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Introduction: Machine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored. Methods: We used real-world data comparing antihypertensive users to non-users with 69 negative control outcomes, and plasmode simulations to study the performance of ML methods in PS and DRS estimation. We conducted a cohort study using UK primary care records. Further, we conducted a plasmode simulation with synthetic treatment and outcome mimicking empirical data distributions. We compared four PS and DRS estimation methods: 1. Reference: Logistic regression including clinically chosen confounders. 2. Logistic regression with L1 regularisation (LASSO). 3. Multi-layer perceptron (MLP). 4. Extreme Gradient Boosting (XgBoost). Covariate balance, coverage of the null effect of negative control outcomes (real-world data) and bias based on the absolute difference between observed and true effects (for plasmode) were estimated. 632,201 antihypertensive users and nonusers were included. Results: ML methods outperformed the reference method for PS estimation in some scenarios, both in terms of covariate balance and coverage/bias. Specifically, XgBoost achieved the best performance. DRS-based methods performed worse than PS in all tested scenarios. Discussion: We found that ML methods could be reliable alternatives for PS estimation. ML-based DRS methods performed worse than PS ones, likely given the rarity of outcomes. [ABSTRACT FROM AUTHOR]

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their performance. In this paper, we aim to facilitate the validation of prediction models for individualized treatment effects. The estimands of interest are defined based on the potential outcomes framework, which facilitates a comparison of existing and novel measures. In particular, we examine existing measures of discrimination for benefit (variations of the c‐for‐benefit), and propose model‐based extensions to the treatment effect setting for discrimination and calibration metrics that have a strong basis in outcome risk prediction. The main focus is on randomized trial data with binary endpoints and on models that provide individualized treatment effect predictions and potential outcome predictions. We use simulated data to provide insight into the characteristics of the examined discrimination and calibration statistics under consideration, and further illustrate all methods in a trial of acute ischemic stroke treatment. The results show that the proposed model‐based statistics had the best characteristics in terms of bias and accuracy. While resampling methods adjusted for the optimism of performance estimates in the development data, they had a high variance across replications that limited their accuracy. Therefore, individualized treatment effect models are best validated in independent data. To aid implementation, a software implementation of the proposed methods was made available in R. [ABSTRACT FROM AUTHOR]

This study aimed to assess the efficacy of hyperbaric oxygen therapy (HBOT) in patients with central retinal artery occlusion (CRAO) by analyzing changes in visual acuity (VA) and enhanced depth imaging optical coherence tomography (EDI-OCT) parameters. A comparative retrospective study was conducted by reviewing the medical records of all HBO-treated RAO patients in our department and comparing them with matched RAO patients who did not receive HBO treatment. All patients treated with HBO received treatment within 7 days of the onset of visual symptoms. Baseline characteristics were compared, and VA and OCT parameters were evaluated at baseline and follow-up visits. A total of 50 eyes from 50 patients were included, with 29 eyes in the HBOT group and 21 eyes in the control group. The mean BCVA of the HBOT group at the initial visit was 2.03 logMAR, which improved to 1.55 logMAR at 6 months, with the change being statistically significant (P < 0.01), while the control group's BCVA remained almost unchanged, from 2.1 to 2.11 logMAR (P = 0.762). The central choroidal thickness increased significantly in the HBOT group over the subsequent period. The central fovea, and outer retinal layer thickness in the HBOT group were significantly greater than those in the control group at the 6-month follow-up after treatment. HBOT appears to be effective in improving VA and inducing favorable changes in OCT parameters in patients with CRAO. It helps to preserve retinal layer thickness, especially in the outer retinal layer. [ABSTRACT FROM AUTHOR]

