Heterogeneity in informational treatment effects on willingness to pay for forest bathing

Introduction

The agriculture and food sectors contribute significantly to greenhouse gas emissions. About 15 percent of food-related carbon emissions are channeled through restaurants. Using a contingent valuation (CV) method with double-bounded dichotomous choice (DBDC) questions, this article investigates U.S. consumers’ willingness to pay (WTP) for an optional restaurant surcharge in support of carbon emission reduction programs. The mean estimated WTP for a surcharge is 6.05 percent of an average restaurant check, while the median WTP is 3.64 percent. Our results show that individuals have a higher WTP when the surcharge is automatically added to restaurant checks. We also find that an information nudge—a short climate change script—significantly increases WTP. Additionally, our results demonstrate that there is heterogeneity in treatment effects across consumers’ age, environmental awareness, and economic views. Our findings suggest that a surcharge program could transfer a meaningful amount of the agricultural carbon reduction burden to consumers that farmers currently shoulder.

If we recognize heterogeneity of treatment effect can we lessen waste?

e15108 Background: HTE occurs when patient factors modify a treatment’s effect on health outcomes so patients in the same study have different responses to a specific treatment. Variation in outcomes is caused by interactions between the factors causing HTE and the treatment. HTE may explain some of the heterogeneity in S4PC prognosis. The HTE literature is evolving; hence some factors causing HTE in S4PC are not yet documented. Methods: A systematic literature review (1946-2011) of published trials and observational studies identified evidence of statistically significant factors influencing HTE in S4PC. Inclusion criteria required that articles examine the impact of HTE factors on survival (OS, DSS, PFS) or health-related quality-of-life measures among S4PC patients. Factors that influenced outcomes but were not specifically evaluated in the context of HTE were excluded. Results: Of 398 articles that included key words, 33 met inclusion/exclusion criteria. Treatments included chemotherapy, radiation, hormonal therapy and bone-modifying agents. Both biologic and non-biologic factors (Table) were found to be associated with HTE in S4PC. Evidence was mixed regarding whether HTE in S4PC is influenced by race or by confounding factors correlated with race or both. As no articles examined prognostic factors such as bcl2, PTEN, p53, Pgp and EGFR status in the context of a specific treatment, such factors were excluded. Conclusions: Current evidence reveals diverse factors influencing HTE in S4PC.Ultimately, such knowledge can help oncologists prescribe more personalized medicine, help patients make more informed treatment choices, and aid policy making and treatment coverage decisions. [Table: see text]

There has been a renewed interest in identifying heterogenous treatment effects (HTEs) to guide personalized medicine. The objective was to illustrate the use of a step-by-step transparent parametric data-adaptive approach (the generalized HTE approach) based on the G-computation algorithm to detect heterogenous subgroups and estimate meaningful conditional average treatment effects (CATE). The following seven steps implement the generalized HTE approach: Step 1: Select variables that satisfy the backdoor criterion and potential effect modifiers; Step 2: Specify a flexible saturated model including potential confounders and effect modifiers; Step 3: Apply a selection method to reduce overfitting; Step 4: Predict potential outcomes under treatment and no treatment; Step 5: Contrast the potential outcomes for each individual; Step 6: Fit cluster modeling to identify potential effect modifiers; Step 7: Estimate subgroup CATEs. We illustrated the use of this approach using simulated and real data. Our generalized HTE approach successfully identified HTEs and subgroups defined by all effect modifiers using simulated and real data. Our study illustrates that it is feasible to use a step-by-step parametric and transparent data-adaptive approach to detect effect modifiers and identify meaningful HTEs in an observational setting. This approach should be more appealing to epidemiologists interested in explanation.

