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.

Cost-utility analysis of adjunct repetitive transcranial magnetic stimulation for treatment resistant bipolar depression.

OBJECTIVE: To evaluate the cost-effectiveness of repetitive transcranial magnetic stimulation (rTMS) as an adjunct to standard care from an Australian health sector perspective, compared to standard care alone for adults with treatment-resistant bipolar depression (TRBD). METHODS: An economic model was developed to estimate the cost per disability-adjusted life-year (DALY) averted and quality-adjusted life-year (QALY) gained for rTMS added to standard care compared to standard care alone, for adults with TRBD. The model simulated the time in three health states (mania, depression, residual) over one year. Response to rTMS was sourced from a meta-analysis, converted to a relative risk and used to modify the time in the depressed state. Uncertainty and sensitivity tested the robustness of results. RESULTS: Base-case incremental cost-effectiveness ratios (ICERs) were $72,299 per DALY averted (95 % Uncertainty Interval (UI): $60,915 to $86,668) and $46,623 per QALY gained (95 % UI: $39,676 - $55,161). At a willingness to pay (WTP) threshold of $96,000 per DALY averted, the base-case had a 100 % probability of being marginally cost-effective. At a WTP threshold of $64,000 per QALY gained, the base-case had a 100 % probability of being cost-effective. Sensitivity analyses decreasing the number of sessions provided, increasing the disability weight or the time spent in the depression state for standard care improved the ICERs for rTMS. CONCLUSIONS: Dependent on the outcome measure utilised and assumptions, rTMS would be considered a very cost-effective or marginally cost-effective adjunct to standard care for TRBD compared to standard care alone.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

Causality-driven multivariate stock movement forecasting.

Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods.

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

The peer effects of resident stock market participation: Evidence from 2019 CHFS.

Limited resident's participation in the stock market has become a key constraint to the capital market development. Utilizing the 2019 China Household Financial Survey (CHFS) data, our paper designs probit models to examine the peer effects of residents' stock market participation and explore the intermediary mechanisms with a multiple intermediary model. We find that: (1) Resident involvement in stock market decision-making exhibits significant peer effects. (2) Heterogeneity analysis reveals that males and rural residents display more pronounced peer effects than females and urban residents. Additionally, middle-aged residents demonstrate more potent peer effects than their younger and older counterparts, with the intensity of peer effects correlating with education levels. (3)We observe that the peer effects of market participation operate by altering economic expectations and enhancing residents' financial literacy. (4) Further investigation establishes that individuals engaging in stock market investments manifest peer effects when deciding whether to diversify their stock portfolio. This study holds reference value for analyzing the impact of social interaction on financial behaviors and regulating individuals' financial conduct.

Critical Examination of Modeling Approaches Used in Economic Evaluations of First-Line Treatments for Locally Advanced or Metastatic Non-Small Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations: A Systematic Literature Review.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with up to 32% of patients with NSCLC harboring an epidermal growth factor receptor (EGFR) mutation. NSCLC harboring an EGFR mutation has a dedicated treatment pathway, with EGFR tyrosine kinase inhibitors and platinum-based chemotherapy often being the therapy of choice. OBJECTIVE: The aim of this study was to systemically review and summarize economic models of first-line treatments used for locally advanced or metastatic NSCLC harboring EGFR mutations, as well as to identify areas for improvement for future models. METHODS: Literature searches were conducted via Ovid in PubMed, MEDLINE, MEDLINE In-Process, Embase, Evidence-Based Medicine Reviews: Health Technology Assessment, Evidence-Based Medicine Reviews: National Health Service Economic Evaluation Database, and EconLit. An initial search was conducted on 19 December 2022 and updated on 11 April 2023. Studies were selected according to predefined criteria using the Population, Intervention, Comparator, Outcome and Study design (PICOS) framework. RESULTS: Sixty-seven articles were included in the review, representing 59 unique studies. The majority of included models were cost-utility analyses (n = 52), with the remaining studies being cost-effectiveness analyses (n = 4) and a cost-minimization analysis (n = 1). Two studies incorporated both a cost-utility and cost-minimization analysis. Although the model structure across studies was consistently reported, justification for this choice was often lacking. CONCLUSIONS: Although the reporting of economic models in NSCLC harboring EGFR mutations is generally good, many of these studies lacked sufficient reporting of justification for structural choices, performing extensive sensitivity analyses and validation in economic evaluations. In resolving such gaps, the validity of future models can be increased to guide healthcare decision making in rare indications.

