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We examine one of the secondary effects of the import surge from China in the last few decades: its potentially depressing impact on house price appreciations. To identify a causal impact, we use the granting of Permanent Normal Trade Relations (PNTR) to China in October 2000 as our exogenous treatment event. We consider housing prices for 685 US commuting zones (CZs) in a panel data setting for the years 1990–2020. We find that the 2000 PNTR trade event caused house prices to appreciate about 7 percent less in highly import-exposed CZs within 5–6 years of the trade event and that the price impact has persisted through 2020. The size of the average impact is highly robust to various sensitivity checks. We also show that the price effect of the 2000 PNTR event varied significantly across CZs with different import exposure to China and with different economic characteristics. In some areas, such as around the Great Lakes and parts of Alabama, house prices appreciated by only about half as much between 2000 and 2020 (< 25%) as would have been expected in the absence of the 2000 PNTR policy event. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the 2022 Mississippi River drought on agricultural trade using counterfactual evaluation methods and detailed trade data at the US port level. The study examines how the drought disrupted agricultural shipments out of Louisiana ports and whether the disruption led to trade diversion to other ports. Our findings reveal that shipments out of Louisiana ports were 3.9% or $560 million below the counterfactual between July 2022 and January 2023. In addition, the dynamic treatment estimates provide evidence of immediate trade recovery after the drought receded in October 2022, indicating that the impact of the drought was short‐lived. Wheat exports were the most affected, experiencing a reduction in shipments from Louisiana ports of $150 million and being diverted to US ports on the West Coast. In contrast, corn and soybeans did not experience lasting trade destruction or diversion to other ports. Our analysis also reveals that export prices increased significantly above the counterfactual level at Louisiana ports, suggesting that the drought impacted the supply and export dynamics of agricultural commodities. In conclusion, this paper provides valuable insights into the short‐run implications of natural disasters on agricultural trade. [EconLit Citations: F14, Q17]. [ABSTRACT FROM AUTHOR]

This paper assesses the response of agricultural commodity markets to Russia's withdrawal from the Black Sea Grain Initiative (BSGI). Employing daily commodity‐level data and event study methods, we analyse the impact on seven agricultural commodities and four key market metrics, including futures prices, historical and implied volatility, and speculative pressure. Our findings show a statistically insignificant increase of 1.1% in agricultural futures prices within the first seven trading days following the BSGI termination. In the following days, futures prices began to decline, eventually returning to levels below those observed before the withdrawal, a pattern further underscored by our implied volatility analysis. While there is no evidence of heightened speculation, we find some evidence for treatment differences across agricultural commodities. These findings suggest that traders did not believe in the likelihood of a blockade of Black Sea grain shipments. [ABSTRACT FROM AUTHOR]

Recent advancements in education scholarship have introduced Item Response Theory (IRT) models to address treatment heterogeneity at the assessment item level. These models for item-level heterogeneous treatment effects (IL-HTE) enable detailed analyses of treatments that may have varying impacts on individual items within an assessment. This article offers a comprehensive tutorial for applied researchers interested in implementing IL-HTE analysis in R, utilizing the lme4 package. Using empirical data from a second-grade reading comprehension assessment as a running example, this tutorial emphasizes model-building strategies, interpretation techniques, visualization methods, and extensions. By following this tutorial, researchers will gain practical insights into utilizing IL-HTE analysis for enhanced understanding and interpretation of treatment effects at the item level. [ABSTRACT FROM AUTHOR]

The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects. [ABSTRACT FROM AUTHOR]

Background and Aims: Effective therapies that target three main signalling pathways are approved to treat pulmonary arterial hypertension (PAH). However, there are few large patient-level studies that compare the effectiveness of these pathways. The aim of this analysis was to compare the effectiveness of the treatment pathways in PAH and to assess treatment heterogeneity. Methods: A network meta-analysis was performed using individual participant data of 6811 PAH patients from 20 Phase III randomized clinical trials of therapy for PAH that were submitted to the US Food and Drug Administration. Individual drugs were grouped by the following treatment pathways: endothelin, nitric oxide, and prostacyclin pathways. Results: The mean (±standard deviation) age of the sample was 49.2 (±15.4) years; 78.4% were female, 59.7% had idiopathic PAH, and 36.5% were on background PAH therapy. After covariate adjustment, targeting the endothelin + nitric oxide pathway {β: 43.7 m [95% confidence interval (CI): 32.9, 54.4]}, nitric oxide pathway [β: 29.4 m (95% CI: 22.6, 36.3)], endothelin pathway [β: 25.3 m (95% CI: 19.8, 30.8)], and prostacyclin pathway [oral/inhaled β: 19.1 m (95% CI: 14.2, 24.0), intravenous/subcutaneous β: 24.4 m (95% CI: 15.1, 33.7)] significantly increased 6 min walk distance at 12 or 16 weeks compared with placebo. Treatments also significantly reduced the likelihood of having clinical worsening events. There was significant heterogeneity of treatment effects by age, body mass index, hypertension, diabetes, and coronary artery disease. Conclusions: Drugs targeting the three traditional treatment pathways significantly improve outcomes in PAH, with significant treatment heterogeneity in patients with some comorbidities. Randomized clinical trials are warranted to identify the most effective treatment strategies in a personalized approach. Structured Graphical Abstract Comparison of the effectiveness and heterogeneity of treatment effects in pulmonary arterial hypertension. 6MWD, 6 min walk distance; CI, confidence interval; IV/Sc, intravenous/subcutaneous; PO/Inh, oral/inhaled. [ABSTRACT FROM AUTHOR]