Willingness to Pay for a Quality-Adjusted Life-Year: The Individual Perspective

BackgroundRandomised controlled trials (RCTs) enrolling patients with sepsis or acute respiratory distress syndrome (ARDS) generate heterogeneous trial populations. Non-random variation in the treatment effect of an intervention due to differences in the baseline risk of death between patients in a population represents one form of heterogeneity of treatment effect (HTE). We assessed whether HTE in two sepsis and one ARDS RCTs could explain indeterminate trial results and inform future trial design.MethodsWe assessed HTE for vasopressin, hydrocortisone and levosimendan in sepsis and simvastatin in ARDS patients, on 28-day mortality, using the total Acute Physiology And Chronic Health Evaluation II (APACHE II) score as the baseline risk measurement, comparing above (high) and below (low) the median score. Secondary risk measures were the acute physiology component of APACHE II and predicted risk of mortality using the APACHE II score. HTE was quantified both in additive (difference in risk difference (RD)) and multiplicative (ratio of relative risks (RR)) scales using estimated treatment differences from a logistic regression model with treatment risk as the interaction term.ResultsThe ratio of the odds of death in the highest APACHE II quartile was 4.9 to 7.4 times compared to the lowest quartile, across the three trials. We did not observe HTE for vasopressin, hydrocortisone and levosimendan in the two sepsis trials. In the HARP-2 trial, simvastatin reduced mortality in the low APACHE II group and increased mortality in the high APACHE II group (difference in RD = 0.34 (0.12, 0.55) (p = 0.02); ratio of RR 3.57 (1.77, 7.17) (p < 0.001). The HTE patterns were inconsistent across the secondary risk measures. The sensitivity analyses of HTE effects for vasopressin, hydrocortisone and levosimendan were consistent with the main analyses and attenuated for simvastatin.ConclusionsWe assessed HTE in three recent ICU RCTs, using multivariable baseline risk of death models. There was considerable within-trial variation in the baseline risk of death. We observed potential HTE for simvastatin in ARDS, but no evidence of HTE for vasopressin, hydrocortisone or levosimendan in the two sepsis trials. Our findings could be explained either by true lack of HTE (no benefit of vasopressin, hydrocortisone or levosimendan vs comparator for any patient subgroups) or by lack of power to detect HTE. Our results require validation using similar trial databases.

There is growing interest in tailoring treatment decisions to individual patient characteristics, but few studies have examined the implementation and performance of individualized treatment rules (ITRs) for count data. Our objective was to compare ITR methods in randomized trials with count outcomes and explore the impact of sample size and distribution of heterogeneity of treatment effect (HTE) on the validity of treatment recommendations. We conducted a simulation study where patients were randomized to receive one of two treatments and created five responder strata to reflect different HTE scenarios. Various ITR methods were used to estimate treatment effects and were evaluated in terms of value function and accuracy. We also conducted a case study involving patients with multiple sclerosis. All ITR methods performed better under favorable conditions such as larger sample size, greater treatment heterogeneity, or fewer neutral patients (also known as equivalent treatments effects), but they were outperformed by fixed treatment strategies with smaller sample sizes or limited HTE. However, larger sample sizes can compensate ITRs for smaller HTEs and high HTEs can compensate ITRs for limited data. In the case study, we identified HTE and developed a tree-based ITR that outperformed fixed treatment recommendations. In conclusion, ITR performance can be influenced by sample size and the distribution of HTE, as well as their interactions. Simulation scenarios, informed by clinical insights, can help us determine if HTE estimation is feasible and, if so, identify the most effective ITR.

