First and Foremost Battle the Virus: Eliciting Patient Preferences in Antiviral Therapy for Hepatitis C Using a Discrete Choice Experiment

Patient-based benefit-risk assessment of medicines: development, refinement, and validation of a content search strategy to retrieve relevant studies

Introduction:

Poor indexing and inconsistent use of terms and keywords may prevent efficient retrieval of studies on the patient-based benefit-risk assessment (BRA) of medicines. We aimed to develop and validate an objectively derived content search strategy containing generic search terms that can be adapted for any search for evidence on patient-based BRA of medicines for any therapeutic area.

Methods:

We used a robust multistep process to develop and validate the content search strategy: (1) we developed a bank of search terms derived from screening studies on patient-based BRA of medicines in various therapeutic areas, (2) we refined the proposed content search strategy through an iterative process of testing sensitivity and precision of search terms, and (3) we validated the final search strategy in PubMed by firstly using multiple sclerosis as a case condition and secondly computing its relative performance versus a published systematic review on patient-based BRA of medicines in rheumatoid arthritis.

Results:

We conceptualized a final search strategy to retrieve studies on patient-based BRA containing generic search terms grouped into two domains, namely the patient and the BRA of medicines (sensitivity 84%, specificity 99.4%, precision 20.7%). The relative performance of the content search strategy was 85.7% compared with a search from a published systematic review of patient preferences in the treatment of rheumatoid arthritis. We also developed a more extended filter, with a relative performance of 93.3% when compared with a search from a published systematic review of patient preferences in lung cancer.

Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C

INTRODUCTION

Hepatitis C, which affects 71 million persons worldwide, is the most common blood-borne pathogen in the United States. Chronic infections can be treated effectively thanks to the availability of modern direct-acting antiviral (DAA) therapies. Real-world data on the duration of DAA therapy, which can be used to optimize and guide the course of therapy, may also be useful in determining quality of life enhancements based upon total required supply of medication and long-term improvements to quality of life. We developed a machine learning model to identify patient characteristics associated with prolonged DAA treatment duration.

METHODS

A nationwide U.S. commercial managed care plan with claims data that covers about 60 million beneficiaries from 2009 to 2019 were used in the retrospective study. We examined differences in age, gender, and multiple comorbidities among patients treated with different durations of DAA treatment. We also examined the performance of machine learning models for predicting a prolonged course of DAA based on the area under the receiver operating characteristic curve (AUC).

RESULTS

We identified 3943 cases with hepatitis C who received sofosbuvir/ledipasvir as the first course of DAA and were eligible for the study. Patients receiving prolonged treatment (n = 240, 6.1%) were more likely to have compensated cirrhosis, decompensated cirrhosis, and other comorbidities (P < 0.001). For distinguishing patients who received prolonged DAA treatment for hepatitis C from patients received standard treatment, the optimal predictive model, constructed with XGBoost, had an AUC of 0.745 ± 0.031 (P < 0.001).

CONCLUSIONS

The risk of antiviral resistance and the cost of DAA are strong motivators to ensure that first-round DAA therapy is effective. For the dominant DAA treatment during the course of this analysis, we present a model that identifies factors already captured in established guidelines and adds to those age, comorbidity burden, and type 2 diabetes status; patient characteristics that are predictive of extended treatment.

Improving Patient Preference Elicitation by Applying Concepts From the Consumer Research Field: Narrative Literature Review

BACKGROUND

Although preference research finds its origins in consumer research, preference elicitation methods have increasingly attracted attention in different decision-making contexts in health care. Simulating real-life decision making is believed to be important during consumer preference elicitation.

OBJECTIVE

The aims of this study were to compare the process of decision making between patients and consumers and to identify methods from the consumer research field that could be applied in patient preference elicitation.

METHODS

A narrative literature review was performed to identify preference elicitation concepts from a consumer context that could offer improvements in health care.

RESULTS

The process of decision making between patients and consumers was highly comparable. The following five concepts from the consumer research field that could effectively simulate a real-life decision-making process for applications in health care were identified: simulating alternatives, self-reflection, feedback-driven exploration, separated (adaptive) dual response, and arranging profiles in blocks.

CONCLUSIONS

Owing to similarities in the decision-making process, patients could be considered as a subgroup of consumers, suggesting that preference elicitation concepts from the consumer field may be relevant in health care. Five concepts that help to simulate real-life decision making have the potential to improve patient preference elicitation. However, the extent to which real decision-making contexts can be mimicked in health care remains unknown.

Preferences of Patients with Chronic Hepatitis B - A Discrete Choice Experiment on the Acceptability of Functional Cure

BACKGROUND

Current antiviral therapies for chronic hepatitis B (CHB) rarely achieve functional cure, thus often requiring lifelong therapy. A therapy achieving functional cure in a significant percentage of patients could change the treatment landscape substantially. However, the acceptability of functional cure by patients is unknown, especially if associated with additional treatment burden.

