Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning.

Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effectively manage non-recurrent traffic congestion and enhance road safety. This study conducts a comprehensive causal analysis of traffic incident duration using a data collected over a long time and including different types of roads across the city of Tianjin, China. Employing the innovative framework of causal forests with biased machine learning (CF-DML) techniques, this study advances beyond traditional methods by focusing on interpreting the causal relationships between various factors and incident duration, emphasizing the role of heterogeneity among these factors. The CF-DML framework enables the assessment of the average treatment effects (ATEs) of various factors on incident duration. Notably, the significant influence of road type and suburban setting on treatment effects is underscored, which is generally consistent with the results obtained through classical methods. Second, to look more closely at the important factors such as road and collision types, a conditional average treatment effects (CATE) analysis is conducted, explaining heterogeneity through a causal heterogeneity tree. Third, based on insights from causal analysis, policies related to lane configurations are explored, emphasizing the necessity of considering causal effects in traffic management decisions. The CF-DML framework enhances our understanding of traffic incident dynamics, contributing to improved road safety and traffic flow in diverse urban environments.

A Bayesian beta-binomial piecewise growth mixture model for longitudinal overdispersed binomial data.

In a recent 12-week smoking cessation trial, varenicline tartrate failed to show significant improvements in enhancing end-of-treatment abstinence when compared with placebo among adolescents and young adults. The original analysis aimed to assess the average effect across the entire population using timeline followback methods, which typically involve overdispersed binomial counts. We instead propose to investigate treatment effect heterogeneity among latent classes of participants using a Bayesian beta-binomial piecewise linear growth mixture model specifically designed to address longitudinal overdispersed binomial responses. Within each class, we fit a piecewise linear beta-binomial mixed model with random changepoints for each study group to detect critical windows of treatment efficacy. Using this model, we can cluster subjects who share similar characteristics, estimate the class-specific mean abstinence trends for each study group, and quantify the treatment effect over time within each class. Our analysis identified two classes of subjects: one comprising high-abstinent individuals, typically young adults and light smokers, in which varenicline led to improved abstinence; and another comprising low-abstinent individuals for whom varenicline showed no discernible effect. These findings highlight the importance of tailoring varenicline to specific participant subgroups, thereby advancing precision medicine in smoking cessation studies.

Do health care quality improvement policies work for all? Distributional effects by baseline quality in South Africa.

Health care quality improvement (QI) initiatives are being implemented by a number of low- and middle-income countries. However, there is concern that these policies may not reduce, or may even worsen, inequities in access to high-quality care. Few studies have examined the distributional impact of QI programmes. We study the Ideal Clinic Realization and Maintenance program implemented in health facilities in South Africa, assessing whether the effects of the program are sensitive to previous quality performance. Implementing difference-in-difference-in-difference and changes-in-changes approaches we estimate the effect of the program on quality across the distribution of past facility quality performance. We find that the largest gains are realized by facilities with higher baseline quality, meaning this policy may have led to a worsening of pre-existing inequity in health care quality. Our study highlights that the full consequences of QI programmes cannot be gauged solely from examination of the mean impact.

Heterogeneity of Treatment Effects of Laser Epilation on Pilonidal Disease Recurrence: A Randomized Clinical Trial.