Key considerations for modelling the long-term costs and benefits of treatments for ANCA-associated vasculitis.

OBJECTIVES: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of severe and chronic autoimmune diseases. Patients undergo two treatment phases: inducing remission and maintaining remission to prevent organ damage. Immunosuppressants, including glucocorticoids (GCs) are used as first-line treatment, but long-term GC use is associated with toxic effects. Novel treatments reduce or replace the need for long-term GC, and therefore can reduce GC-related toxicity. The evolving treatment landscape has presented new challenges for health technology assessment (HTA) of new treatments in AAV and long-term modelling of costs and outcomes in this disease. METHODS: Using the appraisal of avacopan in England (NICE) as a case study, this paper aims to identify the key challenges involved in the economic evaluation of new treatments for AAV, with a particular focus on the long-term modelling of the treatment costs and benefits for the purpose of HTA. The outcome of this study is a set of recommendations for modelling the cost-effectiveness of new treatments for AAV from the HTA perspective. RESULTS: The discussion focuses on the appropriate model structure, approach to modelling end-stage renal disease (ESRD) as a key determinant of costeffectiveness, capturing the impact of GC-related adverse events, and estimation of short and long-term costs of AAV. CONCLUSIONS: Economic evaluation of new treatments for AAV needs to capture all relevant downstream effects. ESRD is a key driver of cost-effectiveness but is associated with major uncertainty. Future observational studies need to offer sufficient detail to allow for differentiation in event rates across treatment options.

Markov modeling for cost-effectiveness using federated health data network.

OBJECTIVE: To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. MATERIALS AND METHODS: We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. RESULTS: We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. DISCUSSION: Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. CONCLUSION: We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.

Cross-sectional analysis of use of real-world data in single technology appraisals of oncological medicine by the National Institute for Health and Care Excellence in 2011-2021.

OBJECTIVES: This study aims to identify how real-world data (RWD) have been used in single technology appraisals (STAs) of cancer drugs by the National Institute for Health and Care Excellence (NICE). DESIGN: Cross-sectional study of NICE technology appraisals of cancer drugs for which guidance was issued between January 2011 and December 2021 (n=229). The appraisals were reviewed following a published protocol to extract the data about the use of RWD. The use of RWD was analysed by reviewing the specific ways in which RWD were used and by identifying different patterns of use. PRIMARY OUTCOME MEASURE: The number of appraisals where RWD are used in the economic modelling. RESULTS: Most appraisals used RWD in their economic models. The parametric use of RWD was commonly made in the economic models (76% of the included appraisals), whereas non-parametric use was less common (41%). Despite widespread use of RWD, there was no dominant pattern of use. Three sources of RWD (registries, administrative data, chart reviews) were found across the three important parts of the economic model (choice of comparators, overall survival and volume of treatment). CONCLUSIONS: NICE has had a long-standing interest in the use of RWD in STAs. A systematic review of oncology appraisals suggests that RWD have been widely used in diverse parts of the economic models. Between 2011 and 2021, parametric use was more commonly found in economic models than non-parametric use. Nonetheless, there was no clear pattern in the way these data were used. As each appraisal involves a different decision problem and the ability of RWD to provide the information required for the economic modelling varies, appraisals will continue to differ with respect to their use of RWD.

Pricing decision and channel selection of fresh agricultural products dual-channel supply chain based on blockchain.