METHODS

A Discrete Choice Experiment (DCE) including patients with CHB was performed between 2018 and 2019 in Germany. Patient inclusion criteria were confirmed CHB; age of at least 18 years; no history of hepatocellular carcinoma; no HIV or HCV/HDV co-infection. The final DCE included the following attributes: route of administration (oral administration by tablets; subcutaneous injection + tablets; intramuscular electroporation + tablets), side effect frequency (0/1/3 days per month), functional cure (1%/30%/50% of patients), frequency of physician visits (monthly, half-yearly) and travel time to treating physician (15/45 min).

RESULTS

The main analysis sample consisted of 108 patients with CHB (mean age: 49.1 years, female: 37.0%, average time since CHB diagnosis: 14.0 years, 52.8% with Hepatitis B surface antigen (HBsAg) chronic HBV infection). High efficacy was found to be the main driver of decisions for/against the presented treatment options (impacted 57% of patients' decisions), followed by therapy regimen (17%), safety profile (12%) and number of physician visits (11%). Latent class analysis revealed first insights into different decision patterns, with age, gender and previous side-effect experience affecting patients' decisions.

CONCLUSION

In comparison to all other treatment-related attributes such as therapy regimen or safety profile, patients with CHB showed a strong preference towards a scenario where a substantial number of patients benefit from sustained disease remission, which mimics functional cure.

Assessing Patient Preferences for Treatment Decisions for New Direct Acting Antiviral (DAA) Therapies for Chronic Hepatitis C Virus Infections

INTRODUCTION

The new direct acting antiviral (DAA) therapies are able to effectively treat chronic hepatitis C (CHC). This study elicited the preferences of CHC patients for treatment attributes of new DAAs.

METHODS

An online discrete choice experiment survey was designed to collect data from adult CHC patients in the USA, UK, France, Germany, Spain, and Italy. Patients were asked to choose from alternative hypothetical DAA options, defined by differing levels of nine attributes [i.e., treatment duration, tablet count and packaging, cure rate, required office visits when on treatment, modifications to statins or to proton pump inhibitors (PPIs), and risks of diarrhea, headache and nausea]. Logistic regression was used to assess preference for the treatment options.

RESULTS

A total of 328 patients with CHC completed the survey (USA, n = 227; European countries, n = 101), with a mean age of 47.7 years (SD = 14.4) and an average 11.2 years since CHC diagnosis; 51% of patients were female. More than half (60%) of the patients had treatment for CHC. Patients significantly preferred a DAA regimen with higher cure rate, shorter treatment duration, lower risks of diarrhea, headache, and nausea (all p < 0.001), reduced need for office visits when on treatment (p = 0.044), and without requiring dose reduction or timing change in PPIs (p = 0.032). Tablet counts were not found to be statistically significant.

CONCLUSION

Given the overall high cure rates of new DAAs, CHC patients' preferences for therapy may be influenced by treatment attributes other than cure rates and tolerability. Treatments that are more convenient and require less disruption to their daily life (e.g., shorter treatment duration, no modification in PPI use, and fewer office visits when on treatment) are important to patients with CHC and should be considered when making treatment decisions.

FUNDING

AbbVie.

Direct-acting antiviral treatment for HIV/HCV patients in safety net settings: patient and provider preferences

HIV/HCV coinfected patients are a priority for direct acting antiretroviral (DAA) treatment, yet barriers to treating vulnerable patients persist. This study surveyed safety net clinic patients and providers to quantify their preferences for DAA treatment and prioritize modifiable barriers. Preferences were assessed using best-worst scaling. General linear mixed models were used to determine whether attributes differed in importance and whether patients and providers valued attributes differently. 158 HIV/HCV coinfected patients and 49 providers participated. Patients and providers had strong preferences for treatment within the medical homes where patients receive HIV care. Support such as reminders and advice numbers were also important, but were more important to providers than patients. Providers identified lack of insurance coverage for DAA as the most significant barrier. Providers rated HIV primary care providers as best suited to deliver DAA to HIV+ patients. Addressing structural barriers is essential for increasing DAA treatment in safety net settings.

Attribute level overlap (and color coding) can reduce task complexity, improve choice consistency, and decrease the dropout rate in discrete choice experiments

A randomized controlled discrete choice experiment (DCE) with 3,320 participating respondents was used to investigate the individual and combined impact of level overlap and color coding on task complexity, choice consistency, survey satisfaction scores, and dropout rates. The systematic differences between the study arms allowed for a direct comparison of dropout rates and cognitive debriefing scores and accommodated the quantitative comparison of respondents' choice consistency using a heteroskedastic mixed logit model. Our results indicate that the introduction of level overlap made it significantly easier for respondents to identify the differences and choose between the choice options. As a stand-alone design strategy, attribute level overlap reduced the dropout rate by 30%, increased the level of choice consistency by 30%, and avoided learning effects in the initial choice tasks of the DCE. The combination of level overlap and color coding was even more effective: It reduced the dropout rate by 40% to 50% and increased the level of choice consistency by more than 60%. Hence, we can recommend attribute level overlap, with color coding to amplify its impact, as a standard design strategy in DCEs.

