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ObjectiveThe combination of d-methylphenidate and guanfacine (an α-2A adrenergic agonist) may be an effective alternative to either agent as monotherapy in children with attention-deficit/hyperactivity disorder (ADHD). This study investigated the neural mechanisms underlying medication effects using cortical source analysis of electroencephalography (EEG) data.MethodA total of 172 children with ADHD (aged 7-14; 118 boys) completed an 8-week randomized, double-blind, comparative study with 3 treatment arms: d-methylphenidate, guanfacine, or their combination. EEG modulations of brain oscillations at baseline and end point were measured during a spatial working memory task from cortical sources localized within the anterior cingulate (midfrontal) and primary visual cortex (midoccipital), based on previously reported ADHD and control differences. Linear mixed models examined treatment effects on EEG and performance measures.ResultsCombined treatment decreased midoccipital EEG power across most frequency bands and task phases. Several midoccipital EEG measures also showed significantly greater changes with combined treatment than with monotherapies. D-methylphenidate significantly increased midoccipital theta during retrieval, while guanfacine produced only trend-level reductions in midoccipital alpha during maintenance and retrieval. Task accuracy improved with combined treatment, was unchanged with d-methylphenidate, and worsened with guanfacine. Treatment-related changes in midoccipital power correlated with improvement in ADHD severity.ConclusionThese findings show that combined treatment ameliorates midoccipital neural activity associated with treatment-related behavioral improvements and previously implicated in visuo-attentional deficits in ADHD. Both monotherapies had limited effects on EEG measures, with guanfacine further showing detrimental effects on performance. The identified midoccipital EEG profile may aid future treatment monitoring for children with ADHD.Clinical trial registration informationSingle Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder (Project1); https://clinicaltrials.gov/; NCT00429273.Diversity & inclusion statementWe worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. We actively worked to promote sex and gender balance in our author group.

Farmers in Ethiopia and other sub-Saharan African countries often implement various agricultural land management (ALM) practices to mitigate the negative impact of land degradation, increase crop yield, improve income, and safeguard the security of food. Despite the widespread use of these measures, their effect on crop productivity and related income has received relatively little attention in research, particularly in the context of mixed farming systems. The objective of this study was to assess the effects of ALM practices on crop production and household income in southern Ethiopia. Data was gathered from 423 sampled respondents, which included adopters and non-adopters of ALM practices. This study adopted multistage sampling to gather the data through a survey questionnaire. This study’s findings reveal that ALM strategies have a considerable impact on crop productivity and the incomes of farm households (p

Purpose of the study. Determination of the expression of microRNA‑34, microRNA‑130, microRNA‑148, microRNA‑181, microRNA‑194 and microRNA‑605 in colon tumor tissue depending on the clinical and morphological features of the tumor and the effectiveness of treatment.Materials and methods. The study included 56 patients diagnosed with colorectal cancer aged 43 to 75 years with the average age of 54 years. Taking into account the local prevalence of the process patients received surgical or combined treatment, including neoadjuvant chemotherapy, in the clinics of the Cancer Research Institute, Tomsk NRMC. MicroRNA expression was determined by polymerase chain reaction (PCR) in real time.Results. The obtained information revealed the relation of microRNA‑130 to the tumor size. The development of regional metastases was associated with changes in microRNA‑130, microRNA‑194 and microRNA‑605. The level of histological organization of the tumor was associated with microRNA‑34, microRNA‑130, microRNA‑148, and the response to therapy – with microRNA‑130, microRNA‑148 and microRNA‑605. In addition, according to the study, the significance of microRNA‑130 was revealed, which is associated with tumor spread, histological differentiation and response to antitumor therapy.Conclusion. The features of expression of microRNA‑34, microRNA‑130, microRNA‑148, microRNA‑181, microRNA‑194 and microRNA‑605 associated with clinical and morphological features of colon tumors were revealed. Correlations between the studied indicators are noted, which probably determine the outcome and prognosis of the disease.

Introduction: In research, it is crucial to accurately estimate treatment effects and analyze experimental results. Common methods include comparing outcome differences between different groups and using linear regression models for analysis. However, observational studies may have significantly different distributions of confounding variables between control and treatment groups, leading to errors in estimating treatment effects. Methods: The propensity score methods can address this issue by weighting or matching samples to approximate the scenario of a randomized experiment and allow for more accurate estimation of treatment this paper. Results: We use propensity score methods to analyze three datasets from observational studies and draw conclusions different from those in the original text. Furthermore, we simulate three scenarios, and the results demonstrate the superiority of propensity score methods over methods such as linear regression in addressing selection bias. Discussion: Therefore, it is essential to thoroughly consider the characteristics of the data and select appropriate methods to ensure reliable conclusions in practical data analysis. [ABSTRACT FROM AUTHOR]