Background: This study aims to evaluate the two-year outcomes of polypoidal choroidal vasculopathy (PCV) treated with conbercept and to investigate the predictive response factors. Methods: Consecutive patients with PCV who received three-loading intravitreal conbercept, followed by as-needed reinjections, were studied retrospectively. The best corrected visual acuity (BCVA), central retinal thickness (CRT) and polyps were evaluated. Patients who achieved dry maculae in month 6 were categorised into the dry group, or otherwise, into the non-dry group. The predictive factors for a dry macula were evaluated. Results: A total of 25 eyes from 25 patients (17 males; mean age: 62.8 ± 6.4 years) were included. At month 24, the average BCVA increased significantly from 49.9 ± 15.0 letters to 57.2 ± 16.0 letters (p = 0.040); the average CRT decreased significantly from 430.16 ± 166.55 μm to 278.31 ± 157.34 μm (p = 0.00), and 88% of the eyes achieved dry maculae. The number of polyps changed from 55 to 20 (fading rate: 63.6%; p < 0.001). The mean number of intravitreal injections was 8.6 ± 5.4. The dry group (10 eyes, 40%) was more likely to have higher branching vascular network vessel density (BVN VD; p = 0.021), submacular haemorrhages (p = 0.011) but lack polyp-related serous pigmented epithelial detachment (PED) (p = 0.037). Conclusions: Conbercept was effective in eyes with PCV at maintaining functional and anatomical improvement. Baseline characteristics, including BVN VD, the presence of polyps with serous PED and submacular haemorrhage, seemed to be related to the response to conbercept. [ABSTRACT FROM AUTHOR]

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (BLUP) of Goldberger in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed PUP is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals. [ABSTRACT FROM AUTHOR]

Gonçalves and Ng propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of the errors is correctly specified. In this comment, we first discuss how the assumptions imposed on the errors depend on the model and estimator adopted. This enables researchers to assess the validity of the assumptions imposed on the structure of the errors, and the relevant information set for conditional analyses. We then propose a simple sensitivity analysis in order to quantify the amount of misspecification on the dependence structure of the errors required for the conclusions of conditional analyses to be changed. [ABSTRACT FROM AUTHOR]

Background: Patients with severe infection often require careful fluid management. Intravenous linezolid can increase the fluid volume, whereas oral linezolid can effectively limit the fluid volume. However, the differences in the treatment effect and incidence of thrombocytopenia between oral and intravenous linezolid in patients with severe infection remain unclear. Methods: Patients who received linezolid in the intensive care unit of PLA General Hospital from December 2010 to December 2020 were divided into an oral group and intravenous group according to the route of administration, and we further established the PO group and PO-match group by propensity score matching. The anti-infection effect of linezolid and incidence of thrombocytopenia were compared between the two groups. Results: In total, 177 patients were enrolled in the study (59 in PO group and 118 in PO-match group). The microbial clearance rate and clinical cure rate were similar between the two groups (70.0% vs. 79.3%, P = 0.293; 72.9% vs. 83.9%, P = 0.213). The 30-day mortality rate was slightly higher in the PO group than in the PO-match group, but the difference was not statistically significant (13.6% vs. 6.8%, P = 0.138). There was no difference in the incidence of thrombocytopenia between the two groups (62.7% vs. 65.3%, P = 0.739). Conclusions: There were no significant differences in the treatment effect or incidence of thrombocytopenia between oral and intravenous administration of linezolid in patients with severe infection. [ABSTRACT FROM AUTHOR]

We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the 'sparsest rule', which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs. Furthermore, we propose a surrogate sparsest penalty that aligns with the identification condition and provides oracle sparse structure simultaneously. Desirable theoretical properties are derived for the proposed estimator with weaker IV strength conditions compared to the previous literature. Finite sample properties are demonstrated using simulations and the selection and estimation method is applied to an empirical study concerning the effect of body mass index on diastolic blood pressure. [ABSTRACT FROM AUTHOR]

The Circular Electron–Positron Collider (CEPC) in China can also work as an excellent powerful synchrotron light source, which can generate high‐quality synchrotron radiation. This synchrotron radiation has potential advantages in the medical field as it has a broad spectrum, with energies ranging from visible light to X‐rays used in conventional radiotherapy, up to several megaelectronvolts. FLASH radiotherapy is one of the most advanced radiotherapy modalities. It is a radiotherapy method that uses ultra‐high dose rate irradiation to achieve the treatment dose in an instant; the ultra‐high dose rate used is generally greater than 40 Gy s−1, and this type of radiotherapy can protect normal tissues well. In this paper, the treatment effect of CEPC synchrotron radiation for FLASH radiotherapy was evaluated by simulation. First, a Geant4 simulation was used to build a synchrotron radiation radiotherapy beamline station, and then the dose rate that the CEPC can produce was calculated. A physicochemical model of radiotherapy response kinetics was then established, and a large number of radiotherapy experimental data were comprehensively used to fit and determine the functional relationship between the treatment effect, dose rate and dose. Finally, the macroscopic treatment effect of FLASH radiotherapy was predicted using CEPC synchrotron radiation through the dose rate and the above‐mentioned functional relationship. The results show that the synchrotron radiation beam from the CEPC is one of the best beams for FLASH radiotherapy. [ABSTRACT FROM AUTHOR]