The application of blockchain can effectively improve the efficiency of fresh agricultural product circulation and consumer trust, but it can also increase investment costs. In this context, this paper introduces parameters such as blockchain unit variable cost, the level of blockchain technology investment, and consumer channel preference in two dual-channel supply chain systems dominated by fresh agricultural product manufacturers: online direct sales and distribution. It compares and analyzes pricing and channel selection strategies in both cases of not using and using blockchain. The research shows that when blockchain is used, manufacturer profits are higher in the direct sales model than in the distribution model. Traditional retailers' profits are lower in the direct sales model than in the distribution model. Total supply chain profits are higher in the direct sales model than in the distribution model, and they exhibit an inverted "U" shape as the level of blockchain investment increases. In the online direct sales model, if the blockchain technology unit variable cost is within a certain threshold range, manufacturer profits, traditional retailer profits, and total supply chain profits are all higher than when blockchain technology is not used. In the online distribution model, when the blockchain variable cost and blockchain usage level meet certain conditions, manufacturers, traditional retailers, and online distributors all have higher profits when using blockchain technology than when not using it. This study provides theoretical guidance for the practical application of blockchain technology in dual-channel fresh agricultural product supply chains.

Digital economy and green total factor productivity in China.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

A short-term price prediction-based trading strategy.

Quantitative investment theory has emerged as a prominent and widely researched domain within the financial markets, where investors predominantly focus on discerning the intricate influences of market dynamics. In this paper, we proposed a short-term prediction-based trading strategy, which can equiponderate between return and risk, considerations while accounting for investor risk preferences. This strategy employs GM(1,1) to capture nuanced features of price dynamics in short-term intervals and update the GM(1,1) model with the latest data. Subsequently, a multi-objective planning equation is formulated to optimize asset allocations by determining the optimal holding that strikes between specific returns and risk mitigation. In the end, this work conducts a case study and sensitivity analysis using five years of gold and bitcoin price data spanning from 2016 to 2021. This empirical examination serves to affirm the efficacy and resilience of the proposed trading strategy. The case study reveals that proficient short-term price forecasting serves as a potent means to proactively mitigate risk, facilitating, judicious and objective trading practices. Moreover, it underscores the strategy's tangible utility as a guide for real-world investment decisions.

Budget Impact of Introducing Fixed-Duration Mosunetuzumab for the Treatment of Relapsed or Refractory Follicular Lymphoma After Two or More Lines of Systemic Therapy in the USA.

OBJECTIVE: This study aimed to assess the budget impact of introducing fixed-duration mosunetuzumab as a treatment option for adult patients with relapsed or refractory follicular lymphoma after at least two prior systemic therapies and to estimate the total cumulative costs per patient in the USA. METHODS: A 3-year budget impact model was developed for a hypothetical 1-million-member cohort enrolled in a mixed commercial/Medicare health plan. Comparators were: axicabtagene ciloleucel, tisagenlecleucel, tazemetostat, rituximab plus lenalidomide, copanlisib, and older therapies (rituximab or obinutuzumab ± chemotherapy). Costs per patient comprised treatment-associated costs including the drug, its administration, adverse events, and routine care. Dosing and safety data were ascertained from respective package inserts and clinical trial data. Drug costs (March 2023) were estimated based on the average wholesale acquisition cost reported in AnalySource®, and all other costs were based on published sources and inflated to 2022 US dollars. Market shares were obtained from Genentech internal projections and expert opinion. Budget impact outcomes were presented on a per-member per-month basis. RESULTS: Compared with a scenario without mosunetuzumab, its introduction over 3 years resulted in a budget increase of $69,812 (1% increase) and an average per-member per-month budget impact of $0.0019. Among the newer therapies, mosunetuzumab had the second-lowest cumulative per patient cost (mosunetuzumab = $202,039; axicabtagene ciloleucel = $505,845; tisagenlecleucel = $476,293; rituximab plus lenalidomide = $263,520; tazemetostat = $250,665; copanlisib = $127,293) and drug costs, and its introduction only increased total drug costs by 0.1%. By year 3, the cumulative difference in the per patient cost with mosunetuzumab was -$303,805 versus axicabtagene ciloleucel, -$274,254 versus tisagenlecleucel, -$61,481 versus rituximab plus lenalidomide, -$48,625 versus tazemetostat, and $74,747 versus copanlisib. Older therapies were less costly with 3-year cumulative costs that ranged from $36,512 to $147,885. CONCLUSIONS: Over 3 years, the estimated cumulative per patient cost of mosunetuzumab is lower than most available newer therapies, resulting in a small increase in the budget after its formulary adoption for the treatment of relapsed or refractory follicular lymphoma.