AbstractInterference occurs when a unit’s treatment (or exposure) affects another unit’s outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking real-world relevance or are based on parametric propensity score models. Here, we propose new causal estimands based on modification of the propensity score distribution which may be more relevant in many contexts and are not based on parametric models. Nonparametric sample splitting estimators of the new estimands are constructed, which allow for flexible data-adaptive estimation of nuisance functions and are consistent, asymptotically normal, and efficient, converging at the usual parametric rate. Simulations show the finite sample performance of the proposed estimators. The proposed methods are applied to evaluate the effect of water, sanitation, and hygiene facilities on diarrhea among children in Senegal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Estimating treatment effects has always been one of the hot issues in empirical research. It brings great challenges to estimating treatment effects because heterogeneity exists in the distribution of covariates between treated and controlled groups. Propensity score methods have been widely used to adjust for heterogeneity in observational studies. However, the propensity score is usually unknown and needs to be estimated. In this article, we propose a generalized single-index model to estimate the propensity score and use the propensity score residuals to reduce the estimation bias. The finite-sample performance of the proposed method is evaluated through simulation studies. We use the proposed method to evaluate the policy of "Sunshine Running" and find that the physical test scores of college students participating in the "Sunshine Running" can be improved by 3.72 points. [ABSTRACT FROM AUTHOR]

Nontuberculous mycobacterium (NTM) pulmonary disease is a clinical frequently occurring disease, and its incidence rate is gradually rising. The diagnosis and treatment of NTM pulmonary disease has attracted much attention. This paper collects relevant research data at home and abroad, reviewing the current status of NTM diagnosis and treatment research, summarizing research results, analyzing existing research shortcomings, and hoping to provide reference for the clinical diagnosis and treatment of NTM and provide reference basis for further research in the future. [ABSTRACT FROM AUTHOR]

Farmers in Ethiopia and other sub-Saharan African countries often implement various agricultural land management (ALM) practices to mitigate the negative impact of land degradation, increase crop yield, improve income, and safeguard the security of food. Despite the widespread use of these measures, their effect on crop productivity and related income has received relatively little attention in research, particularly in the context of mixed farming systems. The objective of this study was to assess the effects of ALM practices on crop production and household income in southern Ethiopia. Data was gathered from 423 sampled respondents, which included adopters and non-adopters of ALM practices. This study adopted multistage sampling to gather the data through a survey questionnaire. This study's findings reveal that ALM strategies have a considerable impact on crop productivity and the incomes of farm households (p<0.05). Moreover, the adoption of integrated physical and biological intervention practices for longer durations resulted in a steady increase in both crop productivity and household incomes. Furthermore, analysis of the effect of long-term treatment indicates that farms show a significant and increasing marginal benefit to production after six years of intervention. These findings imply that integrating ALM practices and maintaining them for an extended period (i.e., 10 years) will help to increase crop productivity and household incomes. Hence, adopting a wider range of physical and biological ALM measures and ensuring their continual adoption are key strategies for improving crop production and increasing household income. This strategy will have significant policy implications and provide a solid foundation for sustainable agricultural development. [ABSTRACT FROM AUTHOR]

An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify a prior distribution in order to obtain a posterior distribution is sometimes an artificial process that may introduce subjectivity or complexity into the analysis. This paper considers a compromise involving confidence distributions, which are probability distributions that summarize uncertainty about the treatment effect without the need for a prior distribution and in a way that is fully compatible with frequentist inference. The concept of a confidence distribution provides a posterior–like probability distribution that is distinct from, but exists in tandem with, the relative frequency interpretation of probability used in frequentist inference. Although they have been discussed for decades, confidence distributions are not well known among clinical trial statisticians and the goal of this paper is to discuss their use in analyzing treatment effects from randomized trials. As well as providing an introduction to confidence distributions, some illustrative examples relevant to clinical trials are presented, along with various case studies based on real clinical trials. It is recommended that trial statisticians consider presenting confidence distributions for treatment effects when reporting analyses of clinical trials. [ABSTRACT FROM AUTHOR]

Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included. [ABSTRACT FROM AUTHOR]

The mixture of a distribution of responses from untreated patients and a shift of that distribution is a useful model for the responses from a group of treated patients. The mixture model accounts for the fact that not all the patients in the treated group will respond to the treatment and consequently their responses follow the same distribution as the responses from untreated patients. The treatment effect in this context consists of both the fraction of the treated patients that are responders and the magnitude of the shift in the distribution for the responders. In this article, we introduce inference based on a pseudo-likelihood approach and compare it with an existing method of moment approach. An extensive simulation study is used to compare robust performance of the two approaches regarding point estimation, confidence regions, and confidence intervals. The methods are demonstrated on an illustrative blood pressure data set. [ABSTRACT FROM AUTHOR]