BACKGROUND: The combination of unhealthy alcohol use and depression is associated with adverse outcomes including higher rates of alcohol use disorder and poorer depression course. Therefore, addressing alcohol use among individuals with depression may have a substantial public health impact. We compared the effectiveness of a brief intervention (BI) for unhealthy alcohol use among patients with and without depression. METHOD: This observational study included 312,056 adult primary care patients at Kaiser Permanente Northern California who screened positive for unhealthy drinking between 2014 and 2017. Approximately half (48%) received a BI for alcohol use and 9% had depression. We examined 12-month changes in heavy drinking days in the previous three months, drinking days per week, drinks per drinking day, and drinks per week. Machine learning was used to estimate BI propensity, follow-up participation, and alcohol outcomes for an augmented inverse probability weighting (AIPW) estimator of the average treatment (BI) effect. This approach does not depend on the strong parametric assumptions of traditional logistic regression, making it more robust to model misspecification. RESULTS: BI had a significant effect on each alcohol use outcome in the non-depressed subgroup (-0.41 to -0.05, all ps  .28). However, differences between subgroups were nonsignificant (0.00 to 0.11, all ps > .44). CONCLUSION: On average, BI is an effective approach to reducing unhealthy drinking, but more research is necessary to understand its impact on patients with depression. AIPW with machine learning provides a robust method for comparing intervention effectiveness across subgroups.

Who reacts politically to fiscally costly immigration? A political economy tradition holds that reactions depend on economic self-interest, whereas a social psychology tradition emphasizes generalized political orientations and trust. Past work largely leans in favor of the latter tradition. We make three contributions. First, our dependent variable is a concrete perception of welfare state sustainability, arguably better suited to capture self-interest. Second, both the political economy- and social psychology traditions have been studied narrowly; we separate between multiple interests (including economic local context), and compare several types of trust orientations. Third, we use machine learning methods well-suited to analyze treatment heterogeneity in a randomized survey experiment. We find support for both interest-based and social psychological explanations. As for the latter, what matters is not only, or even mainly, orientations/trust related to immigration. Rather, generalized political distrust strongly regulates when costly immigration cues trigger welfare sustainability worries. [ABSTRACT FROM AUTHOR]

Gender diversity signals inclusivity, but meta-analyses suggest that it does not boost individual or group performance. This research examined whether a social-psychological intervention can unlock the benefits of gender diversity on college physics students' social and academic outcomes. Analyses of 124 introductory physics classrooms at a large research institution in the eastern United States (N = 3,605) indicated that in classrooms doing "business as usual," cross-gender collaboration was infrequent, there was a substantial gender gap in physics classroom belonging, and classroom gender diversity had no effect on performance. The ecological-belonging intervention aimed to establish classroom norms that adversity in the course is normal and surmountable. In classrooms receiving the intervention, cross-gender interaction increased 51%, the gender gap in belonging was reduced by 47%, and higher classroom diversity was associated with higher course grades and 1-year grade point average for both men and women. Addressing contextual belongingness norms may help to unlock the benefits of diversity. [ABSTRACT FROM AUTHOR]

Abstract Background In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights. Methods To analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them. Results A numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model. Conclusion The method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.