Objective: To investigate the heterogeneity of treatment effects (HTE) of laser epilation in preventing pilonidal disease recurrence through analysis of prespecified clinical factors. Background: Pilonidal disease is a common, painful disease affecting 1% of the population aged 15 to 30 years with postoperative recurrence rates as high as 30% to 40%. Methods: Single-institution randomized controlled trial from September 2017 to September 2022 with 1-year follow-up, including patients aged 11 to 21 years with pilonidal disease undergoing gluteal cleft laser epilation and standard care (improved hygiene and mechanical or chemical depilation) or standard care alone. Results: In total, 302 patients were enrolled with 151 randomized to each intervention. 1-year follow-up was available for 96 patients in the laser group and 134 in the standard care group. There were no significant differences in treatment effects based on sex, body mass index, previous disease, prior surgical excision, or annual household income (all P > 0.05). HTE was identified by race and ethnicity (P = 0.005) and health insurance type (P = 0.001). Recurrence among non-Hispanic white patients was 4% (3/75) with laser treatment and 31.6% (31/98) with standard care versus 38.9% (7/18) with laser treatment and 38.2% (13/34) with standard care among all other racial/ethnic groups. Recurrence rates among privately insured patients were 4.0% (3/75) with laser treatment and 33.3% (29/87) with standard care versus 36.8% (7/19) with laser treatment and 29.7% (11/37) with standard care in patients with public insurance. Conclusions: The effectiveness of laser epilation to reduce pilonidal disease recurrence rates may vary based on race and ethnicity and insurance type. Additional studies are warranted to investigate this potential HTE.

Going beyond randomised controlled trials to assess treatment effect heterogeneity across target populations.

Methods have been developed for transporting evidence from randomised controlled trials (RCTs) to target populations. However, these approaches allow only for differences in characteristics observed in the RCT and real-world data (overt heterogeneity). These approaches do not recognise heterogeneity of treatment effects (HTE) according to unmeasured characteristics (essential heterogeneity). We use a target trial design and apply a local instrumental variable (LIV) approach to electronic health records from the Clinical Practice Research Datalink, and examine both forms of heterogeneity in assessing the comparative effectiveness of two second-line treatments for type 2 diabetes mellitus. We first estimate individualised estimates of HTE across the entire target population defined by applying eligibility criteria from national guidelines (n = 13,240) within an overall target trial framework. We define a subpopulation who meet a published RCT's eligibility criteria ('RCT-eligible', n = 6497), and a subpopulation who do not ('RCT-ineligible', n = 6743). We compare average treatment effects for pre-specified subgroups within the RCT-eligible subpopulation, the RCT-ineligible subpopulation, and within the overall target population. We find differences across these subpopulations in the magnitude of subgroup-level treatment effects, but that the direction of estimated effects is stable. Our results highlight that LIV methods can provide useful evidence about treatment effect heterogeneity including for those subpopulations excluded from RCTs.

The Effects of Age and Treatment Intensity on Behavioral Target Mastery With Applied Behavior Analysis (ABA) Intervention Using Causal Moderation Models.

Introduction Applied behavior analysis (ABA) is a therapy that focuses on improving specific behaviors using positive and negative reinforcement through antecedents, behaviors, and consequences, particularly in individuals with autism and other developmental disorders. It uses the principles of learning theory to bring about meaningful and positive changes in behavior. In ABA treatment, intensity refers to the amount and frequency of therapy an individual receives. This includes weekly hours, session trials, and overall duration. Intensive treatment involves more hours and trials tailored to individual needs and responses. Younger individuals, particularly those with autism, often receive more intensive therapy because early intervention leads to better outcomes. Programs may recommend 25-40 hours per week for young children. As children age, therapy may become less intensive, focusing on specific skills. The study explores how age and treatment intensity affect the mastery of behavioral targets in ABA interventions. Materials and methods This study involved 100 participants (89 children, four adults, and seven instances where the individuals' ages were not recorded due to random data entry errors (MCAR)) who received ABA treatment over three months. The treatments included functional analysis, discrete trials, and mass and naturalistic training. Data on the mastery of target behaviors were collected using the Catalyst software (New York, New York). The primary outcome was the percentage of mastered behavioral targets, indicating the effectiveness of the ABA treatment. Several predictors were examined, including the participant's age and treatment intensity variables, such as the average number of trials and teaching days to achieve behavioral mastery. The interaction effects between age and these treatment intensity variables were analyzed. The study used descriptive and inferential statistics to explore these interactions, including correlational and multiple regression analyses with causal moderator modeling. Results In Model 1, a baseline multiple regression analysis showed that average teaching days significantly predict the percentage of targets mastered. However, its limited explanatory power suggests other variables also play a role. Model 2 introduced interaction effects using causal models, revealing that age moderates the relationship between treatment variables and behavioral outcomes. This model provided a more nuanced understanding but still had room for improvement. Model 3 further refined the approach, achieving higher R-values and lower standard error. It highlighted age's significant role in modifying the impact of teaching days on mastery. This model's superior performance emphasizes the importance of considering age as a moderating factor in ABA interventions, leading to more effective and personalized behavior therapy. Conclusions This study significantly enhances our understanding of the complex interactions between age and treatment intensity within ABA interventions. Practitioners and researchers can develop more tailored and effective therapeutic strategies by identifying and leveraging these interactions. This approach optimizes the treatment process and ensures that interventions are personalized to meet the unique needs of each individual. Ultimately, this leads to more successful outcomes in behavioral therapy, fostering improved adaptive behaviors and overall development.