Estimating treatment (or policy or intervention) effects on a single individual or unit has become increasingly important in health and biomedical sciences. One method to estimate these effects is the synthetic control method, which constructs a synthetic control, a weighted average of control units that best matches the treated unit's pre-treatment outcomes and other relevant covariates. The intervention's impact is then estimated by comparing the post-intervention outcomes of the treated unit and its synthetic control, which serves as a proxy for the counterfactual outcome had the treated unit not experienced the intervention. The augmented synthetic control method, a recent adaptation of the synthetic control method, relaxes some of the synthetic control method's assumptions for broader applicability. While synthetic controls have been used in a variety of fields, their use in public health and biomedical research is more recent, and newer methods such as the augmented synthetic control method are underutilized. This paper briefly describes the synthetic control method and its application, explains the augmented synthetic control method and its differences from the synthetic control method, and estimates the effects of an antimalarial initiative in Mozambique using both the synthetic control method and the augmented synthetic control method to highlight the advantages of using the augmented synthetic control method to analyze the impact of interventions implemented in a single region. [ABSTRACT FROM AUTHOR]

A meta-analysis was performed to assess the benefits and safety profile of approved immune checkpoint inhibitors in hepatocellular carcinoma patients. Eligible studies were searched from Cochrane, Embase, and PubMed databases based on a well-established strategy. Following the exclusion of ineligible studies, 12 studies were included in this meta-analysis. Compared with control group, immune checkpoint inhibitors were associated with improved ORR (OR 3.03, 95% CI 2.26–4.05, P < 0.00001), SD (OR 0.77, 95% CI 0.62–0.95, P = 0.02), OS (HR 0.75, 95% CI 0.68–0.83, P < 0.00001), and PFS (HR 0.74, 95% CI 0.63–0.87, P < 0.0003). However, no significant differences were observed in DCR (OR 1.33, 95% CI 0.97–1.81, P = 0.07), PD (OR 0.90, 95% CI 0.67–1.21, P = 0.48), and all caused any-grade adverse events (OR 1.22, 95% CI 0.62–2.39, P = 0. 57), all caused ≥ grade 3 adverse events (OR 1.10, 95% CI 0.97–1.25, P = 0.14), treatment-related any-grade adverse events (OR 1.13, 95% CI 0.55–2.32, P = 0.73), and treatment-related ≥ grade 3 events (OR 0.82, 95% CI 0.34–1.97, P = 0.65) between the two groups. After subgroup analysis conducted, patients in the immune checkpoint inhibitor group compared with targeted drug group showed significant improvements in OS (HR 0.74, 95% CI 0.66–0.84, P < 0.00001) and PFS (HR 0.75, 95% CI 0.61–0.91, P = 0.004). Immune checkpoint inhibitors have demonstrated peculiar benefits in the treatment of HCC with an acceptable safety profile. Compared to targeted drugs, immune checkpoint inhibitors still offer advantages in the treatment of hepatocellular carcinoma. However, there is still considerable room for further improvement. [ABSTRACT FROM AUTHOR]

Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the unconfoundedness assumption using pre-treatment data. For statistical inference, we develop a normalised placebo test to address the asymmetry issue. The method is illustrated and evaluated on numerical simulations and a case study on air pollution alerts. [ABSTRACT FROM AUTHOR]

Collective action in the agricultural sector of Peru started in the form of agrarian production cooperatives around 1969. Following the collapse of the collectivisation program in the 1980s, external stakeholders helped to form marketing cooperatives in select sectors to discourage coca leaf production. Nowadays, cooperatives are only active in four sectors: banana, cocoa, coffee, and palm oil. Case study evidence of the performance of cooperatives in Peru is mixed, which raises the need for applied research to inform if cooperatives have been successful as instruments of smallholder development. We address the situation with empirical analysis of comprehensive survey data collected for the 2016-2019 period. Using multiple empirical techniques, we estimate the farm-level treatment effect of selling output to cooperatives. Generally, we find a positive effect of cooperatives on the price received and the quantity sold of their patrons. Palm oil cooperatives, which appear to have started in the recent past, form an exception. Implications of our findings are discussed.

Min Gao,1,2,&ast; Wenhua Sang,1,3,4,&ast; Kun Mi,1,3,4 Jiancong Liu,1,3,4 Yudong Liu,1,3,4 Wenge Zhen,1,3,4 Bang An2 1Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China; 2Xianyang Central Hospital, Xianyang Mental Health Center, Xianyang, People’s Republic of China; 3Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, People’s Republic of China; 4The Sixth Clinical Medical College of Hebei University, Baoding, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wenhua Sang, Department of Affective Disorders II, Hebei Provincial Mental Health Center, Hebei Key Laboratory of Major Mental and Behavioral Disorders, The Sixth Clinical Medical College of Hebei University, Baoding, People’s Republic of China, Email whsang997169@163.com Bang An, Xianyang Central Hospital, Xianyang Mental Health Center, Xianyang, People’s Republic of China, Email xyanbang@163.comObjective: Only about one-third of depressed patients respond to initial antidepressant treatment. Therefore, it is crucial to find effective predictors of antidepressants. The purpose of our study was to learn the relationship between EEG theta power, theta asymmetry, and the efficacy of escitalopram.Methods: The study included 34 patients with depression. Before and after each patient’s course of treatment, EEG data was gathered. Both the Hamilton Anxiety Scale (HAMA) and the 17-item Hamilton Depression Scale (HAMD-17) were evaluated simultaneously. The natural logarithm of right frontal theta power minus left frontal theta power was used to calculate inter-electrode theta asymmetry (AT).Results: First, our study found no statistically significant difference between intra-electrode theta power and inter-electrode AT before and after treatment (P ≥ 0.05). When we later looked at the data regarding treatment effects, the findings revealed that patients (n = 9) who did not respond to treatment had lower baseline theta power at C4 [6.190 (2.000, 12.990) vs 15.800 (7.255, 22.330), z = − 2.166, P = 0.030]. The two groups had no difference in other electrodes (P ≥ 0.05). The AT of C3/C4 in non-responders (n = 9) was lower [0.012 (0.795) vs 0.733 (0.539), t = − 3.224, P = 0.005]. However, there was no difference in inter-electrode AT between the two groups in F3/F4 and F7/F8 (P ≥ 0.05). We finally show that the theta power at C4 was negatively correlated with HAMD scores before treatment (r = − 0.346, P = 0.045).Conclusion: Our findings determined that increased theta power and positive asymmetry in the right frontal-central area correlate with favourable escitalopram treatment, providing a basis for finding predictive markers for antidepressants.Keywords: depression, EEG, theta power, theta asymmetry, treatment effect