This paper evaluates the effects of sanctions on Russia between 2014 and 2019 and the resulting countersanctions. We estimate their impact on trade in a gravity framework, allowing for treatment heterogeneity among pairs and sectors, and use the estimated elasticities in a general equilibrium analysis. We find that the sanctions decreased trade with Russia in key sectors, translating to a loss in real income in Russia by 0.3%. Full decoupling of the EU and its allies from Russia would increase this effect to over 4%. Our results emphasize the role of deep sanctions as a foreign policy instrument and international cooperation. [ABSTRACT FROM AUTHOR]

Objectives: This study examines the effect Project Green Light Detroit (PGLD), an integrated CCTV program, on crime at commercial and non-commercial city parcels in Detroit, MI. Methods: A quasi-experimental design was used by implementing a difference-in-differences model with adjustments made for variation in treatment timing and treatment heterogeneity. Results: Findings from the study indicate that PGLD increased reporting of property crimes at some participating locations but did not significantly impact violent or disorder crimes. Most of the impact of PGLD was attributable to locations that joined the program early in its implementation. Conclusions: Studies examining treatment effects that are implemented over time should adjust for variation in treatment timing and treatment heterogeneity. Several new statistical methods exist that can implement these in a variety of software packages. [ABSTRACT FROM AUTHOR]

Abstract Background Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used. Methods A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported. Results Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity. Conclusions Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.

This study examines the impact of repealing Sunday blue laws on alcohol sales and retail competition, focusing on Connecticut's 2012 policy change allowing Sunday beer sales in grocery stores. Using nationwide data from 2004 to 2021, we find a short-term increase in beer sales post-policy change, but no significant long-term economic effects on grocery and liquor stores. Our analysis also shows similar treatment effects for chain and standalone liquor retailers, suggesting limited lasting implications for the liquor retail industry's performance and conduct after Sunday sale restrictions were lifted. [ABSTRACT FROM AUTHOR]

Background: In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights. Methods: To analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them. Results: A numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model. Conclusion: The method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution. [ABSTRACT FROM AUTHOR]

Covid-19 vaccination has posed crucial challenges to policymakers and health administrations worldwide. Besides the pressure posed by the pandemic, government administrations have to strive against vaccine hesitancy, which seems to be higher with respect to previous vaccination rollouts. To increase the vaccinated population, Ohio announced a monetary incentive as a lottery for those who were vaccinated. 18 other states followed this first example, with varying results. In this paper, we want to evaluate the effect of such policies within the potential outcome framework using the penalized synthetic control method. In the context of staggered treatment adoption, we estimate the effects at a disaggregated level using a panel dataset. We focused on policy outcomes at the county, state, and supra-state levels, highlighting differences between counties with different social characteristics and time frames for policy introduction. We also studied the treatment effect to see whether the impact of these monetary incentives was permanent or only temporary, accelerating the vaccination of citizens who would have been vaccinated in any case. [ABSTRACT FROM AUTHOR]

Background: Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used. Methods: A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported. Results: Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity. Conclusions: Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies. [ABSTRACT FROM AUTHOR]

Background: Stroke is an acute disorder and dysfunction of the focal neurological systemthat has long been recognized as one of the leading causes of death and severe disability inmost regions globally. This study aimed to supplement and exploitmultiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning. Methods: We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis. Results: We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predictingmortality in stroke patients.We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group. Conclusion: We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients.Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification. [ABSTRACT FROM AUTHOR]