A 2-stage model of heterogenous treatment effects for brain atrophy in multiple sclerosis utilizing the MS PATHS research network.

BACKGROUND: Two-stage models of heterogenous treatment effects (HTE) may advance personalized medicine in multiple sclerosis (MS). Brain atrophy is a relatively objective outcome measure that has strong relationships to MS prognosis and treatment effects and is enabled by standardized MRI. OBJECTIVES: To predict brain atrophy outcomes for patients initiating disease-modifying therapies (DMT) with different efficacies, considering the patients' baseline brain atrophy risk measured via brain parenchymal fraction (BPF). METHODS: Analyses included patients enrolled in the Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) network who started DMT and had complete baseline data and ≥ 6-month brain MRI follow-up. All brain MRIs were acquired using standardized acquisition sequences on Siemens 3T scanners. BPF change risk was derived by linear mixed effects models using baseline covariates. Model performance was assessed by predicted versus actual BPF change R2. Propensity score (PS) weighting was used to balance covariates between groups defined by DMT efficacy (high: natalizumab, alemtuzumab, ocrelizumab, and rituximab; moderate: dimethyl fumarate, fingolimod, and cladribine; low: teriflunomide, interferons, and glatiramer acetate). HTE models predicting 1 year change in BPF were built using a weighted linear mixed effects model with low-efficacy DMT as the reference. RESULTS: Analyses included 581 high-, 183 moderate-, and 106 low-efficacy DMT-treated patients. The mean and median number of brain MRI observations per treatment period were 2.9 and 3.0, respectively. Risk model performance R2=0.55. After PS weighting, covariate standardized mean differences were <10 %, indicating excellent balance across measured variables. Changes in BPF between baseline and follow-up were found to be statistically significant (p < 0.001), suggesting a pathological change. Patients with low brain atrophy risk had a similar outcome regardless of DMT selection. In patients with high brain atrophy risk, high- and moderate-efficacy DMTs performed similarly, while a 2-fold worse BPF change was predicted for patients selecting low-efficacy DMTs (p < 0.001). Similar results were observed in a sensitivity analysis adjusting for pseudoatrophy effects in a sub-population of patients treated with natalizumab. CONCLUSIONS: The relative benefit of selecting higher efficacy treatments may vary depending on patients' baseline brain atrophy risk. Poor outcomes are predicted in individuals with high baseline risk who are treated with low-efficacy DMTs.

Efficacy of KarXT on negative symptoms in acute schizophrenia: A post hoc analysis of pooled data from 3 trials.