Tumor-induced osteomalacia (TIO), also known as oncogenic osteomalacia, is very rare, with about 1000 reported cases globally. Removing most TIO culprit tumors requires the evaluation and intervention of orthopedic doctors. However, orthopedic doctors often have a poor understanding of the optical treatment of TIO due to its rarity. In addition, most TIO patients lack specific clinical manifestations. Also, the clinical localization and qualitative diagnosis of TIO are difficult and thus can easily be misdiagnosed and mistreated. Furthermore, the true incidence rate of TIO may be underestimated. Although many breakthroughs have been made in exploring the pathogenesis, clinical diagnosis, and treatment of TIO, rational and standardized orthopedic surgical treatment experience summary and sorting for TIO patients are lacking. In this article, the recent experience and progress in the field of orthopedic surgical treatment for TIO globally have been summarized, providing a theoretical basis and new clinical practice guidance for the rational treatment of TIO patients.

We investigate whether receiving health information changes human behavior by using a novel approach to inference in the fuzzy regression discontinuity design. The approach is robust to the strength of identification and allows for mean squared error optimal bandwidths as well as undersmoothing. It is based on the Anderson-Rubin test in the instrumental variable literature augmented with either robust bias correction or critical value adjustment. We find that the resulting confidence sets of the treatment effect are mostly wide or even unbounded. These findings indicate that we could not rule out most magnitudes of behavior change, including zero and non-zero ones. [ABSTRACT FROM AUTHOR]

A method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form, called neural kernels. The conditional average treatment effect is estimated by using the survival functions as outcomes of the control and treatment neural networks, which consist of a set of neural kernels with shared parameters. The neural kernels are more flexible and can accurately model a complex location structure of feature vectors. BENK does not require a large dataset for training due to its special way for training networks by means of pairs of examples from the control and treatment groups. The proposed method extends a set of models that estimate the conditional average treatment effect. Various numerical simulation experiments illustrate BENK and compare it with the well-known T-learner, S-learner and X-learner for several types of control and treatment outcome functions based on the Cox models, the random survival forest and the Beran estimator with Gaussian kernels. The code of the proposed algorithms implementing BENK is publicly available. [ABSTRACT FROM AUTHOR]

Objective To investigate the changes of sex hormone levels in polycystic ovary syndrome (PCOS) in infertile population after the assisted reproductive technology treatment, and to provide an evidence for the choice of the treatment. Methods The medical data of patients admitted to the First Affiliated Hospital of Kunming Medical University from January 2016 to June 2021 were collected and divided into PCOS group (103) and non-PCOS group (589) according to whether they were diagnosed with PCOS, and the sex hormone changes of the two groups were compared. Results The patients in PCOS group were younger and had the higher BMI, more sinus follicles, higher AMH value, and lower total Gn usage. The number of LH/FSH > 2 in PCOS group was higher than that in non-PCOS group (P < 0.05). After the treatment, LH in both groups decreased, FSH, E2 and (P < 0.05) increased; The difference of LH and E2 before and after the treatment in PCOS group was greater than that in non-PCOS group < 0.05). Conclusion Compared with non-PCOS infertile patients, the changes of sex hormone indexes in PCOS infertile patients before and after the treatment were more obvious. In order to obtain the better clinical effect in patients with polycystic ovaries, it is recommended to pay attention to the changes of related sex hormone levels in the course of subsequent treatment, and choose a reasonable treatment plan. [ABSTRACT FROM AUTHOR]