The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement of 2020 proposed predictive modeling for identifying heterogeneity in treatment effects (HTE) in randomized clinical trials (RCTs). It described 2 approaches: risk modeling, which develops a multivariable model predicting individual baseline risk of study outcomes and then examines treatment effects across strata of predicted risk, and effect modeling, which develops a model that directly predicts individual treatment effects using a variety of regression and machine learning methods. To identify, describe, and evaluate findings from reports that cited the PATH Statement and presented predictive modeling of HTE in RCTs. Reports were identified using PubMed, Google Scholar, Web of Science, and SCOPUS through July 5, 2024. Using double review with adjudication, reports were assessed for consistency with PATH Statement recommendations, credibility of HTE findings (applying criteria adapted from the Instrument to Assess Credibility of Effect Modification Analyses), and clinical importance of credible findings. A total of 65 reports (presenting 31 risk models and 41 effect models) analyzing 162 RCTs were identified, with credible, clinically important HTE in 24 reports (37%). Contrary to PATH Statement recommendations, only 25 of 48 studies with positive overall findings included a risk model. Most effect models were exploratory, including multiple predictors with little prior evidence for HTE. Claims of HTE were noted in 23 risk modeling and 31 effect modeling reports but were more likely to meet credibility criteria with risk modeling (20 of 23 reports [87%]) than effect modeling (10 of 31 reports [32%]). For effect modeling, validation of HTE findings in external datasets was critical in establishing credibility. Credible HTE from either approach was usually judged clinically important (24 of 30 reports [80%]). In the 19 reports from RCTs suggesting overall treatment benefits, modeling identified subgroups of 5% to 67% of patients predicted to experience no benefit or net treatment harm. In the 5 reports that found no overall benefit, subgroups of 25% to 60% of patients were nevertheless predicted to benefit. This scoping review of 65 reports of multivariable predictive modeling of HTE in RCTs identified credible, clinically important HTE in 37%. Risk modeling was more likely than effect modeling to find credible HTE, but external validation of HTE findings served to increase the credibility of findings from exploratory effect models.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于居民生态认知的非使用价值支付意愿空间分异研究——以三江平原湿地为例 DOI: 10.5846/stxb201306031284 作者: 作者单位: 山东工商学院 中加学院,东北农业大学,东北农业大学,东北农业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(71171044);山东省社科规划项目(13DJJJ01);中国博士后基金(2013M531012) Spatial differentiation research of non-use value WTP based on the residents'ecological cognition:taking the sanjiang plain as a case Author: Affiliation: Shandong Institute of Business and Technology,Northeast Agricultural University,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在WTP距离衰减性研究基础上,将菲什拜因理论与条件价值法相结合,假设个人对于物品的认知在空间上并不是均衡分布的,不同空间内的受访者的支付意愿存在差异,以三江平原湿地生态系统为应用对象,将样本分为核心区、辐射区、外围区,采用双边界二分式CVM,探讨受访者对三江平原湿地生态环境保护的支付意愿水平及支付意愿的影响因素,建立基于居民生态认知的支付意愿空间分异模型。计算得到核心区、辐射区、外围区居民平均支付意愿分别为142.23 元 人-1 a-1、105.01 元 人-1 a-1、77.62 元/人,总体呈递减趋势,验证了距离、认知和WTP之间相关性。研究结果表明,通过空间视角将居民的认知程度纳入支付意愿的计算,能提高CVM在环境价值评估应用中的有效性及可靠性。研究结论将为政府相关政策的制定和决策提供参考依据。 Abstract:Contingent valuation is a survey-based method that randomly selects families or individuals as samples. It reveals consumer preferences for public goods and services such as ecological environment resources in a hypothetical market, and infers respondents' willingness to pay (WTP) to improve, for example, environmental quality. The method can also be used to calculate the respondents' WTP (or willingness to accept, WTA) and extend the samples to the whole study region. The average WTP (or WTA) can then be used to obtain the economic benefits or losses brought about by a planning project. A great variety of questionnaire formats have been developed and applied in practice, among which the Dichotomous Choice CVM is considered one of the most advanced methods nowadays. With the continuous development of CVM, it is important to analyze what factors influence WTP in the related empirical studies. Different scholars have reached an agreement that the respondents' willingness to pay for environmental improvement is closely related to the distance between the evaluation objects and the environmental resources. To a certain extent, the distance between the respondents and the evaluation objects can adjust demand for environmental goods as an alternative of price mechanism. On the one hand, the distance factor can influence the respondents' awareness level on environmental goods, which is to produce environmental preferences through affecting the information validity and the accessibility; on the other hand, it can also affect the possibility of usability and substitutability of environmental goods. Logically, the distance should have negative effects on affecting respondents' WTP in a given region in the assumption. The farther the distance of respondents' live from the assessment object, the less possible improvement and protection WTP for environmental goods will have. This is the "Distance Decay Effect". Based on the study on WTP distance-decay, this paper, combining the Fishbein theory with the contingent valuation method, assumes that individuals' cognition and attitude towards the goods are imbalanced and the willingness to pay (WTP) of respondents in different space exists difference. The random utility model is applied to build the Double-bounded Dichotomous Choice CVM Data Analysis Model,and establishes the influential factor of WTP. Taking Sanjiang plain wetland as the application object, the samples are divided into core zone, radiation zone and peripheral zone by using double-bounded dichotomous contingent valuation technique and discussing the payment ability, payment willing and their influential factors so as to establish the spatial differentiation model of the willingness to pay to the non-use value WTP based on the cognition and attitude. Respectively, the average WTP of residents is: 142.23, 105.01 and 77.62 RMB per year, exhibiting a step-decreasing trend, which means the distance, cognition and the WTP are closely related. Research results show that, involving the individuals' cognition in WTP model, the effectiveness of CVM can be improved. The findings will provide useful references for government to make related ecological policies and the conclusion of the study will lay a foundation and provide reference for the government policy and decision making. 参考文献 相似文献 引证文献