BACKGROUND: Currently approved antipsychotics do not adequately treat negative symptoms (NS), which are a major determinant of functional disability in schizophrenia. KarXT, an M1 /M4 preferring muscarinic receptor agonist, has shown efficacy as a broad-spectrum monotherapy for the treatment of schizophrenia in participants with acute psychosis. Post hoc analyses evaluated the possibility that NS improve independently of positive symptoms with KarXT in a subgroup of participants with moderate-to-severe NS and no predominance of positive symptoms. METHODS: Data were pooled from the three pivotal trials of KarXT monotherapy in people with schizophrenia with an acute exacerbation of psychosis. All 3 studies used similar 5-week randomized, double-blind, placebo-controlled designs (modified intention-to-treat sample N = 640). PANSS criteria proposed in the literature identified a subset of study participants (n = 64) with prominent NS. RESULTS: KarXT was significantly better than placebo on PANSS Marder Negative Factor Scores in the full sample (p < .001; Cohen's d = 0.42) and more so in the prominent NS subgroup (p < .001; Cohen's d = 1.18). Further, the KarXT effect in the NS subgroup remained significant after accounting for changes in positive symptoms, depression/anxiety, disorganization, and hostility. CONCLUSIONS: Participants with prominent NS revealed greater improvement of NS following KarXT therapy than the full sample that persisted after accounting for positive and other symptoms. While these findings must be interpreted with caution, they are consistent with the possibility that NS improvements associated with KarXT are not secondary to improvements in other symptom domains and support further investigation in larger, stable outpatient studies.

Cognitive Behavioral Psychotherapy as an Add-on in Comprehensive Outpatient Care of Non-affective Psychoses: A Multicenter Randomized-Controlled Effectiveness Trial in a Naturalistic Setting.

BACKGROUND AND HYPOTHESIS: Non-affective psychoses (NAP) are associated with severe consequences with regard to social functioning, physical health, employment, and suicidality. Treatment guidelines recommend cognitive behavioral therapy for psychosis (CBTp) as an effective additional treatment strategy to psychopharmacology. We hypothesized that outpatient CBTp has an add-on effect in individuals with NAP who already receive comprehensive outpatient care (COC) in Germany. STUDY DESIGN: In a randomized-controlled effectiveness trial, 6 months of COC + CBTp were compared to COC. The primary outcomes were change of symptom severity as assessed by the Positive and Negative Symptom Scale (pre-/post-treatment and 6-month follow-up). Mixed linear models and effect sizes were used to compare changes across treatment groups. Additionally, the number of readmissions was compared. STUDY RESULTS: N = 130 individuals with chronic NAP were recruited (COC + CBTp: n = 64, COC: n = 66). COC + CBTp participants significantly improved more regarding positive symptom severity (estimated mean difference at follow-up: -2.33, 95% CI: -4.04 to -0.61, P = .0083, d = 0.32) and general psychopathology (estimated mean difference at follow-up: -4.55, 95% CI: -7.30 to -1.81, P = .0013, d = 0.44) than the COC group. In both groups, negative symptom severity did not change significantly over time nor did groups differ regarding readmissions. CONCLUSION: The results underline an add-on benefit of CBTp in chronically ill individuals with NAP. Superiority of CBTp was demonstrated in comparison with high-quality comprehensive care and may also be true in different comprehensive care settings. CLINICAL TRIALS REGISTRATION: DRKS00015627.

Individualized treatment effects of a digital alcohol intervention and their associations with participant characteristics and engagement.

AIMS: Conditional average treatment effects are often reported in intervention studies, in which assumptions are made regarding how effects are similar across a heterogeneous sample. Nonetheless, differing factors, such as genetics, age, and sex, can impact an intervention's effect on outcomes. The study aimed to estimate the individualized effects of a digital alcohol intervention among individuals looking online to reduce their drinking. METHODS: We used data from a randomized controlled trial (RCT), including 2129 adults from the Swedish general population. The RCT concerned a text message-based alcohol intervention that sought to engender change through increasing knowledge on how to change and instilling confidence in changing behaviour. Outcomes were total weekly alcohol consumption and monthly heavy episodic drinking. Individualized treatment effects were modelled using baseline characteristics (age, gender, alcohol consumption, and psychosocial variables) and engagement with the intervention content. RESULTS: We found evidence that the effects of the digital alcohol intervention were heterogeneous concerning participants' age, baseline alcohol consumption, confidence, and importance. For heavy episodic drinking, there was evidence that effects were heterogeneous concerning age, sex, and baseline alcohol consumption. Overall, women, older individuals, and heavier drinkers benefitted more from the intervention in terms of effect size. In addition, participants who engaged more with the goal-setting and screening content reported better outcomes. CONCLUSIONS: The results highlight how different individuals respond differently to a digital alcohol intervention. This allows insight into who benefits the most and least from the intervention and highlights the potential merit of designing interventions adapted to different individuals' needs.

Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis.

In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing-remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing-remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment-placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.

Validation of predicted individual treatment effects in out of sample respondents.

Personalized medicine promises the ability to improve patient outcomes by tailoring treatment recommendations to the likelihood that any given patient will respond well to a given treatment. It is important that predictions of treatment response be validated and replicated in independent data to support their use in clinical practice. In this paper, we propose and test an approach for validating predictions of individual treatment effects with continuous outcomes across samples that uses matching in a test (validation) sample to match individuals in the treatment and control arms based on their predicted treatment response and their predicted response under control. To examine the proposed validation approach, we conducted simulations where test data is generated from either an identical, similar, or unrelated process to the training data. We also examined the impact of nuisance variables. To demonstrate the use of this validation procedure in the context of predicting individual treatment effects in the treatment of alcohol use disorder, we apply our validation procedure using data from a clinical trial of combined behavioral and pharmacotherapy treatments. We find that the validation algorithm accurately confirms validation and lack of validation, and also provides insights into cases where test data were generated under similar, but not identical conditions. We also show that the presence of nuisance variables detrimentally impacts algorithm performance, which can be partially reduced though the use of variable selection methods. An advantage of the approach is that it can be widely applied to different predictive methods.

Heterogeneous treatment effects of sodium-glucose cotransporter 2 inhibitors on risk of dementia in people with type 2 diabetes: A population-based cohort study.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain. METHODS: Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016-2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all-cause dementia. RESULTS: Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all-cause dementia over a 3.2-year follow-up, yielding a lower risk for SGLT2 inhibitors (RD, -2.5%; 95% CI, -3.0% to -2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from -4.3% (-5.5 to -3.2) to -0.9% (-1.9 to 0.2). DISCUSSION: Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all-cause dementia, but the association varied among different subgroups. HIGHLIGHTS: New users of sodium-glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all-cause dementia as compared to those of sulfonylureas. The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease. A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas.

Bronchial artery chemoembolization in the treatment of refractory central lung cancer with atelectasis.

Objective: This study aims to explore the clinical application of bronchial artery chemoembolization (BACE) in managing refractory central lung cancer with atelectasis. Methods: The retrospective case series includes patients diagnosed with refractory central lung cancer and atelectasis who underwent BACE treatment at Yueyang Integrated Traditional Chinese and Western Medicine Hospital, affiliated with Shanghai University of Traditional Chinese Medicine, from January 2012 to December 2021. Results: All 30 patients with lung cancer successfully underwent BACE procedures. Their ages ranged from 62 to 88 years, with an average age of 67.53. The treatment interval was 21 days, and the treatment cycle ranged from 2 to 12 times, averaging 4.13 times. During the BACE procedures, the Karnofsky Performance Status (KPS) score after 2 to 3 BACE cycles showed a significant improvement (82.0 ± 10.1 vs 68.3 ± 14.0, P < 0.001) than that of before BACE. Only nutritional support and symptomatic treatment were performed after BACE, and no major hemoptysis were observed. During follow-up, 23 cases resulted in mortality, while seven survived. The median progression-free survival (PFS) and overall survival (OS) were 7.0 (95% CI: 4.6-9.4) and 10.0 (95% CI: 6.2-13.8) months, respectively, with 1-, 2-, and 3-year survival rates of 84.0%, 53.5%, and 11.3%, respectively. Eight cases exhibited bronchial recanalization and relief of atelectasis. According to the RECIST scale, there were 4 cases of complete response (CR), 16 cases of partial response (PR), 9 cases of stable disease (SD), and 1 case of progressive disease (PD). No serious adverse events were reported. Conclusion: BACE might be a safe intervention for refractory central lung cancer accompanied by atelectasis. The procedure exhibits satisfactory outcomes in tumor control, atelectasis relief, and enhancement of quality of life, warranting further investigation.