Introduction: Age-associated depletion in nicotinamide adenine dinucleotide (NAD+) concentrations has been implicated in metabolic, cardiovascular, and neurodegenerative disorders. Supplementation with NAD+ precursors, such as nicotinamide riboside (NR), offers a potential therapeutic avenue against neurodegenerative pathologies in aging, Alzheimer's disease, and related dementias. A crossover, double-blind, randomized placebo (PBO) controlled trial was conducted to test the safety and efficacy of 8 weeks' active treatment with NR (1 g/day) on cognition and plasma AD biomarkers in older adults with subjective cognitive decline and mild cognitive impairment., Methods: The primary efficacy outcome was the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Secondary outcomes included plasma phosphorylated tau 217 (pTau 217 ), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Exploratory outcomes included Lumosity gameplay ( z -scores) for cognition and step counts from wearables. Mixed model for repeated measures was used for between-group comparisons; paired t -tests were used for within-individual comparisons., Results: Forty-six participants aged over 55 were randomized to NR-PBO or PBO-NR groups; 41 completed baseline visits, and 37 completed the trial. NR supplementation was safe and well tolerated with no differences in adverse events reported between NR and PBO treatment phases. For the between-group comparison, there was a 7% reduction in pTau 217 concentrations after taking NR, while an 18% increase with PBO ( p = 0.02). No significant between-group differences were observed for RBANS, other plasma biomarkers(GFAP and NfL), Lumosity gameplay scores or step counts. For the within-individual comparison, pTau 217 concentrations significantly decreased during the NR phase compared to the PBO ( p = 0.02), while step counts significantly increased during the NR phase than PBO ( p = 0.04)., Discussion: Eight weeks NR supplementation is safe and lowered pTau 217 concentrations but did not alter cognition as measured by conventional or novel digital assessments. Further research is warranted to validate NR's efficacy in altering pathological brain aging processes., Highlights: The integrated study design combines a two-arm parallel trial with a crossover phase, offering the opportunity to enhance sample size for within-individual analysis and assess carryover effects.NR is safe but did not alter cognition as measured by multi-modal assessments in SCD/MCI.For between-group comparison, pTau 217 levels decreased with NR and increased with PBO at 8-week follow-up.For within-individual comparison, step counts increased after NR and decreased after PBO.A larger, longer study with pharmacodynamic and pathophysiological biomarkers is needed to assess NR's disease-modifying effects., Competing Interests: C.Y.W., A.K., L.C., A.J.M., P.K., J.A.G., N.S., L.A.D., V.J.W., J.G., M.R., C.Y., E.G.V., H.H.D., and C.M.S. have no declarations of interest directly related to the contents of the work presented herein. S.E.A. has no declarations of conflict of interest directly related to the work presented here, but has consulted and/or served on advisory boards for Allyx Therapeutics, BioVie, Bob's Last Marathon, Daewoong Pharmaceuticals, Foster & Eldridge, LLP, Quince Therapeutics, Sage Therapeutics, and Vandria; received sponsored research grant support via his institution from the following commercial entities: AbbVie, Amylyx, Athira Pharma, Cyclerion Therapeutics, EIP Pharma, Ionis Pharmaceuticals, Janssen Pharmaceuticals, Inc., Novartis AG, Seer Biosciences, Inc. and vTv Therapeutics, Inc; and has received sponsored research grant support via his institution from the following non‐commercial entities: Alzheimer's Association, Alzheimer's Drug Discovery Foundation, Challenger Foundation, Cure Alzheimer's Fund, John Sperling Foundation, the National Institutes of Health and the Prion Alliance. R.E.T. is a paid consultant for, and holds equity in Chromadex, Inc and was not involved with the execution of the trial. Author disclosures are available in the supporting information., (© 2025 The Author(s). Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

BackgroundOver the last 50 years, clinical trials of novel interventions for acute heart failure (AHF) have, with few exceptions, been neutral or shown harm. We hypothesize that this might be related to a differential response to pharmacological therapy.MethodsWe studied the magnitude of treatment effect of rolofylline across clinical characteristics and plasma biomarkers in 2033 AHF patients and derived a biomarker-based responder sum score model. Treatment response was survival from all-cause mortality through day 180.ResultsIn the overall study population, rolofylline had no effect on mortality (HR 1.03, 95% CI 0.82-1.28, p = 0.808). We found no treatment interaction across clinical characteristics, but we found interactions between several biomarkers and rolofylline. The biomarker-based sum score model included TNF-R1α, ST2, WAP four-disulfide core domain protein HE4 (WAP-4C), and total cholesterol, and the score ranged between 0 and 4. In patients with score 4 (those with increased TNF-R1α, ST2, WAP-4C, and low total cholesterol), treatment with rolofylline was beneficial (HR 0.61, 95% CI 0.40-0.92, p = 0.019). In patients with score 0, treatment with rolofylline was harmful (HR 5.52, 95% CI 1.68-18.13, p = 0.005; treatment by score interaction p

We have previously identified substantial antibiotic treatment heterogeneity, even among organism-specific and site-specific infections with treatment guidelines. Therefore, we sought to quantify the extent of treatment heterogeneity among patients hospitalized with P. aeruginosa pneumonia in the national Veterans Affairs Healthcare System from Jan-2015 to Apr-2018. Daily antibiotic exposures were mapped from three days prior to culture collection until discharge. Heterogeneity was defined as unique patterns of antibiotic treatment (drug and duration) not shared by any other patient. Our study included 5300 patients, of whom 87.5% had unique patterns of antibiotic drug and duration. Among patients receiving any initial antibiotic/s with a change to at least one anti-pseudomonal antibiotic (n = 3530, 66.6%) heterogeneity was 97.2%, while heterogeneity was 91.5% in those changing from any initial antibiotic/s to only anti-pseudomonal antibiotics (n = 576, 10.9%). When assessing heterogeneity of anti-pseudomonal antibiotic classes, irrespective of other antibiotic/s received (n = 4542, 85.7%), 50.5% had unique patterns of antibiotic class and duration, with median time to first change of three days, and a median of two changes. Real-world evidence is needed to inform the development of treatment pathways and antibiotic stewardship initiatives based on clinical outcome data, which is currently lacking in the presence of such treatment heterogeneity. [ABSTRACT FROM AUTHOR]