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Phase 3 trials using the anti‐amyloid antibodies aducanumab, lecanemab, donanemab, and high‐dose gantenerumab in prodromal and mild Alzheimer's disease dementia were heterogeneous in respect to statistical significance of effects. However, heterogeneity of results has not yet directly be quantified. METHODS: We used Bayesian random effects meta‐analysis to quantify evidence for or against a treatment effect, and assessed the size of the effect and its heterogeneity. Data were extracted from published studies where available and Web based data reports, assuming a Gaussian data generation process. RESULTS: We found moderate evidence in favor of a treatment effect (Bayes factor = 13.2). The effect was moderate to small with −0.33 (95% credible interval −0.54 to −0.10) points on the Clinical Dementia Rating – Sum of Boxes (CDR‐SB) scale. The heterogeneity parameter was low to moderate with 0.21 (0.04 to 0.45) CDR‐SB points. DISCUSSION: Heterogeneity across studies was moderate despite some trials reaching statistical significance, while others did not. This suggests that the negative aducanumab and gantenerumab trials are in full agreement with the expected effect sizes. [ABSTRACT FROM AUTHOR]

Many clinical studies evaluate the benefit of a treatment based on both survival and other continuous/ordinal clinical outcomes, such as quality of life scores. In these studies, when subjects die before the follow‐up assessment, the clinical outcomes become undefined and are truncated by death. Treating outcomes as "missing" or "censored" due to death can be misleading for treatment effect evaluation. We show that if we use the median in the survivors or in the always‐survivors as estimands to summarize clinical outcomes, we may conclude that a trade‐off exists between the probability of survival and good clinical outcomes, even in settings where both the probability of survival and the probability of any good clinical outcome are better for one treatment. Therefore, we advocate not always treating death as a mechanism through which clinical outcomes are missing, but rather as part of the outcome measure. To account for the survival status, we describe the survival‐incorporated median as an alternative summary measure for outcomes in the presence of death. The survival‐incorporated median is the threshold such that 50% of the population is alive with an outcome above that threshold. Through conceptual examples and an application to a prostate cancer treatment study, we show that the survival‐incorporated median provides a simple and useful summary measure to inform clinical practice. [ABSTRACT FROM AUTHOR]

We present a novel nonparametric approach for estimating average treatment effects (ATEs), addressing a fundamental challenge in causal inference research, both in theory and empirical studies. Our method offers an effective solution to mitigate the instability problem caused by propensity scores close to zero or one, which are commonly encountered in (augmented) inverse probability weighting approaches. Notably, our method is straightforward to implement and does not depend on outcome model specification. We introduce an estimator for ATE and establish its consistency and asymptotic normality through rigorous analysis. To demonstrate the robustness of our method against extreme propensity scores, we conduct an extensive simulation study. Additionally, we apply our proposed methods to estimate the impact of social activity disengagement on cognitive ability using a nationally representative cohort study. Furthermore, we extend our proposed method to estimate the ATE on the treated population. [ABSTRACT FROM AUTHOR]

Background: Time‐varying drug treatments are common in studies using routinely collected health data (RCD) for assessing treatment effects. This study aimed to examine how these studies reported, handled, and interpreted time‐varying drug treatments. Methods: A systematic search was conducted on PubMed from 2018 to 2020. Eligible studies were those used RCD to explore drug treatment effects. We summarized the reporting characteristics and methods employed for handling time‐varying treatments. Logistic regressions were performed to investigate the association between study characteristics and the reporting of time‐varying treatments. Results: Two hundred and fifty‐six studies were included, and 225 (87.9%) studies involved time‐varying treatments. Of these, 24 (10.7%) reported the proportion of time‐varying treatments and 105 (46.7%) reported methods used to handle time‐varying treatments. Multivariable logistic regression showed that medical studies, prespecified protocol, and involvement of methodologists were associated with a higher likelihood of reporting the methods applied to handle time‐varying treatments. Among the 105 studies that reported methods, as‐treated analyses were the most commonly used analysis sets, which were employed in 73.9%, 75.3% and 88.2% of studies that reported approaches for treatment discontinuation, treatment switching and treatment add‐on. Among the 225 studies involved time‐varying treatments, 27 (12.0%) acknowledged the potential bias introduced by treatment change, of which 14 (51.9%) suggested that potential biases may impact acceptance or rejection of the null hypothesis. Conclusions: Among observational studies using RCD, the underreporting about the presence and methods for handling time‐varying treatments was largely common. The potential biases due to time‐varying treatments have frequently been disregarded. Collaborative endeavors are strongly needed to enhance the prevailing practices. [ABSTRACT FROM AUTHOR]

