The timing and probability of treatment switch under cost uncertainty: an application to patients with gastrointestinal stromal tumor

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

On the timing and probability of Presurgical Teledermatology: how it becomes the dominant strategy

Health level fluctuations make the outcome of any treatment option uncertain, so that decision-makers might have to wait for more information before optimally choosing treatments, especially when time spent in treatment cannot be fully recovered later in terms of health outcome. To examine whether or not, and when decision-makers should use presurgical teledermatology, a dynamic stochastic model is applied to patients waiting for dermatology surgical intervention. The theoretical model suggests that health uncertainty discourages using teledermatology. As teledermatology becomes less cost competitive, the uncertainty becomes more dominant. The results of the model were then tested empirically with the teledermatology network covering the area served by one Portuguese regional hospital, which links the primary care centers of 24 health districts with the hospital's dermatology department via the corporate intranet of the Portuguese healthcare system. Under uncertainty and irreversibility, presurgical teledermatology becomes the dominant strategy for younger patients and with lower probability of developing skin cancer.

Financial Estimation of the Uncertainty in Medicine Using Present Value of Medical Fees and a Mortality Risk Prediction Model: a Retrospective Cohort Study

This study aimed to develop a method to enable the financial estimation of each patient’s uncertainty without focusing on healthcare technology. We define financial uncertainty (FU) as the difference between an actual amount of claim (AC) and the discounted present value of the AC (DAC). DAC can be calculated based on a discounted present value calculated using a cash flow, a period of investment, and a discount rate. The present study considered these three items as AC, the length of hospital stay, and the predicted mortality rate. The mortality prediction model was built using typical data items in standard level electronic medical records such as sex, age, and disease information. The performance of the prediction model was moderate because an area under curve was approximately 85%. The empirical analysis primarily compares the FU of the top 20 diseases with the actual AC using a retrospective cohort in the University of Miyazaki Hospital. The observational period is 5 years, from April 1, 2013, to March 31, 2018. The analysis demonstrates that the proportion of FU to actual AC is higher than 20% in low-weight children, patients with leukemia, brain tumor, myeloid leukemia, or non-Hodgkin’s lymphoma. For these diseases, patients cannot avoid long hospitalization; therefore, the medical fee payment system should be designed based on uncertainty. Our method is both practical and generalizable because it uses a small number of data items that are required in standard electronic medical records. This method contributes to the decision-making processes of health policymakers.

Broadening the Concept of Value: A Scoping Review on the Option Value of Medical Technologies

OBJECTIVES

A recent debate in health economics and outcomes research community identified option value as one of the elements warranting consideration in the assessment of medical technologies. To conduct a scoping review of contributions on option value in the healthcare sector and identify relevant conceptual aspects and methods used to incorporate it in standard economic evaluations.

METHODS

A systematic search was conducted up to July 2020 to identify contributions from electronic bibliographic database and gray literature. Data on the proposed definitions of option value, theoretical implications of its use in economic evaluations, and methods used to estimate it were extracted and analyzed.

RESULTS

We found 57 eligible studies. Three different definitions emerged: insurance value, real option value, and option value of survival. Focusing on the latter (24 studies), we analyzed in depth 8 empirical applications across 7 therapeutic areas. The most relevant methodological challenges were on the perspective used in economic evaluations and how to robustly manage forecasting uncertainty, update cost-effectiveness thresholds, and avoid double-counting issues. For empirical studies assessing the total value of the technology, including option value, estimates ranged from +7% to +469% of its conventional value.

CONCLUSIONS

This review synthesizes theoretical and empirical aspects on option value of healthcare technologies and proposes a terminology to distinguish 3 different concepts identified. Future work should focus primarily on agreeing on whether option value should be included in economic evaluations and, if so, on developing and validating reliable methods for its ex-ante estimation.

HIV patients' decision of switching to second-line antiretroviral therapy in India

The objective is to examine when patients should switch to second-line antiretroviral therapy (ART) under health uncertainty and in the absence of viral load monitoring. We formalize and solve the therapeutic dilemma about whether or not, and when, to switch a therapy. The model's main value-added consists in the concrete application to patients with HIV in India. In our dynamic stochastic model, health level volatility can be understood as the variation in CD4 count and the trend of health level as increases in CD4 count and, thus, decreases in the incidence of opportunistic infections and mortality. The results of the empirical application suggest that the theoretical model can explain ART treatment switch. Treatment switch depends negatively on the volatility of patients' health, and on trend of health, i.e., the greater the variation in CD4 count and the more CD4 count increase, the fewer treatment switches one expects to occur. Treatment switch also depends negatively on the degree of irreversibility. Under irreversibility, low-risk patients must begin the second-line treatment as soon as possible, which is precisely when the second-line treatment is least valuable. The existence of an option value means that ART first-line regimen may be the better choice when considering lifetime welfare. Conversely, treatment switch depends positively on the discount rate and on the correlation between the patient's health under first- and second-line treatments. This means that treatment switch is likelier to succeed in second-line treatments that are similar to the first-line treatments, implying that a decision-maker should not rely on treatment switch as a risk diversification tool.