Exploiting changes in the geography of economic integration in Europe, this paper quantifies the effects of Brexit from ex post to ex ante using structural gravity. By isolating the directional treatment effects of EU agreements for the UK, the analysis reveals important heterogeneity across agreements, sectors, and within pairs. We find that these directional effects matter for the size and distribution of the welfare effects of Brexit—the withdrawal of the UK from EU agreements resulting into a return of trade costs to the situation quo ante. We make this point with the help of a modern multi-sector trade model that is able to capture inter- and intranational production networks. In line with other papers, the welfare costs of Brexit are higher in the UK than in most other EU countries. However, heterogeneity tends to attenuate overall costs while giving rise to substantial heterogeneity between EU27 members and sectors. A scenario that could shift bargaining power eliminates asymmetries in the costs of Brexit as soon as the UK fully liberalizes its market. [ABSTRACT FROM AUTHOR]

Calculation of the difference of means is the most common approach when analyzing treatment effects on continuous outcomes. Nevertheless, it is possible that the treatment has a different effect on patients who have a lower value of the outcome compared with patients who have a greater value of the outcome. The estimation of quantile treatment effects (QTEs) allows the analysis of treatment effects over the entire distribution of a continuous outcome, such as the duration of illness or the duration of hospital stay. Furthermore, most of these outcomes have asymmetric distributions with fat tails, and censored observations are not uncommon. These features can be accounted for in the analysis of the QTE. In this paper, we use the QTE approach to analyze the effect of zinc lozenges on common cold duration. We use the data set of the Mossad (1996) trial with zinc gluconate lozenges, and three data sets of trials with zinc acetate lozenges. In the Mossad (1996) trial, zinc gluconate lozenges shortened common cold duration on average by 4.0 days (95% CI 2.3–5.7 days). However, the QTE analysis indicates that 15- to 17-day colds were shortened by 8 days, and 2-day colds by just 1 day, for the group taking zinc lozenges. Thus, the overall 4.0-day average effect of zinc gluconate lozenges in the Mossad (1996) trial is inconsistent with our QTE findings for both short and long colds. Similar results were found in our QTE analysis of the pooled data sets of the three zinc acetate lozenge trials. The average effect of 2.7 days (95% CI 1.8–3.3 days) was inconsistent with the effects on short and long colds. The QTE approach may have broad usefulness for examining treatment effects on the duration of illness and hospital stay, and on other similar outcomes. [ABSTRACT FROM AUTHOR]

Differences in patient characteristics, including age, sex, and race influence the safety and effectiveness of drugs, biologic products, and medical devices. Here we provide a summary of the topics discussed during the opening panel at the 2018 Johns Hopkins Center for Excellence in Regulatory Science and Innovation symposium on Assessing and Communicating Heterogeneity of Treatment Effects for Patient Subpopulations: Challenges and Opportunities. The goal of this session was to provide a brief overview of FDA‐regulated therapeutics, including drugs, biologics and medical devices, and some of the major sources of heterogeneity of treatment effects (HTE) related to patient demographics, such as age, sex and race. The panel discussed the US Food and Drug Administration's role in reviewing and regulating drugs, devices, and biologic products and the challenges associated with ensuring that diverse patient populations benefit from these therapeutics. Ultimately, ensuring diverse demographic inclusion in clinical trials, and designing basic and clinical research studies to account for the intended patient population's age, sex, race, and genetic factors among other characteristics, will lead to better, safer therapies for diverse patient populations. [ABSTRACT FROM AUTHOR]

This paper assesses the effects of deep trade agreements on agri-food global value chain (GVC) integration. We employ a theory-consistent gravity model and utilize a detailed bilateral GVC flow dataset covering 1991 to 2020. Our findings indicate that such trade agreements favor forward integration into agri-food GVCs more than backward integration. The analysis of how these effects unfold over time suggests a delay of up to four years before significant changes in GVC integration are observable. Moreover, deeper trade agreements exert a stronger influence on GVC integration, with specific provisions related to the regulation of standards and foreign investment acting as catalysts for agri-food GVC integration. Conversely, clauses regarding intellectual property rights and geographical indicators obstruct GVC integration, while developed countries benefit most from GVC integration. These insights are vital for policymakers seeking to mitigate the unequal benefits of deep trade agreements in agri-food GVC integration. [ABSTRACT FROM AUTHOR]

We develop a method for data‐driven estimation and analysis of heterogeneity in cost‐effectiveness analyses (CEA) with experimental or observational individual‐level data. Our implementation uses causal forests and cross‐fitted augmented inverse probability weighted learning to estimate heterogeneity in incremental outcomes, costs and net monetary benefits, as well as other parameters relevant to CEA. We also show how the results can be visualized in relevant ways for the analysis of heterogeneity in CEA, such as using individual‐level cost effectiveness planes. Using a simulated dataset and an R package implementing our methods, we show how the approach can be used to estimate the average cost‐effectiveness in the entire sample or in subpopulations, explore and analyze the heterogeneity in incremental outcomes, costs and net monetary benefits (and their determinants), and learn policy rules from the data. [ABSTRACT FROM AUTHOR]