Objective To explore the clinical effect of radioactive iodine 125 seed implantation in the treatment of superior vena cava syndrome (SVCS) caused by lung cancer. Methods A total of 42 patients with SVCS caused by lung cancer in the hospital from 2017 to 2021 were selected retrospectively and divided into the observation group and the control group with 21 cases in each group. Patients in both groups were treated with conventional lung cancer therapy, while patients in the observation group were treated with radioactive iodine 125 seed implantation therapy. The average diameter of tumor, the stenosis degree of superior vena cava and the clinical symptom score were compared between the two groups after three months of treatment. Results After three months of treatment, the average diameter of tumor and the stenosis degree of superior vena cava [ (24.6±10.6) mm and (21.6±24.1) %] in the observation group were smaller than those in the control group [ (49.2±17.3) mm and (59.6±22.6) %],and the clinical symptom score [ (1.29±1.15) score] was lower than that of the control group [ (2.33±0.86) score],and the differences were statistically significant (P＜0.05) .The total incidence of complications in the observation group was 33.3%.Conclusion Radioactive iodine 125 seed implantation in the treatment of SVCS caused by lung cancer can effectively reduce tumor volume, alleviate the stenosis of superior vena cava, and alleviate related clinical symptoms, with definitive efficacy. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the clinical efficacy of a combination treatment of traditional Chinese medicine (TCM) in scalp seborrheic dermatitis (SSD) of differing severity. Methods: Our study included patients with typical SSD who visited the Medical Research Center for Hair and Skin at our hospital. Symptoms were evaluated using a "16‐point scale" developed at the center. Patients who had mild SSD were treated with Pi Fu Kang Xi Ye (PFKXY), those with moderate SSD were treated with PFKXY combined with Run Zao Zhi Yang Jiao Nang (RZZYJN), and those with severe dermatitis were treated with PFKXY and RZZYJN along with garlicin enteric‐coated tablets. Patients were asked to revisit 4 weeks later to evaluate the efficacy. Results: Symptom scores of all patients decreased by (5.48 ± 2.51) after treatment as compared with before treatment, and the results of t‐test and correlation test were significant (p < 0.01). The scores of patients with mild, moderate and severe SSD decreased by 3.14 ± 1.83, 4.90 ± 1.77, and 8.05 ± 2.21, respectively, after treatment as compared with before treatment. Among them, the changes in scores of patients with moderate dermatitis before and after treatment were significant in the t‐test and correlation test (p < 0.01). Conclusion: In this study, the combination treatment of TCM showed significant efficacy in the treatment of mild, moderate, and severe SSD, and the efficacy was stable, especially for patients with moderate SSD. [ABSTRACT FROM AUTHOR]

How was life expectancy in Iran affected by the Islamic Revolution and subsequent war with Iraq? This study examines the joint effect of regime change and the war against Iraq on life expectancy in Iran between 1978 and 1988. If there had been no revolution and war in Iran, how would the life expectancy of Iranians have developed? To answer this question, we use a synthetic control model to construct a counterfactual Iran based on a weighted average of other comparable countries, which reproduces the situation of pre‐revolution Iran but does not experience the revolution and war. We then compare the life expectancies of the counterfactual and actual Iran that underwent a regime change and war with Iraq. Our results indicate that an average Iranian's total life expectancy would have been approximately five years longer without the revolution and war. The revolution had a moderate long‐term impact on total life expectancy at birth, with the most significant influence being attributed to the war itself, particularly on male life expectancy. Our main findings are robust to a series of tests, including placebo tests. We investigate possible reasons that may explain the impact on longevity. [ABSTRACT FROM AUTHOR]

Abstract Background The estimand for a clinical trial is a precise definition of the treatment effect to be estimated. Traditionally, estimates of treatment effects are based on either an ITT analysis or a per-protocol analysis. However, there are important clinical questions which are not addressed by either of these analyses. For example, consider a trial where patients take a rescue medication. The ITT analysis includes data after use of rescue, while the per-protocol analysis excludes these patients altogether. Neither of these analyses addresses the important question of what the treatment effect would have been if patients did not take rescue medication. Main text Trial estimands provide a broader perspective compared to the limitations of ITT and per-protocol analysis. Trial treatment effects depend on how events occurring after treatment initiation such as use of alternative medication or discontinuation of the intervention are included in the definition. These events can be accounted for in different ways, depending on the clinical question of interest. Conclusion The estimand framework is an important step forward in improving the clarity and transparency of clinical trials. The centrality of estimands to clinical trials is currently not reflected in methods recommended by the Cochrane group or the CONSORT statement, the current standard for reporting clinical trials in medical journals. We encourage revisions to these guidelines.

Abstract Background In the causal analysis of observational studies, covariates should be carefully balanced to approximate a randomized experiment. Numerous covariate balancing methods have been proposed for this purpose. However, it is often unclear what type of randomized experiments the balancing approaches aim to approximate; and this may cause ambiguity and hamper the synthesis of balancing characteristics within randomized experiments. Methods Randomized experiments based on rerandomization, known for significant improvement on covariate balance, have recently gained attention in the literature, but no attempt has been made to integrate this scheme into observational studies for improving covariate balance. Motivated by the above concerns, we propose quasi-rerandomization, a novel reweighting method, where observational covariates are rerandomized to be the anchor for reweighting such that the balanced covariates obtained from rerandomization can be reconstructed by the weighted data. Results Through extensive numerical studies, not only does our approach demonstrate similar covariate balance and comparable estimation precision of treatment effect to rerandomization in many situations, but it also exhibits advantages over other balancing techniques in inferring the treatment effect. Conclusion Our quasi-rerandomization method can approximate the rerandomized experiments well in terms of improving the covariate balance and the precision of treatment effect estimation. Furthermore, our approach shows competitive performance compared with other weighting and matching methods. The codes for the numerical studies are available at https://github.com/BobZhangHT/QReR .

Abstract Estimating individual treatment effects (ITE) from observational data have become an important topic in various fields. In healthcare, determining the optimal time and amount of the treatment is crucial for improving each patient’s quality of life. However, existing methods mainly focused on estimating treatment effect under static setting with categorical treatments. In this work, we proposed Recurrent Continuous Adversarial Balancing (RCAB), a method that incorporates adversarial learning into a sequence-to-sequence recurrent network to reduce selection bias in dynamic data. Moreover, we reformulated imbalance loss to be appropriate for continuous scenario. RCAB is empirically validated in both synthetic and real-world datasets. The results demonstrate that RCAB outperforms various state-of-the-art methods, and it is applicable to time-series data with any types of treatments.

Abstract INTRODUCTION Phase 3 trials using the anti‐amyloid antibodies aducanumab, lecanemab, donanemab, and high‐dose gantenerumab in prodromal and mild Alzheimer's disease dementia were heterogeneous in respect to statistical significance of effects. However, heterogeneity of results has not yet directly be quantified. METHODS We used Bayesian random effects meta‐analysis to quantify evidence for or against a treatment effect, and assessed the size of the effect and its heterogeneity. Data were extracted from published studies where available and Web based data reports, assuming a Gaussian data generation process. RESULTS We found moderate evidence in favor of a treatment effect (Bayes factor = 13.2). The effect was moderate to small with −0.33 (95% credible interval −0.54 to −0.10) points on the Clinical Dementia Rating – Sum of Boxes (CDR‐SB) scale. The heterogeneity parameter was low to moderate with 0.21 (0.04 to 0.45) CDR‐SB points. DISCUSSION Heterogeneity across studies was moderate despite some trials reaching statistical significance, while others did not. This suggests that the negative aducanumab and gantenerumab trials are in full agreement with the expected effect sizes.

The authors propose a new Bayesian synthetic control framework to overcome limitations of extant synthetic control methods (SCMs). The proposed Bayesian synthetic control methods (BSCMs) do not impose any restrictive constraints on the parameter space a priori. Moreover, they provide statistical inference in a straightforward manner as well as a natural mechanism to deal with the "large p, small n" and sparsity problems through Markov chain Monte Carlo procedures. Using simulations, the authors find that for a variety of data-generating processes, the proposed BSCMs almost always provide better predictive accuracy and parameter precision than extant SCMs. They demonstrate an application of the proposed BSCMs to a real-world context of a tax imposed on soda sales in Washington state in 2010. As in the simulations, the proposed models outperform extant models, as measured by predictive accuracy in the posttreatment periods. The authors find that the tax led to an increase of 5.7% in retail price and a decrease of 5.5%∼5.8% in sales. They also find that retailers in Washington overshifted the tax to consumers, leading to a pass-through rate of approximately 121%. [ABSTRACT FROM AUTHOR]

To assess the effect of treatment in survival studies, a randomized controlled trial (RCTs) is usually designed. Due to disease progression or other safety reasons, patients in the standard group may be allowed to switch to the experimental treatment. In such cases, estimation of the treatment effect is inevitably performed using naive methods such as intention-to-treat (ITT), per-protocol (PP), or more advanced methods such as the rank-preserving structural failure time (RPSFT) models or iterative parameter estimation (IPE). In some trials, people who progress early might be more likely to switch, and in other trials, people who progress late might be more likely to switch – depending on the characteristics of the disease and the drug being examined. So, this study simulated a new range of realistic scenarios with regard to patients' prognosis and investigated the performance of the adjusted methods in the early and late switching with respect to the median of the survival time. [ABSTRACT FROM AUTHOR]

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional—and even some advanced MRI techniques—are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence. [ABSTRACT FROM AUTHOR]

In clinical studies, it is often that the medical treatments take a period of time before having an effect on patients and the delayed time may vary from person to person. Even though there exists a rich literature developing methods to estimate the time-lag period and treatment effects after lag time, most of these existing studies assume a fixed lag time. In this paper, we propose a hazard model incorporating a random treatment time-lag effect to describe the heterogeneous treatment effect among subjects. The EM algorithm is used to obtain the maximum likelihood estimator. We give the asymptotic properties of the proposed estimator and evaluate its performance via simulation studies. An application of the proposed method to real data is provided. [ABSTRACT FROM AUTHOR]

• Most papers show that the Paris Agreement (PA) has led to decline in global emissions. • However, it is not clear whether this is due to efficiency gains or economic decline. • We adopt a treatment method to study impact of the PA on global emissions intensity. • Our results show a 4.1% decline in global emissions intensity driven by the PA. • We also find that some countries might be too poor to achieve reductions under the PA. Dozens of studies point to evidence of significant reductions in carbon emissions driven by climate agreements, such as the Paris climate agreement. However, these studies fail to answer a pertinent question, that is, are global carbon emission reductions due to reduced production activities or are production processes becoming more efficient as less carbon is emitted per unit output due to technical progress? Such an understanding is important to evaluate the tension between environmental quality and economic growth. Therefore, drawing on difference-in-differences approaches, our results suggest a significant decline in global carbon emission intensity due to the 2015 Paris climate agreement, which is estimated at a 4.1% reduction on average over the sample period. Our evidence also shows that while some countries may be too poor to generate meaningful outcomes from climate agreements, the associated rate of reduction in carbon intensity in others tends to decline above a certain income threshold. These results imply that although there has been a worldwide increase in production efficiency and technical progress, at least owing to the Paris climate agreement, reaching net zero carbon emissions globally requires new approaches, especially for the very poor countries relative the ones at the top of the income ladder. [ABSTRACT FROM AUTHOR]