Assurance methods for designing a clinical trial with a delayed treatment effect.

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur.

Comparative Effects of Applied Behavior Analysis on Male and Female Individuals With Autism Spectrum Disorder.

Introduction Current evidence-based treatments for autism spectrum disorder (ASD) are based on applied behavior analysis (ABA). However, research on gender differences in ABA therapy response is limited. This study seeks to (1) confirm the 4:1 male-to-female ratio reported in the literature and (2) identify any possible gender differences in target behaviors over seven timepoints measured every two weeks. Materials and methods  For three months, from March 19, 2023, to June 11, 2023, a team of 3-5 behavioral technicians per individual collected daily data on general target mastery for 100 individuals with ASD treated with ABA. Data was collected at seven timepoints every two weeks. Descriptive demographics were computed. Two independent sample t-tests were performed to determine significant or nonsignificant gender differences with the seven timepoint variables.  Results Nonstatistically significant gender differences (p > .05) were found on all seven cumulative target behavior timepoints measured at two-week intervals. For targets mastered Time 1, baseline between males and females, there was no significant difference in the means for males (M = 1.0571, SD = 1.9196) and females (M = 2.0455, SD = 3.9457) (t(90) = -1.591, p = 0.115, confidence interval (CI) = -2.2223, 0.2456, d = -0.389). For targets mastered Time 2, two weeks between males and females, there was no significant difference in the means for males (M = 3.7132; SD = 4.5065) and females (M = 4.0682, SD = 5.1508) (t(88) = -0.310, p = 0.757, CI = -2.6305, 1.92056, d = -0.076). For targets mastered Time 3, four weeks between males and females, there was no significant difference in the means for males (M = 7.0956; SD = 8.7781) and females (M = 8.6136; SD = 11.2799) (t(88) = -0.656, p = 0.514, CI = -6.1173, 3.0811, d = -0.161). For targets mastered Time 4, six weeks between males and females, there was no significant difference in the means for males (M = 13.1728, SD = 16.2003) and females (M = 13.0682, SD = 16.9272) (t(88) = 0.026, p = 0.979, CI = -7.8779, 8.0871, d = 0.006). For targets mastered Time 5, eight weeks between males and females, there was no significant difference in the means for males (M = 17.2096; SD = 18.8546) and females (M = 17.4286, SD = 22.1683) (t(87) = -0.045, p = 0.965, CI = -9.9773, 9.5393, d = -0.011). For targets mastered Time 6, 10 weeks between males and females, there was no significant difference in the means for males (M = 21.0074, SD = 21.3329) and females (M = 20.6818, SD = 26.1231) (t(88) = 0.059, p = 0.953, CI = -10.6752, 11.3262, d = 0.014). For targets mastered Time 7, 12 weeks between males and females, there was no significant difference in the means for males (M = 26.1196, SD = 24.2235) and females (M = 29.6364, SD = 33.7406) (t(89) = -0.536, p = 0.593, CI = -16.5431, 9.5094, d = -0.131). Conclusions The study indicates that ABA treatments may be equally beneficial for both genders with ASD, showing no significant gender differences. However, the broad CIs in this study imply a level of statistical uncertainty, indicating potential gender differences, suggesting the results may not be uniform across genders. These findings challenge assumptions on gender-specific treatment responses, implying that ABA treatments shouldn't be recommended based on gender. Instead, individual needs should guide treatment recommendations. Future research could consider other response moderators like age, ASD severity, or coexisting mental health conditions.

Propensity Weighted federated learning for treatment effect estimation in distributed imbalanced environments.