Regression to the mean occurs when an unusual observation is followed by a more typical outcome closer to the population mean. In pre- and post-intervention studies, treatment is administered to subjects with initial measurements located in the tail of a distribution, and a paired sample t -test can be utilized to assess the effectiveness of the intervention. The observed change in the pre-post means is the sum of regression to the mean and treatment effects, and ignoring regression to the mean could lead to erroneous conclusions about the effectiveness of the treatment effect. In this study, formulae for regression to the mean are derived, and maximum likelihood estimation is employed to numerically estimate the regression to the mean effect when the test statistic follows the bivariate t -distribution based on a baseline criterion or a cut-off point. The pre-post degrees of freedom could be equal but also unequal such as when there is missing data. Additionally, we illustrate how regression to the mean is influenced by cut-off points, mixing angles which are related to correlation, and degrees of freedom. A simulation study is conducted to assess the statistical properties of unbiasedness, consistency, and asymptotic normality of the regression to the mean estimator. Moreover, the proposed methods are compared with an existing one assuming bivariate normality. The p -values are compared when regression to the mean is either ignored or accounted for to gauge the statistical significance of the paired t -test. The proposed method is applied to real data concerning schizophrenia patients, and the observed conditional mean difference called the total effect is decomposed into the regression to the mean and treatment effects. [ABSTRACT FROM AUTHOR]

Vacuum preloading is a commonly adopted method for improving soft ground. During the preloading period, the applied vacuum pressure is constant or multistaged, implying that the vacuum pump must work continuously with constant or increasing operating power. To reduce the energy consumption of the vacuum pump, a new loading strategy of cyclic vacuum loading was proposed in this study, whereby the pump is periodically switched on and off, and hence the vacuum pressure ascends and descends accordingly. To guide the design of cyclic vacuum preloading, a large-strain radial consolidation theory incorporating the shifting of the consolidation status of ground soil from normally consolidated to overconsolidated (and vice versa) was established in the present study. Based on the established theory, the treatment effects of cyclic vacuum loading can be quantitatively determined, including the ground settlement and the degree of consolidation, while the energy consumption can be simply determined by multiplying the power by the operating time of the vacuum pump, which works only during the ascending stage of cyclic vacuum pressure. Two cases of the cyclic vacuum preloading strategy were evaluated herein, that is, the complete cyclic vacuum loading and combined cyclic/constant vacuum loading cases. Finally, experimental tests were conducted to demonstrate the energy-saving effect of cyclic vacuum preloading. Compared to the conventional vacuum preloading strategy (i.e., constant vacuum pressure), the energy consumption of the cyclic vacuum loading strategy can be drastically reduced (80% reduction), while the treatment effects remain basically unaffected. [ABSTRACT FROM AUTHOR]

In cancer and other medical studies, time‐to‐event (eg, death) data are common. One major task to analyze time‐to‐event (or survival) data is usually to compare two medical interventions (eg, a treatment and a control) regarding their effect on patients' hazard to have the event in concern. In such cases, we need to compare two hazard curves of the two related patient groups. In practice, a medical treatment often has a time‐lag effect, that is, the treatment effect can only be observed after a time period since the treatment is applied. In such cases, the two hazard curves would be similar in an initial time period, and the traditional testing procedures, such as the log‐rank test, would be ineffective in detecting the treatment effect because the similarity between the two hazard curves in the initial time period would attenuate the difference between the two hazard curves that is reflected in the related testing statistics. In this paper, we suggest a new method for comparing two hazard curves when there is a potential treatment time‐lag effect based on a weighted log‐rank test with a flexible weighting scheme. The new method is shown to be more effective than some representative existing methods in various cases when a treatment time‐lag effect is present. [ABSTRACT FROM AUTHOR]