To determine the level of willingness to pay (WTP) for re-treatment of mosquito nets and to compare the theoretical validity of WTP estimates from three contingent valuation question formats: the bidding game, binary with follow-up technique, and a novel structured haggling technique that mimicked price-taking behaviour in the study area. WTP was elicited from randomly selected respondents from three villages in Southeast Nigeria, using pretested interviewer-administered questionnaires. Respondents' WTP for insecticide-treated nets (ITNs) was first elicited before their WTP for re-treatment of ITNs. Ordinary least-squares regression was used to assess theoretical validity. More than 95% of the respondents were willing to pay for re-treatment. The mean WTP was 37.1 Naira, 43.4 Naira and 49.2 Naira in the bidding game, binary with follow-up and structured haggling groups, respectively (US dollar 1.00 = 120 Naira). The WTP estimates elicited across the three question formats were statistically different (P < 0.01). Ordinary least-squares estimation showed that WTP was positively related to many variables, especially stated WTP for ITNs (P < 0.05). Structured haggling generated the highest number of statistically significant variables to explain WTP. The three contingent valuation approaches generated different distributions of WTP for net retreatment, possibly due to their inherent differences. Structured haggling generated the most theoretically valid estimates of WTP. The levels of WTP identified suggest that user fees exceeding 50 Naira per net re-treatment may discourage demand for the service. This is an important challenge for ITN programmes.

Corticosteroids improve outcomes in patients with severe COVID-19. In the COVID STEROID 2 randomised clinical trial, we found high probabilities of benefit with dexamethasone 12 versus 6 mg daily. While no statistically significant heterogeneity in treatment effects (HTE) was found in the conventional, dichotomous subgroup analyses, these analyses have limitations, and HTE could still exist. We assessed whether HTE was present for days alive without life support and mortality at Day 90 in the trial according to baseline age, weight, number of comorbidities, category of respiratory failure (type of respiratory support system and oxygen requirements) and predicted risk of mortality using an internal prediction model. We used flexible models for continuous variables and logistic regressions for categorical variables without dichotomisation of the baseline variables of interest. HTE was assessed both visually and with p and S values from likelihood ratio tests. There was no strong evidence for substantial HTE on either outcome according to any of the baseline variables assessed with all p values >.37 (and all S values <1.43) in the planned analyses and no convincingly strong visual indications of HTE. We found no strong evidence for HTE with 12 versus 6 mg dexamethasone daily on days alive without life support or mortality at Day 90 in patients with COVID-19 and severe hypoxaemia, although these results cannot rule out HTE either.