Discrete Choice Experiments in Health Economics: Past, Present and Future

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990-2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges.

METHODS

A systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated.

RESULTS

Of the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels.

CONCLUSIONS

The use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers' confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research.

The Value of Treatment Processes in Germany: A Discrete Choice Experiment on Patient Preferences in Complementary and Conventional Medicine

BACKGROUND

The effects of health interventions are often complex, and it is argued that they comprise more than pure changes in clinical parameters. Aspects of the treatment process, so-called 'benefits beyond health', are often overlooked in the evaluation of health interventions but can be of value to the patients.

OBJECTIVES

The aim of this study was to assess patients' preferences and willingness to pay regarding the treatment process and its attributes in patients using acupuncture, homeopathy or general medicine (GM).

METHODS

A systematic literature search, six semi-structured interviews and a stakeholder involvement were conducted to determine the attributes of the treatment process. Five process attributes and one cost attribute were used to construct the experimental design of the discrete choice experiment (DCE) (6 × 3), a cross sectional survey method. Patients were recruited by outpatient physicians practicing in Berlin and Munich, Germany. Process attributes were effects-coded. Data were analyzed in a conditional logit regression.

RESULTS

Data from 263 patients were analyzed. DCE results showed that the treatment process attributes 'active listening' and 'time' were most relevant to all patients. Preferences for the attributes 'holistic treatment' (more relevant to the acupuncture and homeopathy groups) and 'information' (more relevant to the GM group) seemed to differ slightly between the groups. Willingness-to-pay values were higher in the acupuncture and homeopathy groups.

CONCLUSIONS

The time physicians take and the extent to which they listen attentively are most important and are equally important to all patients. These results may contribute to the debate about more patient-centered healthcare. They support a strengthening of medical consultations in the German healthcare system. We suggest giving physicians the opportunity to spend more time with their patients, which may be achieved by changing the general conditions of remuneration (e.g., improved reimbursement of medical consultations).

GERMAN CLINICAL TRIAL REGISTER

DRKS00013160.

Effect of Level Overlap and Color Coding on Attribute Non-Attendance in Discrete Choice Experiments

OBJECTIVE

The aim of this study was to test the hypothesis that level overlap and color coding can mitigate or even preclude the occurrence of attribute nonattendance in discrete choice experiments.

METHODS

A randomized controlled experiment with five experimental study arms was designed to investigate the independent and combined impact of level overlap and color coding on respondents' attribute nonattendance. The systematic differences between the study arms allowed for a direct comparison of observed dropout rates and estimates of the average number of attributes attended to by respondents, which were obtained by using augmented mixed logit models that explicitly incorporated attribute non-attendance.

RESULTS

In the base-case study arm without level overlap or color coding, the observed dropout rate was 14%, and respondents attended, on average, only two out of five attributes. The independent introduction of both level overlap and color coding reduced the dropout rate to 10% and increased attribute attendance to three attributes. The combination of level overlap and color coding, however, was most effective: it reduced the dropout rate to 8% and improved attribute attendance to four out of five attributes. The latter essentially removes the need to explicitly accommodate for attribute non-attendance when analyzing the choice data.

CONCLUSIONS

On the basis of the presented results, the use of level overlap and color coding are recommendable strategies to reduce the dropout rate and improve attribute attendance in discrete choice experiments.

The Probabilistic Efficiency Frontier: A Framework for Cost-Effectiveness Analysis in Germany Put into Practice for Hepatitis C Treatment Options

BACKGROUND

The German Institute for Quality and Efficiency in Health Care (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen) adapted the efficiency frontier (EF) approach to conform to statutory provisions on cost-effectiveness analysis of health technologies. EF serves as a framework for evaluating cost-effectiveness and indirectly for pricing and reimbursement decisions.

OBJECTIVES

To calculate an EF on the basis of single multidimensional benefit by taking patient preferences and uncertainty into account; to evaluate whether EF is useful to inform decision makers about cost-effectiveness of new therapies; and to find whether a treatment is efficient at given prices demonstrated through a case study on chronic hepatitis C.

METHODS

A single multidimensional benefit was calculated by linear additive aggregation of multiple patient-relevant end points. End points were identified and weighted by patients in a previous discrete-choice experiment (DCE). Aggregation of overall benefit was ascertained using preferences and clinical data. Monte-Carlo simulation was applied. Uncertainty was addressed by price acceptability curve (PAC) and net monetary benefit (NMB).

RESULTS

The case study illustrates that progress in benefit and efficiency of hepatitis C virus treatments could be depicted very well with the EF. On the basis of cost, effect, and preference data, the latest generations of interferon-free treatments are shown to yield a positive NMB and be efficient at current prices.

CONCLUSIONS

EF was implemented taking uncertainty into account. For the first time, a DCE was used with the EF. The study shows how DCEs in combination with EF, PAC, and NMB can contribute important information in the course of reimbursement and pricing decisions.