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework. [ABSTRACT FROM AUTHOR]

Vehicle lane changing is one of the most important topics in autonomous driving and is the behavior most likely to cause vehicle accidents. In order to optimize the performance of vehicle lane change by controlling the speed of the vehicle, which makes the lane change safer, more efficient, and more eco-friendly, a causal inference-based speed control framework is proposed in this study. First, variables affecting the lane change efficiency are selected and their treatment effects are obtained by conducting causal effect analysis and causal refutation analysis. Second, the conditional average treatment effect is estimated by taking the safety and eco-friendly requirements into consideration and applying the double machine learning version causal forest model. Finally, a controller is built, and grid search is used to optimize lane change decision-making. By conducting numerical experiments, the average lane change duration decreased by 5.9% and average velocity increased from 7.165 m=s to 7.791 m=s. The results show that the framework has good performance on improving the efficiency and environmental friendliness of a lane change process while ensuring safety. The proposed framework provides a new approach to speed control for discretionary lane-changing behavior. The approach can be well adapted to a vehicle-to-vehicle communication environment and is expected to be applied to vehicle-assisted driving systems in the future. [ABSTRACT FROM AUTHOR]

Eliminating poverty is a multifaceted global challenge and a focal point of global development governance. With the implementation of targeted poverty alleviation (TPA), China's poverty alleviation efforts have had significant achievements. This study provides a new perspective by classifying poverty‐stricken households based on poverty root causes at the household level, a multi‐propensity score weighting model based on counterfactual inference is employed to examine the poverty reduction effect and policy precision on six non‐equivalent poverty‐stricken household groups in a Chinese county. The results reveal that the poverty reduction effects differed among poverty‐stricken households with different root causes of poverty. Specifically, households impoverished due to disability have the lowest income of the six groups. The assistance policy effects also vary significantly and are found to be poorly suited to poverty‐stricken households a lack of labor force and funds. A robustness test confirms this conclusion and a more nuanced analysis reveals that these differences are reflected in the wage and transfer incomes. Therefore, to ensure the stability of poverty reduction and the sustainability of income for poverty‐stricken households, relevant associated aid policies need to place different emphases based on their household characteristics. [ABSTRACT FROM AUTHOR]

Objective To evaluate the short-term outcomes of thoracic endovascular aortic repair (TEVAR) with outer branched and inner branched endografts in the treatment of Stanford type B aortic dissection (TBAD), and to analyze the effects of endografts on blood flow status of aortic dissection by computational fluid dynamics (CFD). Methods The clinical data of TBAD patients treated with outer branched endograft technology in outer branched endograft group and inner branched endograft technology in inner branched endograft group in Department of Vascular Surgery Tianjin Medical University General Hospital from May 2018 to December 2021 were collected, and the short-term results of two groups were analyzed retrospectively. Based on CT angiography images of patients in two groups, one typical case in each group was selected to construct personalized 3D model and CFD numerical simulation was performed. The parameters including flow field velocity distribution, wall pressure and wall shear stress (WSS) before and after surgery were compared and analyzed. Results A total of 55 cases with TBAD were enrolled, consisting of 49 cased in outer branched endograft group and 6 cases in inner branched endograft group. There was no statistical difference in baseline data between two groups, and surgical success rate were both 100%. Reconstruction of left subclavical artery(LSA) simple was 41(83.7%) cases in outer branched endograft group and all 6 cases in inner branched endograft group. In outer branched endograft group there were 5 cases with reconstruction of left common carotid artery (LCCA) combined with bridging carotic clavical artery, 2 cases with reconstruction of LCCA combined with LSA embolism, and 1 case with reconstruction of LCCA combined with LSA window. Four cases were lost during follow-up in outer branched endograft group. There were no significant differences in the aorta-related mortality rate (P=1.000), branch patency rate (P=1.000) and avoidance of secondary intervention of target vessels between the two groups during the perioperative period and follow-up period (P=0.298). After endograft implantation of two groups the flow field disturbance in dissection lesions improved significantly, aortic blood flow pattern restored normal, and local abnormally increased WSS decreased. However, the interference of inner branch on aortic arch and blood flow of branch was more obvious than that of outer branch. Conclusions Both outer branched endograft and inner branched endograft TEVAR for reconstruction of LSA had good short-term results in the treatment of TBAD. Compared with inner branched endograft, outer branched endograft has a higher anatomical fit and can restore the normal blood flow to a greater extent for aortic arch.