Background: According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment. Methods: We construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve. Results: The simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period. Conclusions: We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data. [ABSTRACT FROM AUTHOR]

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Determining whether a surrogate marker can be used to replace a primary outcome in a clinical study is complex. While many statistical methods have been developed to formally evaluate a surrogate marker, they generally do not provide a way to examine heterogeneity in the utility of a surrogate marker. Similar to treatment effect heterogeneity, where the effect of a treatment varies based on a patient characteristic, heterogeneity in surrogacy means that the strength or utility of the surrogate marker varies based on a patient characteristic. The few methods that have been recently developed to examine such heterogeneity cannot accommodate censored data. Studies with a censored outcome are typically the studies that could most benefit from a surrogate because the follow‐up time is often long. In this paper, we develop a robust nonparametric approach to assess heterogeneity in the utility of a surrogate marker with respect to a baseline variable in a censored time‐to‐event outcome setting. In addition, we propose and evaluate a testing procedure to formally test for heterogeneity at a single time point or across multiple time points simultaneously. Finite sample performance of our estimation and testing procedure are examined in a simulation study. We use our proposed method to investigate the complex relationship between change in fasting plasma glucose, diabetes, and sex hormones using data from the diabetes prevention program study. [ABSTRACT FROM AUTHOR]

Analyzing treatment or exposure effect is a major research theme in scientific studies. In the current big-data era where multiple sources of data are available, it is of interest to perform a synthesized analysis of treatment effects by integrating information from different data sources or studies. However, studies may contain heterogeneous and incomplete covariate sets, and individual data therein may not be accessible. We apply and extend the generalized meta-analysis method to integrate summary results (e.g., regression coefficients) of outcome and treatment (propensity score, PS) regression analyses across different datasets that may contain heterogeneous covariate sets. The proposed integrated analysis utilizes a reference dataset, which contains data on the complete set of covariates. The asymptotic distribution for the proposed integrated estimator is established. Simulations reveal that the proposed estimator performs well. We apply the proposed method to obtain the causal effect of waist circumference on hypertension by integrating two existing outcomes and PS regression analyses with different sets of covariates. [ABSTRACT FROM AUTHOR]

Background: Many randomized controlled trials (RCTs) and network meta-analyses have demonstrated that the progression-free survival (PFS) and overall survival (OS) of advanced non-small cell lung cancer (NSCLC) patients can be improved through combination immunotherapy or monotherapies. However, time-dependent analysis of the treatment effect is currently lacking. Thus, we aimed to evaluate the efficacy of first-line immunotherapy, and establish a hazard ratio function to reflect the time-varying progression or mortality risk of patients with NSCLC. Methods: Seventeen clinical trials were selected based on search strategy. Baseline characteristics, including the age, sex, smoking status, geographical region, and Eastern Cooperative Oncology Group (ECOG) performance status of patients, were balanced, resulting in ten immunotherapies from nine appropriate clinical trials to conduct treatment effect comparison. Results: We found that nivolumab plus ipilimumab (nivo + ipi) improved the PFS and OS over time. The hazard ratio of nivo + ipi, relative to that of pembrolizumab, decreased from 1.11 to 0.36 for PFS, and from 0.93 to 0.49 for OS over a 10-year period. In terms of the response to immunotherapy in patients with different PD-L1 expression levels, patients with PD-L1 > = 50% experienced lower rates of progression and a reduced mortality risk over time. The hazard ratio of patients with PD-L1 > = 50% relative to all of the patients decreased from 0.73 to 0.69 for PFS, and from 0.78 to 0.67 for OS. Conclusions: Based on the fact that time-dependent progression and mortality risk existed during the treatment duration, physicians should select a suitable treatment regimen for patients based on the hazard ratio. [ABSTRACT FROM AUTHOR]

Time‐to‐event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two‐group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all‐cause mortality. [ABSTRACT FROM AUTHOR]