应用二分式条件价值评估法对三江平原湿地生态保护价值进行定量评价。运用支付意愿函数模型构建双边界二分式CVM数据分析模型,应用生存分析中的参数回归模型建立支付意愿影响因素分析模型。建立具有距离变量及个人社会、经济属性变量的支付意愿函数,验证支付意愿具有"距离衰减性"。研究采用以面访调查为主、网上调查为辅的方式,进行支付意愿问卷调查,得出2010年三江平原湿地生态保护的人均支付意愿为134.582元/a,三江平原湿地的生态保护价值为33.412亿元/a。研究结论为政府相关政策的制定提供参考依据。;The Contingent Valuation Method (CVM) is one of the most widely used methods to assess the values of natural resources and environmental goods. Through questionnaires, CVM induces people's preferences and makes an expression of monetization; the induced value of Willingness to Pay (WTP) is based on a hypothetical market. The inducing technique or the questionnaire format used to derive the maximum WTP is an important subject among CVM research. A great variety of questionnaire formats have been developed and applied in practice, among which the Dichotomous Choice CVM is considered one of the most advanced methods nowadays. The open-ended double-bounded Dichotomous Choice questionnaire, which is universally applied in foreign literature, combines the advantages of Dichotomous Choice and open-ended questionnaire. It induces the respondents' true WTP, reduces the deviation of assessment results, and thus makes the statistics results more accurate and reliable. In this study, with the open-ended double-bounded Dichotomous Choice CVM, we looked into the respondents' WTP mainly based on the interview survey and supplemented by the online survey, aiming to evaluate the ecological conservation value of Sanjiang Plain Wetland and the respondents' WTP to protect Sanjiang Plain Wetland ecology, and identify what factors influence their WTP. It is important to analyze what factors influence WTP in the related empirical studies. Researchers are continually attempting to expend the influential factors reflected by CVM. The spatial distance factor has now been gradually incorporated into the influential mechanism research of WTP in CVM. The respondents' WTP and the distance between respondents and environmental resource that serves as the evaluation object has a negative relationship, that is, the WTP of the respondents is relatively lower if they are further away from the evaluation object. This phenomenon is called the Distance Decay Effect. The distance factor is defined as the straight-line distance between the respondents and evaluation object, and incorporated into the WTP Function Model as an independent variable along with other personal, social and economic attribute variables to verify that the willingness has the characteristic of distance decay. The Dichotomous Choice CVM was used to evaluate the ecological conservation value of Sanjiang Plain Wetland. The WTP Function Model was applied to build the Double-bounded Dichotomous Choice CVM Data Analysis Model, and the parameter regression model of survival analysis was used to establish the influential factor analysis model of WTP. The conclusions are shown as follows: (1) in 2010, the WTP to protect Sanjiang Plain Wetland is RMB 134.582 per person, and the total ecological conservation value of Sanjiang Plain Wetland is RMB 3.3412 billion per year; (2) some factors have significant effects on the WTP, such as respondents' gender, age, educational level and personal annual income. The WTP is higher among people with higher education and income, while it is lower with the aggrandizing of age and distance. Besides, the WTP of female is significantly higher than that of male. The findings will provide useful references for government to make related ecological policies.

Background: Trials’ data are increasingly re-analyzed to identify treatment effect heterogeneity: that is, subgroups of patients who have either enhanced or adverse effects in a trial. This study investigates the robustness of subgroup identification methods in an acute stroke trial. Methods and Analysis: The Model-based recursive partitioning (MOB), Stochastic Subgroup Identification based on Differential Effects Search (Stochastic SIDEScreen), and Virtual Twin (VT) methods would be used to detect heterogeneity in Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion with Emphasis on Minimizing CT to Recanalization Times (ESCAPE) trial. Results: In the ESCAPE trial, patients in the intervention group had a higher rate of functional independence (90-day mRS 0-2) than those in the control group (OR=2.6; p&lt;0.001, and 95% CI=1.7–3.8). The three methods identified patients with differential treatment effects. The MOB identified 2-terminal subgroups, with the NIHSS &gt; 11 group showing a significant treatment effect (OR=3.67; p&lt;0.001 and 95% CI=2.11–6.40), while the subgroup of with a maximum NIHSS score of 11 did not (OR=1.63; p=0.463 and 95% CI=0.44–6.05). The stochastic SIDEScreen identified 4-terminal subgroups, but the group of patients with NIHSS greater than 9 and older than 54 years had a significant treatment effect (OR=4.92; p&lt;0.001, and 95% CI= 2.66–9.10). Other three subgroups, like patients with a maximum NIHSS score of 9 and older than 54 years (OR=2.17, p=0.34, and 95% CI=0.44–10.65), did not have a significant treatment effect. VT identified 6-terminal subgroups; the subgroup consisting of patients older than 56 years and NIHSS &gt; 11 had significant treat effect (OR=5.11; p&lt;0.001 and 95% CI=2.68–9.73). As other renaming 4 subgroups, the subgroup consisting of younger patients and with a maximum NIHSS score of 11 did not show a treatment effect (OR=1.60, p=0.64, and 95% CI=0.39–6.30). Conclusion: Data-driven subgroup identification methods provide insight into the heterogeneity of treatment effects in acute stroke trials. Information about the identified subgroups might inform the development of clinical practice guidelines for acute stroke management.

BackgroundHeterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected.MethodsFive unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable.FindingsNo single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms.InterpretationMachine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation.FundingNIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC)