on test combinations. UniS of newborns in combination with CS had an ICER≤15,000 EUR/QALY for sequential cholesterol-genetic screening. In other studies, UniS was dominated by OS/CS. CONCLUSIONS: Our systematic review demonstrates the values of FH screening and provides an overview of potentially relevant screening strategies to be tested using a decision-analytic model for the respective country or region. Future research is needed on the transferability of results to other countries and modeling spillover effects to newborns.

A microsimulation of the cost-effectiveness of maraviroc for antiretroviral treatment-experienced HIV-infected individuals.

PURPOSE: Maraviroc (MVC) is the first approved CCR5 antagonist. The aim of this study was to explore the cost-effectiveness of MVC in treatment-experienced or treatment-resistant HIV-infected adults. METHODS: The validated HIV microsimulation model ARAMIS was used to predict clinical and economic outcomes of treating patients with optimized background therapy (OBT) alone, as compared to a strategy of testing for the patient's viral tropism and treating with OBT with or without (+/-) MVC in a cohort corresponding to the MOTIVATE screening cohort. RESULTS: Compared to treatment with OBT alone, a treatment strategy of OBT +/- MVC (twice daily) according to tropism test result was predicted to increase CD4+ cell count after 5 years (from mean 249 to 360 cells/microL), undiscounted life expectancy (7.6 to 8.9 years), and quality-adjusted life years (QALYs; from 4.99 to 5.71) for an additional $40,500, giving an incremental cost-effectiveness ratio of $56,400 per QALY gained. The result was relatively insensitive to alternative clinical and cost assumptions within reasonable ranges, but for individuals with HIV susceptible to only two or fewer components of OBT, the ICER decreased to $52,000 per QALY gained. CONCLUSION: MVC is cost-effective, especially among individuals with few remaining options for active antiretroviral therapy.

Cost of transfusion-dependent myelodysplastic syndrome (MDS) from a German payer's perspective.

No curative treatment exists for patients with myelodysplastic syndrome (MDS) besides allogeneic stem cell transplantation. Hence, palliative treatment is provided for a life time accruing high health care cost. As no study in cost of MDS exists in Germany, the objective of this study was to assess and analyze costs of transfusion-dependent low/intermediate-1-risk MDS in Germany from a payers' perspective. From seven centers, 116 low/intermediate-1-risk transfusion-dependent MDS patients with and without isolated 5q-deletion were identified. Claims data and patient records of the previous 5 years were used to collect health care utilization data retrospectively. Publicly available tariff books and remuneration schemes were applied to evaluate mean costs per year in Euro with 2007 as base year. The annual cost of MDS patients was estimated at 14,883. Subgroup analyses showed differences in patient's characteristics and outcomes among patients treated at a hospital-based vs. an office-based setting. Patients treated at the hospital-based registry show higher cost, whereas the reasons for that still need to be detected. Overall, per annum direct costs range from 12,543 (SD 12,967) to 24,957 (SD 36,399) in different subgroups of patients. In both groups, patients with 5q-deletion use more medication than those without deletion. Mean costs for medication in the office-based setting are 5,902 for patients with isolated 5q-deletion vs. 3,932 for patients with no deletion, respectively. MDS leads to a high health care utilization and resulting costs for the health care system which requires a detailed analysis of underlying services.

Cost-effectiveness of maraviroc for antiretroviral treatment-experienced HIV-infected individuals in Mexico.

OBJECTIVE: Maraviroc is the first approved drug in a new class of antiretrovirals, the CCR5 antagonists. The objective of this study was to predict the long-term clinical impact and cost-effectiveness of maraviroc in treatment-experienced adults with HIV/AIDS in Mexico. METHODS: The AntiRetroviral Analysis by Monte Carlo Individual Simulation (ARAMIS) model was adapted to the Mexican context to predict clinical and economic outcomes of treating with optimized background therapy (OBT) versus testing for viral tropism status and treating with OBT ± maraviroc accordingly in treatment-experienced adults in Mexico. Baseline characteristics and efficacy were from the MOTIVATE trials' screening cohort. Costs and population mortality data were specific to Mexico. Results were reported from the perspective of health care payers in 2008 Mexican pesos (converted to 2008 US$ in parentheses). RESULTS: Compared to treatment with OBT alone, treatment with OBT ± maraviroc contingent on tropism test result increased projected undiscounted life expectancy and discounted quality-adjusted life expectancy from 7.54 to 8.71 years and 4.42 to 4.92 quality-adjusted life years (QALYs), respectively, at an incremental cost of $228,215 (US$21,329). The resultant incremental cost-effectiveness ratio (ICER) was $453,978 (US$42,429) per QALY gained. The ICER was somewhat lower when maraviroc was modeled in individuals susceptible to ≤ 2 components of OBT ($407,329; US$38,069), while the ICER was higher in individuals susceptible to ≥3 OBT components ($718,718; US$67,171). CONCLUSION: In treatment-experienced individuals with HIV/AIDS in Mexico, maraviroc may be cost-effective, particularly in individuals with limited options for active antiretroviral therapy (ART).