Objectives: If questionnaires contributing to the diagnosis of dry eye disease are to be recommended as alternatives to existing questionnaires, they must be comparable, with similar repeatability and treatment sensitivity. Comparability was thus examined for three common dry eye questionnaires along with identifying the individual questions that most strongly predicted overall scores. Methods: Anonymised data (n = 329) collected via the Ocular Surface Disease Index (OSDI), 5-item Dry Eye Questionnaire (DEQ-5) and Symptom Assessment in Dry Eye (SANDE) questionnaires (including responses to individual questions) from consenting patients were drawn from real-world dry eye clinics/registries in the United Kingdom, Australia and New Zealand; at follow-up, normalised changes were evaluated in 54 of these patients. Treatment data were also analysed from a 6-month, randomised controlled trial assessing artificial tear supplement treatments with 43 responders and 13 non-responders to treatment identified. The questions extracted from the OSDI which form the abbreviated 6-item OSDI were also analysed. Results: The agreement between the questionnaires ranged from r = 0.577 to 0.754 (all p < 0.001). For the OSDI, three questions accounted for 89.1% of the variability in the total score. The correlation between the OSDI and OSDI-6 was r = 0.939, p < 0.001. For the DEQ-5, two questions accounted for 88.5% of the variance in the total score. Normalised treatment changes were also only moderately correlated between the questionnaires (r = 0.441 to 0.595, p < 0.01). For non-responders, variability was 7.4% with both OSDI and OSDI-6, 9.7% with DEQ-5, 12.1% with SANDE-frequency and 11.9% with SANDE-severity scale. For responders, improvement with drops was detected with a 19.1% change in OSDI, 20.2% in OSDI-6, 20.9% in DEQ-5, and 27.5%/23.6% in SANDE-frequency/severity scales. Conclusions: Existing commonly used dry eye questionnaire scores do not show high levels of correlation. The OSDI was the least variable of the questionnaires and while displaying a slightly lower treatment effect than either the DEQ or SANDE, it was more sensitive to detection of a treatment effect. The quicker-to-complete OSDI-6 exhibited essentially the same outcome as the OSDI, with similar variability and treatment sensitivity. [ABSTRACT FROM AUTHOR]

Background: There is limited knowledge regarding the comparative patient-reported outcomes (PROMs) and effect sizes (ESs) across orthopedic elective surgery. Methods: All patient data between January 2020 and December 2022 were collected, and treatment outcomes assessed as a PROM difference between baseline and one-year follow-up. The cohort was divided into subgroups (hand, elbow, shoulder, spine, hip, knee, and foot/ankle). The PROM ESs were calculated for each patient separately, and patients with ES > 0.5 were considered responders. Results: In total, 7695 patients were operated on. The mean ES across all patient groups was 1.81 (SD 1.41), and the largest ES was observed in shoulder patients and the smallest in hand patients. Overall, shoulder, hip, and knee patients had a larger ES compared to hand, spine, and foot/ankle patients (p < 0.0001). The proportion of positive responders ranged between 91–94% in the knee, shoulder, and hip, and 69–70% in the hand, spine, and foot/ankle subgroups. Conclusions: The ESs are generally high throughout elective orthopedic surgery. However, based on our institutional observations, shoulder, hip, and knee patients experience larger treatment effects compared to hand, spine, and foot/ankle patients, among whom there are also more non-responders. The expected treatment outcomes should be clearly communicated to patients when considering elective surgery. Because of the study limitations, the results should be approached with some caution. [ABSTRACT FROM AUTHOR]

Using plant-level data for 2008-2017, we examine the impact of the adoption of ISO 14000 by Indian manufacturing plants on their energy intensity. The propensity-score-matching difference-in-difference (PSM-DID) estimator is used to assess the effect. Our results show that the adoption of ISO 14000 tends to reduce energy intensity in Indian manufacturing plants, especially among the low and low-medium technology industries. ISO 14000 lowers energy intensity in industrial plants in energy-intensive industries, and the effect is stronger for the group of such industries covered under the Perform–Achieve–Trade (PAT) scheme of the Indian government, a market-based compliance mechanism for energy efficiency improvement. [ABSTRACT FROM AUTHOR]