Estimating treatment effects from observational data in medicine using causal inference is a very relevant task due to the abundance of observational data and the ethical and cost implications of conducting randomized experiments or experimental interventions. However, how could we estimate the effect of a treatment in a hospital that has very restricted access to treatment? In this paper, we want to address the problem of distributed causal inference, where hospitals not only have different distributions of patients, but also different treatment assignment criteria. Furthermore, it is necessary to take into account that due to privacy restrictions, personal patient data cannot be shared between hospitals. To address this problem, we propose an adaptation of the federated learning algorithm FederatedAveraging to one of the most advanced models for the prediction of treatment effects based on neural networks, TEDVAE. Our algorithm adaptation takes into account the shift in the treatment distribution between hospitals and is therefore called Propensity WeightedFederatedAveraging (PW FedAvg). As the distributions of the assignment of treatments become more unbalanced between the nodes, the estimation of causal effects becomes more challenging. The experiments show that PW FedAvg manages to reduce errors in the estimation of individual causal effects when imbalances are large, compared to VanillaFedAvg and other federated learning-based causal inference algorithms based on the application of federated learning to linear parametric models, Gaussian Processes and Random Fourier Features.

End points in clinical trials in diffuse large B-cell lymphoma: time for more dialogue?

We observed lack of clarity and consistency in end point definitions of large randomized clinical trials in diffuse large B-cell lymphoma. These inconsistencies are such that trials might, in fact, address different clinical questions. They complicate interpretation of results, including comparisons across studies. Problems arise from different ways to account for events occurring after randomization including absence of improvement in disease status, treatment discontinuation or the initiation of new therapy. We call for more dialogue between stakeholders to define with clarity the questions of interest and corresponding end points. We illustrate that assessing different end point rules across a range of plausible patient journeys can be a powerful tool to facilitate such a discussion and contribute to better understanding of patient-relevant end points.

What is this article about? This article talks about the lack of clarity and consistency in the definitions of outcomes used in clinical trials that investigate new treatments for diffuse large B-cell lymphoma. This is mainly due to how these different outcome definitions handle events such as absence of improvement in disease status, treatment discontinuation or initiation of new treatment. The authors discuss how these inconsistencies make it hard to interpret the results of individual clinical trials and to compare results across clinical trials.Why is it important? Defining the above events and consequently defining outcomes affects what we can learn from the trials and can lead to different results. Some approaches may not reflect good and bad outcomes for patients appropriately. This makes it challenging for patients, physicians, health authorities and payors to understand the true benefit of treatments under investigation and which one is better.What are the key take-aways? This article serves as a call-to-action for more dialogue among all stakeholders involved in drug development and the decision-making process related to drug evaluations. There is an urgent need for clinical trials to be designed with more clarity and consistency on what is being measured so that relevant questions for patients and prescribing physicians are addressed. Understanding patient journeys will be key to successfully understand what truly matters to patients and how to measure the benefit of new treatments. Such discussions will contribute toward more clarity and consistency in the evaluation of new treatments.

Collaborative effect of the energy conservation and emission reduction fiscal policy in China.

Government-led national comprehensive demonstration cities for Energy Conservation and Emission Reduction Fiscal Policy (ECERFP) are pivotal for China in addressing environmental governance. Using a panel dataset covering 278 Chinese cities from 2003 to 2019, this study adopts the staggered difference-in-differences (DID) approach to investigate the synergistic impacts of ECERFP on pollution and carbon reduction. The findings indicate that ECERFP contributes to a 3% improvement in pollution reduction performance, a 1.5% enhancement in carbon reduction performance, and a 4% overall increase in combined pollution and carbon reduction efforts. Furthermore, the study examines the heterogeneous effects of ECERFP on environmental performance. ECERFP significantly influences the synergistic efforts in pollution and carbon reduction by fostering green innovation, enhancing energy allocation, and optimizing industrial structures. This study both theoretically and empirically outlines the specific pathways and mechanisms through which "incentive-based" green fiscal policy promotes synergistic pollution and carbon reduction, thus providing a pragmatic foundation for enhancing the role of fiscal policy in environmental governance.