Measuring Costs of Cardiovascular Disease Prevention for Patients with Familial Hypercholesterolemia in Administrative Claims Data.

INTRODUCTION: Familial hypercholesterolemia is a common genetic condition that significantly increases an individual's risk of cardiovascular events such as heart attack, stroke, and cardiac death and is a candidate for population-wide screening programs. Economic analyses of strategies to identify and treat familial hypercholesterolemia are limited by a lack of real-world cost estimates for screening services and medications for reducing cardiovascular risk in this population. METHODS: We estimated the cost of lipid panel testing in patients with hyperlipidemia and the cost of statins, ezetimibe, and PCKS9 inhibitors in patients with familial hypercholesterolemia from a commercial claims database and report costs and charges per panel and prescription by days' supply. RESULTS: The mean cost for a 90-day supply for statins was $183.33, 2.3 times the mean cost for a 30-day supply at $79.35. PCSK9 inhibitors generated the highest mean costs among medications used by patients with familial hypercholesterolemia. CONCLUSIONS: Lipid testing and lipid-lowering medications for cardiovascular disease prevention generate substantial real-world costs which can be used to improve cost-effectiveness models of familial hypercholesterolemia screening and care management.

Simplified Methods for Modelling Dependent Parameters in Health Economic Evaluations: A Tutorial.

BACKGROUND: In health economic evaluations, model parameters are often dependent on other model parameters. Although methods exist to simulate multivariate normal (MVN) distribution data and estimate transition probabilities in Markov models while considering competing risks, they are technically challenging for health economic modellers to implement. This tutorial introduces easily implementable applications for handling dependent parameters in modelling. METHODS: Analytical proofs and proposed simplified methods for handling dependent parameters in typical health economic modelling scenarios are provided, and implementation of these methods are illustrated in seven examples along with the SAS and R code. RESULTS: Methods to quantify the covariance and correlation coefficients of correlated variables based on published summary statistics and generation of MVN distribution data are demonstrated using examples of physician visits data and cost component data. The use of univariate normal distribution data instead of MVN distribution data to capture population heterogeneity is illustrated based on the results from multiple regression models with linear predictors, and two examples are provided (linear fixed-effects model and Cox proportional hazards model). A conditional probability method is introduced to handle two or more state transitions in a single Markov model cycle and applied in examples of one- and two-way state transitions. CONCLUSIONS: This tutorial proposes an extension of routinely used methods along with several examples. These simplified methods may be easily applied by health economic modellers with varied statistical backgrounds.

Emergent invariance and scaling properties in the collective return dynamics of a stock market.

A key metric to determine the performance of a stock in a market is its return over different investment horizons (τ). Several works have observed heavy-tailed behavior in the distributions of returns in different markets, which are observable indicators of underlying complex dynamics. Such prior works study return distributions that are marginalized across the individual stocks in the market, and do not track statistics about the joint distributions of returns conditioned on different stocks, which would be useful for optimizing inter-stock asset allocation strategies. As a step towards this goal, we study emergent phenomena in the distributions of returns as captured by their pairwise correlations. In particular, we consider the pairwise (between stocks i, j) partial correlations of returns with respect to the market mode, ci,j(τ), (thus, correcting for the baseline return behavior of the market), over different time horizons (τ), and discover two novel emergent phenomena: (i) the standardized distributions of the ci,j(τ)'s are observed to be invariant of τ ranging from from 1000min (2.5 days) to 30000min (2.5 months); (ii) the scaling of the standard deviation of ci,j(τ)'s with τ admits good fits to simple model classes such as a power-law τ-λ or stretched exponential function [Formula: see text] (λ, ß > 0). Moreover, the parameters governing these fits provide a summary view of market health: for instance, in years marked by unprecedented financial crises-for example 2008 and 2020-values of λ (scaling exponent) are substantially lower. Finally, we demonstrate that the observed emergent behavior cannot be adequately supported by existing generative frameworks such as single- and multi-factor models. We introduce a promising agent-based Vicsek model that closes this gap.