We investigate the impact of the Circle Line (CL) opening in Singapore on individuals' vehicle kilometers traveled (VKT) using the two repeated cross-sectional travel surveys. In the unmatched difference-in-differences analyses, the CL opening reduces the average VKT by individuals living in the 500-meter buffer zone (treatment) by 38.3% relative to those residing in the 500-1500-meter buffer zone (control) of CL stations. We apply a two-dimensional propensity-score-matching approach to control the spatial heterogeneity of individuals in the treatment and control zones and the temporal heterogeneity of individuals in the pre-CL and after-CL periods due to residential relocation. The CL effect increases to 48.0% with the matched samples. We apply a two-stage regression to test the impact of the CL opening on VKT in the extensive and intensive margins. We find that the CL only affects individuals' driving decisions but does not significantly affect driving distance. The study has significant policy implications for rail transit investment decisions and sustainable urban transport. [ABSTRACT FROM AUTHOR]

In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation. [ABSTRACT FROM AUTHOR]

Estimation of causal effects from observational data has been the main objective in several high-impact scientific domains, while the golden standard for calculating the true causal effect is through the conduction of randomized controlled trials. The abundance of this type of data, which are continuously produced and collected, makes them potentially valuable for estimating causal effects. However, observational data may lead to erroneous treatment effect estimation, since they often suffer from various forms of bias. A recent work has shown that causal effect estimation provided by neural network-based model can be improved by leveraging information from the outcome of neighboring instances in the covariate space. In this work, we propose an information-theoretic methodology for selecting the neighbors to be considered in the estimation of the treatment effect for each sample. The proposed methodology, named Mutual Information-based Neighbor selection for Treatment effect estimation (MINT), selects the optimal number of neighbors as well as the type of distance metric with respect to pre-defined criteria. Then, the average outcome of the neighbors in the treatment and control groups is used as an informative input to the estimator, in addition to the covariates. The presented numerical experiments demonstrate that the adoption of the proposed MINT methodology with the state-of-the-art Dragonnet model is able to develop a reliable and accurate model for treatment effect estimation. [ABSTRACT FROM AUTHOR]

The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands. Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets. [ABSTRACT FROM AUTHOR]

This paper compares water and sanitation services in municipalities that entered into a concession arrangement with a private operator versus those in a comparable control group that continued with a public operator. We explore five variables of interest: average tariff; water coverage; sewage collection; sewage treatment; and water losses. Our empirical strategy improves on existing techniques: after controlling for municipality idiosyncrasies, we adopt a difference-in-differences model with nearest-neighbor matching to evaluate private sector management impacts on these variables. We find a large drop in tariff after the concession switching to a private operator, but tariff increases are greater than public operator during the first four years. There is economical and statistical evidence of increasing sewage collection and treatment over time, but only economical evidence of improvements in water coverage; and no convincing evidence of reduction in water losses. The results suggest huge positive economic impact of switching services from public to private. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new procedure to test conditional independence assumption in studying casual inference for time series data. The conditional independence assumption is transformed to a nonparametric conditional moment test with the help of auxiliary variables which are allowed to affect policy choice but the dependence can be fully captured by potential outcomes and observable controls. When the policy choice is binary, a nonparametric statistic test is developed further for testing the conditional independence assumption conditional on policy propensity score. Under some regular conditions, we show that the proposed test statistics are asymptotically normal under the null hypotheses for time series data. In addition, the performances of the proposed methods are illustrated through Monte Carlo simulations and a real example considered in Angrist and Kuersteiner (2011). [ABSTRACT FROM AUTHOR]

