The Influence of Genotype Information on Psychiatrists' Treatment Recommendations: More Experienced Clinicians Know Better What to Ignore

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.

METHODS

An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.

RESULTS

Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.

CONCLUSIONS

Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

Attribute nonattendance in COVID-19 vaccine choice: A discrete choice experiment based on Chinese public preference

Objectives

The global coronavirus disease 2019 (COVID‐19) pandemic has not been well controlled, and vaccination could be an effective way to prevent this pandemic. By accommodating attribute nonattendance (ANA) in a discrete choice experiment (DCE), this paper aimed to examine Chinese public preferences and willingness to pay (WTP) for COVID‐19 vaccine attributes, especially the influence of ANA on the estimated results.

Methods

A DCE was designed with four attributes: effectiveness, protection period, adverse reactions and price. A random parameter logit model with an error component (RPL‐EC) was used to analyse the heterogeneity of respondents' preferences for COVID‐19 vaccine attributes. Two equality constraint latent class (ECLC) models were used to consider the influence of ANA on the estimated results in which the ECLC‐homogeneity model considered only ANA and the ECLC‐heterogeneity model considered both ANA and preference heterogeneity.

Results

Data from 1,576 samples were included in the analyses. Effectiveness had the highest relative importance, followed by adverse reactions and protection period, which were determined by the attributes and levels presented in this study. The ECLC‐heterogeneity model improved the goodness of fit of the model and obtained a lower probability of ANA. In the ECLC‐heterogeneity model, only a small number of respondents (29.09%) considered all attributes, and price was the most easily ignored attribute (64.23%). Compared with the RPL‐EC model, the ECLC‐homogeneity model obtained lower WTPs for COVID‐19 vaccine attributes, and the ECLC‐heterogeneity model obtained mixed WTP results. In the ECLC‐heterogeneity model, preference group 1 obtained higher WTPs, and preference groups 2 and 3 obtained lower WTPs.

Conclusions

The RPL‐EC, ECLC‐homogeneity and ECLC‐heterogeneity models obtained inconsistent WTPs for COVID‐19 vaccine attributes. The study found that the results of the ECLC‐heterogeneity model considering both ANA and preference heterogeneity may be more plausible because ANA and low preference may be confused in the ECLC‐homogeneity model and the RPL‐EC model. The results showed that the probability of ANA was still high in the ECLC‐heterogeneity model, although it was lower than that in the ECLC‐homogeneity model. Therefore, in future research on DCE (such as the field of vaccines), ANA should be considered as an essential issue.

Public Contribution

Chinese adults from 31 provinces in mainland China participated in the study. All participants completed the COVID‐19 vaccine choice questions generated through the DCE design.

Acceptability of Pharmacogenetic Testing among French Psychiatrists, a National Survey

Psychiatric disorder management is based on the prescription of psychotropic drugs. Response to them remains often insufficient and varies from one patient to another. Pharmacogenetics explain part of this variability. Pharmacogenetic testing is likely to optimize the choice of treatment and thus improve patients’ care, even if concerns and limitations persist. This practice of personalized medicine is not very widespread in France. We conducted a national survey to evaluate the acceptability of this tool by psychiatrists and psychiatry residents in France, and to identify factors associated with acceptability and previous use. The analysis included 397 observations. The mean acceptability score was 10.70, on a scale from 4 to 16. Overall acceptability score was considered as low for 3.0% of responders, intermediate for 80.1% and high for 16.9%. After regression, the remaining factors influencing acceptability independently of the others were prescription and training history and theoretical approach. The attitude of our population seems to be rather favorable, however, obvious deficiencies have emerged regarding perceived skills and received training. Concerns about the cost and delays of tests results also emerged. According to our survey, one of the keys to overcoming the barriers encountered in the integration of pharmacogenetics seems to be the improvement of training and the provision of information to practitioners.

Approaches to assessing the provider experience with clinical pharmacogenomic information: a scoping review

PURPOSE

Barriers to the implementation of pharmacogenomics in clinical practice have been thoroughly discussed over the past decade.

METHODS

The objective of this scoping review was to characterize the peer-reviewed literature surrounding the experiences and actions of prescribers, pharmacists, or genetic counselors when using pharmacogenomic information in real-world or hypothetical research settings.

RESULTS

A total of 33 studies were included in the scoping review. The majority of studies were conducted in the United States (70%), used quantitative or mixed methods (79%) with physician or pharmacist respondents (100%). The qualitative content analysis revealed five major methodological approaches: hypothetical clinical case scenarios, real-world studies evaluating prescriber response to recommendations or alerts, cross-sectional quantitative surveys, cross-sectional qualitative surveys/interviews, and a quasi-experimental real-world study.

CONCLUSION

The findings of this scoping review can guide further research on the factors needed to successfully integrate pharmacogenomics into clinical care.

Patients' preferences for delaying metastatic castration-resistant prostate cancer: Combining health state and treatment valuation

PURPOSE

Men with castration-resistant prostate cancer (CRPC) experience disease progression at different rates. The purpose of this study was to quantify the strength of patient preferences for delaying prostate cancer progression utilizing a discrete choice experiment (DCE) and valuing 3 health states in the continuum of CRPC.

PATIENTS AND METHODS

Men with CRPC, recruited from US patient panels, completed a cross-sectional web-based survey. The survey consisted of vignette-based time trade-off and a DCE designed to quantify patients' willingness to pay to delay metastatic CRPC. Three health states were presented: (1) living with non-metastatic castration-resistant prostate cancer (nmCRPC) (2) living with metastatic CRPC (mCRPC) before chemotherapy, and (3) living with mCRPC either on or after chemotherapy. The DCE consisted of 15 hypothetical choices with attributes characterizing CRPC (pain, fatigue, out of pocket cost, dosing, and time until cancer metastasizes). Patients' willingness to pay for changes in each attribute were derived.

RESULTS

A total of 176 patients with CRPC were surveyed (mean age: 64.2 years; 74% nmCRPC). Patients valued the nmCRPC health state (0.865) significantly higher than mCRPC before chemotherapy (0.743) or mCRPC on or after chemotherapy (0.476), both P < 0.001. In the DCE, patient treatment valuation was most affected by increasing the number of months until cancer metastasized; patients were willing to pay an additional $682 per month to delay time to metastases from 6 to 24 months (95% Confidence Interval: $387-$977) and additional $1,041 per month to delay time to metastasis to 48 months (95% Confidence Interval: $591-$1,490).

CONCLUSIONS

The results of this study demonstrated men with CRPC place significant value on delaying metastases. This study represents the first time 2 stated preference methods, time trade-off and DCE, were used together to understand patients' preferences and valuation of health states in CRPC.

Scientific challenges for precision public health

The notion of ‘precision’ public health has been the subject of much debate, with recent articles coming to its defence following the publication of several papers questioning its value.

Critics of precision public health raise the following problems and questionable assumptions: the inherent limits of prediction for individuals; the limits of approaches to prevention that rely on individual agency, in particular the potential for these approaches to widen inequalities; the undue emphasis on the supposed new information contained in individuals’ molecules and their ‘big data’ at the expense of their own preferences for a particular intervention strategy and the diversion of resources and attention from the social determinants of health.

In order to refocus some of these criticisms of precision public health as scientific questions, this article outlines some of the challenges when defining risk for individuals; the limitations of current theory and study design for precision public health; and the potential for unintended harms.