The Optimizing-Risk-Communication (OptRisk) randomized trial - impact of decision-aid-based consultation on adherence and perception of cardiovascular risk.

BACKGROUND: Shared decision-making is a well-established approach to increasing patient participation in medical decisions. Increasingly, using lifetime-risk or time-to-event (TTE) formats has been suggested, as these might have advantages in comparison with a 10-year risk prognosis, particularly for younger patients, whose lifetime risk for some events may be considerably greater than their 10-year risk. In this study, a randomized trial, the most popular 10-year risk illustration in the decision-aid software Arriba (emoticons), is compared with a newly developed TTE illustration, which is based on a Markov model. The study compares the effect of these two methods of presenting cardiovascular risk to patients on their subsequent adherence to intervention. METHODS: A total of 294 patients were interviewed 3 months after they had had a consultation with their GP on cardiovascular risk prevention. Adherence to behavioral change or medication intervention was measured as the primary outcome. The latter was expressed as a generated score. Furthermore, different secondary outcomes were measured, ie, patient perception of risk and self-rated importance of avoiding a cardiovascular event, as well as patient numeracy, which was used as a proxy for patient health literacy. RESULTS: Overall, no significant difference in patient adherence was found depending on risk representation. In the emoticon group, the number of interventions had a significant impact on the adherence score (P=0.025). Perception of risk was significantly higher in patients counseled with the TTE risk display, whereas the importance of avoiding a cardiovascular event was rated equally highly in both groups and actually increased over time. CONCLUSION: The TTE format is an appropriate means for counseling patients. Adherence is a very complex construct, which cannot be fully explained by our findings. The study results support our call for considering TTE illustrations as a valuable alternative to current decision-support tools covering cardiovascular prevention. Nevertheless, further research is needed to shed light on patient motivation and adherence with regard to cardiovascular risk prevention. TRIAL REGISTRATION: The study was registered at the German Clinical Trials Register and at the WHO International Clinical Trials Register Platform (ICTRP, ID DRKS00004933); registered February 2, 2016 (retrospectively registered).

Lessons Learned from a Cross-Model Validation between a Discrete Event Simulation Model and a Cohort State-Transition Model for Personalized Breast Cancer Treatment.

OBJECTIVES: Breast cancer is the most common malignancy among women in developed countries. We developed a model (the Oncotyrol breast cancer outcomes model) to evaluate the cost-effectiveness of a 21-gene assay when used in combination with Adjuvant! Online to support personalized decisions about the use of adjuvant chemotherapy. The goal of this study was to perform a cross-model validation. METHODS: The Oncotyrol model evaluates the 21-gene assay by simulating a hypothetical cohort of 50-year-old women over a lifetime horizon using discrete event simulation. Primary model outcomes were life-years, quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). We followed the International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM) best practice recommendations for validation and compared modeling results of the Oncotyrol model with the state-transition model developed by the Toronto Health Economics and Technology Assessment (THETA) Collaborative. Both models were populated with Canadian THETA model parameters, and outputs were compared. RESULTS: The differences between the models varied among the different validation end points. The smallest relative differences were in costs, and the greatest were in QALYs. All relative differences were less than 1.2%. The cost-effectiveness plane showed that small differences in the model structure can lead to different sets of nondominated test-treatment strategies with different efficiency frontiers. We faced several challenges: distinguishing between differences in outcomes due to different modeling techniques and initial coding errors, defining meaningful differences, and selecting measures and statistics for comparison (means, distributions, multivariate outcomes). CONCLUSIONS: Cross-model validation was crucial to identify and correct coding errors and to explain differences in model outcomes. In our comparison, small differences in either QALYs or costs led to changes in ICERs because of changes in the set of dominated and nondominated strategies.