To compare the treatment efficacy of childhood myopia control optical interventions [spectacles, soft contact lenses (SCLs) and orthokeratology (OK) lenses], explore the consistency of treatment efficacies during the treatment period and evaluate the impact of baseline spherical equivalent refraction (SER), axial length (AL) and age on the treatment effect. A literature search of EMBASE, PubMed and Google Scholar databases identified 220 articles published between January 2000 and April 2022, which reported the treatment efficacy by differences in the SER and AL change between intervention and control groups. Thirty‐five articles were included in the analysis. Treatment effect sizes (ESs) were calculated, where more positive and negative directions indicated greater treatment efficacy for SER and AL respectively. For SER, the ESs with peripheral add design spectacles (0.66) and SCLs (0.53) were large but not significantly different between treatment types (p = 0.69). For AL, ESs with peripheral add design spectacles (−0.37), SCLs (−0.55) and OK lenses (−0.93) were large, but OK lenses had a significantly greater effect than peripheral add design spectacles (p ≤ 0.001). ESs were large during the first 12 months of treatment for all interventions [peripheral add design SCLs and OK (F ≥ 5.39, p ≤ 0.01), peripheral add design spectacles (F = 0.47, p = 0.63)] but reduced towards the end of 24–36 months of treatment. Baseline SER had an impact on the treatment effect with peripheral add design spectacles only. Optical interventions are efficacious in controlling childhood myopia progression. However, treatment effects were largest only during the first 12 months of treatment and reduced over time. [ABSTRACT FROM AUTHOR]

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]

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]

Background: Azvudine has clinical benefits and acceptable safety against COVID-19, including in patients with comorbidities, but there is a lack of available data for its use in older adult patients. This study explored the effectiveness and safety of azvudine in older adults with mild or moderate COVID-19. Methods: This retrospective cohort study included patients aged ≥80 diagnosed with COVID-19 at the Central Hospital of Shaoyang between October and November 2022. According to the therapies they received, the eligible patients were divided into the azvudine, nirmatrelvir/ritonavir, and standard-of-care (SOC) groups. The outcomes were the proportion of patients progressing to severe COVID-19, time to nucleic acid negative conversion (NANC), and the 5-, 7-, 10-, and 14-day NANC rates from admission. Results: The study included 55 patients treated with azvudine (n = 14), nirmatrelvir/ritonavir (n = 18), and SOC (n = 23). The median time from symptom onset to NANC of the azvudine, nirmatrelvir/ritonavir, and SOC groups was 14 (range, 6–25), 15 (range, 11–24), and 19 (range, 18–23) days, respectively. The median time from treatment initiation to NANC of the azvudine and nirmatrelvir/ritonavir groups was 8 (range, 4–20) and 9 (range, 5–16) days, respectively. The median length of hospital stay in the three groups was 10.5 (range, 5–23), 13.5 (range, 10–21), and 17 (range, 10–23) days, respectively. No treatment-related adverse events or serious adverse events were reported. Conclusion: Azvudine showed satisfactory effectiveness and acceptable safety in older adults with mild or moderate COVID-19. Therefore, azvudine could be a treatment option for this special patient population. [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]

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]

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]

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]

Background: Azvudine has clinical benefits and acceptable safety against COVID-19, including in patients with comorbidities, but there is a lack of available data for its use in older adult patients. This study explored the effectiveness and safety of azvudine in older adults with mild or moderate COVID-19. Methods: This retrospective cohort study included patients aged ≥80 diagnosed with COVID-19 at the Central Hospital of Shaoyang between October and November 2022. According to the therapies they received, the eligible patients were divided into the azvudine, nirmatrelvir/ritonavir, and standard-of-care (SOC) groups. The outcomes were the proportion of patients progressing to severe COVID-19, time to nucleic acid negative conversion (NANC), and the 5-, 7-, 10-, and 14-day NANC rates from admission. Results: The study included 55 patients treated with azvudine (n = 14), nirmatrelvir/ritonavir (n = 18), and SOC (n = 23). The median time from symptom onset to NANC of the azvudine, nirmatrelvir/ritonavir, and SOC groups was 14 (range, 6–25), 15 (range, 11–24), and 19 (range, 18–23) days, respectively. The median time from treatment initiation to NANC of the azvudine and nirmatrelvir/ritonavir groups was 8 (range, 4–20) and 9 (range, 5–16) days, respectively. The median length of hospital stay in the three groups was 10.5 (range, 5–23), 13.5 (range, 10–21), and 17 (range, 10–23) days, respectively. No treatment-related adverse events or serious adverse events were reported. Conclusion: Azvudine showed satisfactory effectiveness and acceptable safety in older adults with mild or moderate COVID-19. Therefore, azvudine could be a treatment option for this special patient population. [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]

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]

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]

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]

Copyright of Journal of Kunming Medical University / Kunming Yike Daxue Xuebao is the property of Kunming Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)