We have previously identified substantial antibiotic treatment heterogeneity, even among organism-specific and site-specific infections with treatment guidelines. Therefore, we sought to quantify the extent of treatment heterogeneity among patients hospitalized with P. aeruginosa pneumonia in the national Veterans Affairs Healthcare System from Jan-2015 to Apr-2018. Daily antibiotic exposures were mapped from three days prior to culture collection until discharge. Heterogeneity was defined as unique patterns of antibiotic treatment (drug and duration) not shared by any other patient. Our study included 5300 patients, of whom 87.5% had unique patterns of antibiotic drug and duration. Among patients receiving any initial antibiotic/s with a change to at least one anti-pseudomonal antibiotic (n = 3530, 66.6%) heterogeneity was 97.2%, while heterogeneity was 91.5% in those changing from any initial antibiotic/s to only anti-pseudomonal antibiotics (n = 576, 10.9%). When assessing heterogeneity of anti-pseudomonal antibiotic classes, irrespective of other antibiotic/s received (n = 4542, 85.7%), 50.5% had unique patterns of antibiotic class and duration, with median time to first change of three days, and a median of two changes. Real-world evidence is needed to inform the development of treatment pathways and antibiotic stewardship initiatives based on clinical outcome data, which is currently lacking in the presence of such treatment heterogeneity.

Purpose: The purpose of this study was to disclose the variability of pathways currently taken in the treatment of adolescent patients from diagnosis to final follow-up with a view to developing a more homogenous system. Patients & methods: A cross-sectional, observational and retrospective study of the cancer diagnosis and assignment to medical care teams in adolescent patients (12-20 years) from January 2008 to December 2018 was conducted. A total of 345 adolescent patients aged between 12 and 20 years, diagnosed with cancer and treated at Hospital Clinico Universitario Virgen de la Arrixaca were included. Results: CNS tumors, followed by leukemia were the most frequent tumors. At the time of diagnosis, the highest incidences of patients were assisted in the pediatrics service adult oncology service (21.7%) and hematology (11%). Conclusion: Our aim is to highlight the need for a better transition for patients from pediatric to adult oncology and hematology services. [ABSTRACT FROM AUTHOR]

In the presence of treatment heterogeneity due to unknown grouping information, standard methods that assume homogeneous treatment effects cannot capture the subgroup structure in the population. To accommodate such heterogeneity, we propose a concave fusion approach to identifying the subgroup structures and estimating the treatment effects for a semiparametric linear regression with censored data. In particular, the treatment effects are subject-dependent and subgroup-specific, and our concave fusion penalized method conducts the subgroup analysis without needing to know the individual subgroup memberships in advance. The proposed estimation procedure automatically identifies the subgroup structure and simultaneously estimates the subgroup-specific treatment effects. The proposed algorithm combines the Buckley-James iterative procedure and the alternating direction method of multipliers. The resulting estimators enjoy the oracle property, and simulation studies and a real-data application demonstrate the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when conducting opioid policy evaluation studies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms. [ABSTRACT FROM AUTHOR]

Wildland fire smoke exposures are an increasing threat to public health, highlighting the need for studying the effects of protective behaviours on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals in real-time and subsequently study the effectiveness, but also pose methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality, and ways to record their own health symptoms and actions taken to reduce smoke exposure. We propose a doubly robust estimator of the structural nested mean model that accounts for spatially and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework also handles informative missingness by inverse probability weighting of estimating functions. We evaluate the method using extensive simulation studies and apply it to Smoke Sense data to increase the knowledge base about the relationship between health preventive measures and health-related outcomes. Our results show that the protective behaviours' effects vary over space and time and find that protective behaviours have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the U.S., Competing Interests: Conflicts of interest: None declared., (© The Royal Statistical Society 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

In recent years, the European Union is stressing the importance of monitoring and evaluating its policies, among which the common agricultural policy plays an important role. Policy evaluation, in order to provide reliable results on which to take important legislative decisions, should rely on robust methodological tools. A recent strand of literature casts some doubts about the reliability of the two-way fixed effect estimator when the effect of a treatment is heterogeneous across groups of units or over time. This estimator is widely used in agricultural economics to estimate the effect of policies where effect heterogeneity may be at stake. Using the European geographical indication (GI) policy, we compared the two-way fixed effects estimator with a novel non-parametric estimator that accounts for the issues created by effect heterogeneity. The results show that the two estimators, consistently with the concerns expressed by the technical literature, may lead to different estimates of the policy effect. This suggests that treatment effect heterogeneity is likely a concern when assessing the impact of GI-type policies. Therefore, the use of the standard estimator may lead to misleading conclusions and, as a result, to inappropriate policy actions.

In the analysis of censored survival data, to avoid a biased inference of treatment effects on the hazard function of the survival time, it is important to consider the treatment heterogeneity. Without requiring any prior knowledge about the subgroup structure, we propose a data driven subgroup analysis procedure for the heterogeneous Cox model by constructing a pairwise fusion penalized partial likelihood-based objective function. The proposed method can determine the number of subgroups, identify the group structure, and estimate the treatment effect simultaneously and automatically. A majorized alternating direction method of multipliers algorithm is then developed to deal with the numerically challenging high-dimensional problems. We also establish the oracle properties and the model selection consistency for the proposed penalized estimator. Our proposed method is evaluated by simulation studies and further illustrated by the analysis of the breast cancer data. [ABSTRACT FROM AUTHOR]

Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges of achieving this goal is that we usually do not have a priori knowledge of the grouping information of patients with respect to treatment effect. To address this problem, we consider a heterogeneous regression model which allows the coefficients for treatment variables to be subject-dependent with unknown grouping information. We develop a concave fusion penalized method for estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator based on a priori knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects using the proposed method. The proposed method is illustrated in simulation studies and illustrated with real data from an AIDS Clinical Trials Group Study. [ABSTRACT FROM AUTHOR]

Currently, placebo-controlled clinical trials report mean change and effect sizes, which masks information about heterogeneity of treatment effects (HTE). Here, we present a method to estimate HTE and evaluate the null hypothesis (H 0 ) that a drug has equal benefit for all participants (HTE=0). We developed measure termed 'estimated heterogeneity of treatment effect' or eHTE, which estimates variability in drug response by comparing distributions between study arms. This approach was tested across numerous large placebo-controlled clinical trials. In contrast with variance-based methods which have not identified heterogeneity in psychiatric trials, reproducible instances of treatment heterogeneity were found. For example, heterogeneous response was found in a trial of venlafaxine for depression (p eHTE =0.034), and two trials of dasotraline for binge eating disorder (Phase 2, p eHTE =0.002; Phase 3, 4mg p eHTE =0.011; Phase 3, 6mg p eHTE =0.003). Significant response heterogeneity was detected in other datasets as well, often despite no difference in variance between placebo and drug arms. The implications of eHTE as a clinical trial outcomes independent from central tendency of the group is considered and the important of the eHTE method and results for drug developers, providers, and patients is discussed.

Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible prediction modeling of competing risks data using Bayesian Additive Regression Trees (BART). We study the simulation performance in two-sample scenarios as well as a complex regression setting, and benchmark its performance against standard regression techniques as well as random survival forests. We illustrate the use of the proposed method on a recently published study of patients undergoing hematopoietic stem cell transplantation. [ABSTRACT FROM AUTHOR]

In recent years, the European Union is stressing the importance of monitoring and evaluating its policies, among which the common agricultural policy plays an important role. Policy evaluation, in order to provide reliable results on which to take important legislative decisions, should rely on robust methodological tools. A recent strand of literature casts some doubts about the reliability of the two-way fixed effect estimator when the effect of a treatment is heterogeneous across groups of units or over time. This estimator is widely used in agricultural economics to estimate the effect of policies where effect heterogeneity may be at stake. Using the European geographical indication (GI) policy, we compared the two-way fixed effects estimator with a novel non-parametric estimator that accounts for the issues created by effect heterogeneity. The results show that the two estimators, consistently with the concerns expressed by the technical literature, may lead to different estimates of the policy effect. This suggests that treatment effect heterogeneity is likely a concern when assessing the impact of GI-type policies. Therefore, the use of the standard estimator may lead to misleading conclusions and, as a result, to inappropriate policy actions. [ABSTRACT FROM AUTHOR]

Objective: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.Materials and Methods: A multicenter, retrospective cohort study of 29 253 hospitalized adult sepsis patients between 2010 and 2013 in Northern California. We applied an unsupervised machine learning method, Latent Dirichlet Allocation, to the orders, medications, and procedures recorded in the electronic health record within the first 24 hours of each patient's hospitalization to uncover empiric treatment topics across the cohort and to develop computable clinical signatures for each patient based on proportions of these topics. We evaluated how these topics correlated with common sepsis treatment and outcome metrics including inpatient mortality, time to first antibiotic, and fluids given within 24 hours.Results: Mean age was 70 ± 17 years with hospital mortality of 9.6%. We empirically identified 42 clinically recognizable treatment topics (eg, pneumonia, cellulitis, wound care, shock). Only 43.1% of hospitalizations had a single dominant topic, and a small minority (7.3%) had a single topic comprising at least 80% of their overall clinical signature. Across the entire sepsis cohort, clinical signatures were highly variable.Discussion: Heterogeneity in sepsis is a major barrier to improving targeted treatments, yet existing approaches to characterizing clinical heterogeneity are narrowly defined. A machine learning approach captured substantial patient- and population-level heterogeneity in treatment during early sepsis hospitalization.Conclusion: Using topic modeling based on treatment patterns may enable more precise clinical characterization in sepsis and better understanding of variability in sepsis presentation and outcomes. [ABSTRACT FROM AUTHOR]

Objective: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2 × 2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) trial, which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a prediabetic population.Materials and Methods: Our target data consisted of people with prediabetes serviced at the Duke University Health System. We used random survival forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes, and estimated the treatment effect in our local patient populations, as well as subpopulations, and compared the results with the traditional weighting approach.Results: Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach and we effectively estimated subgroup differences.Conclusions: The described method represents a straightforward approach to efficiently transporting an RCT result to any target population. [ABSTRACT FROM AUTHOR]

Summary: Previous reports of substantial inter‐individual differences in weight change following an exercise intervention are often based solely on the observed responses in the intervention group. Therefore, we aimed to quantify the magnitude of inter‐individual differences in exercise‐mediated weight change. We synthesized randomized controlled trials (RCTs) of structured, supervised exercise interventions. Fourteen electronic databases were searched for relevant studies published up to March 2017. Search terms focused on structured training, RCTs and body weight. We then sifted these results for those RCTs (n = 12, 1500 participants) that included relevant comparator group data. Standard deviations (SDs) of weight change were extracted, thereby allowing the SD for true inter‐individual differences in weight loss to be calculated for each study. Using a random effects meta‐analysis, the pooled SD (95% CI) for true individual responses was 0.8 (−0.9 to 1.4) kg. The 95% prediction interval (based on 2SDs) for true inter‐individual responses was −2.8 to 3.6 kg. The probability (% chance) that the true individual response variability would be clinically meaningful (>2.5 kg) in a future study in similar settings was 23% (‘unlikely’). Therefore, we conclude that evidence is limited for the notion that there are clinically important individual differences in exercise‐mediated weight change. [ABSTRACT FROM AUTHOR]

Under treatment effect heterogeneity, an instrument identifies the instrument-specific local average treatment effect (LATE). With multiple instruments, two-stage least squares (2SLS) estimand is a weighted average of different LATEs. What is often overlooked in the literature is that the postulated moment condition evaluated at the 2SLS estimand does not hold unless those LATEs are the same. If so, the conventional heteroscedasticity-robust variance estimator would be inconsistent, and 2SLS standard errors based on such estimators would be incorrect. I derive the correct asymptotic distribution, and propose a consistent asymptotic variance estimator by using the result of Hall and Inoue (2003, Journal of Econometrics) on misspecified moment condition models. This can be used to correctly calculate the standard errors regardless of whether there is more than one LATE or not. [ABSTRACT FROM AUTHOR]

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model, which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk. Supplementary material for this article is available online. [ABSTRACT FROM AUTHOR]

• Beech wood torrefaction at different conditions using comprehensive instrumentation. • Heat treatment heterogeneity can be assessed by water vapour sorption measurements. • The data-set allowed a complete computational code to be validated. • Torrefaction schedule should be adapted to the particle size and its initial MC. • Treatment conditions meeting users' expectations found by probabilistic optimization. This paper uses a comprehensive computational model to propose optimal wood torrefaction conditions by probabilistic optimization. Its main outcome is to propose tailor-made heat treatment conditions (temperature levels-duration of mild pyrolysis at temperature levels ranging from 200 to 300 °C) to meet users' expectations in terms of overall mass loss, duration and homogeneity of treatment. To this purpose, beech wood boards were torrefied with a usual 3-steps treatment schedule (drying, heating and cooling) under contrasting configurations in a well-instrumented device. The heterogeneity of the treatment within the wood sample was assessed through X-ray attenuation profile and water vapour sorption isotherm. These results allowed the model to be validated. In particular, it predicts the evolution of the mass loss and internal temperatures with good accuracy, including the temperature overshot. The results highlight the need to adjust the heat treatment schedule to each input parameter such as the wood piece dimensions and its initial moisture content or density, in order to limit the effect of exothermic reactions. The torrefaction model was then embedded in a probabilistic optimization process. A case study demonstrates the ability of the model to propose an alternative 3-steps treatment schedule able to reach the target mass loss while controlling the temperature overshot within the wood piece. [ABSTRACT FROM AUTHOR]