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AbstractThis paper provides a self-contained introduction to the methods of classical stratification and stratification on the propensity score for students of linear regression and more generally for applied statisticians who have not yet made up their minds about the relative worth of these methods. By considering only discrete regressors and by taking into account what is known about the distribution of the data, it is possible to make simple statements about consistency, unbiasedness and efficiency and to provide some perspective on the relative merits of the three methods. [ABSTRACT FROM AUTHOR]

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]

BACKGROUND: In the contemporary healthcare environment, optimizing patient care strategies is of importance, particularly in neurosurgical environments. While traditional nursing methods have the foundation of patient care, there exists a growing recognition of the potential benefits of comprehensive nurse management. Despite this acknowledgment, there remains a gap in understanding the comparative effectiveness of comprehensive nurse management versus traditional approaches in reducing postoperative psychological stress, enhancing patient satisfaction, and promoting adherence in neurosurgical patients. OBJECTIVE: This study compares the efficacy of comprehensive nurse management against traditional methods in facilitating the postoperative recovery of neurosurgical patients. METHODS: Taking the traditional nursing management and detailed nursing management of neurosurgical patients in a municipal neurosurgical hospital from March 2021 to March 2022 as an example, the neurosurgery was divided into 50 patients in the detailed nursing management group and 50 patients in the traditional nursing management group. In the clear nursing management group, there were 50 patients, 20 male patients, with an average age of 58.7 ± 3.8 years, and 30 female patients, with an average age of 60.4 ± 4.3 years; In the traditional nursing management group, there were 50 patients, 26 male patients, with an average age of 59.7 ± 3.7 years, and 24 female patients, with an average age of 59.4 ± 3.9 years; Among the 100 patients, there were 60 cases of cerebral infarction, 25 cases of intracerebral hemorrhage and 15 cases of other neurosurgical diseases. RESULTS: Hundred neurosurgical patients were divided into two groups: comprehensive nursing management group and traditional nursing management group. The results showed that the comprehensive treatment effect of patients with detailed nursing management was better than that of the routine nursing group. At the same time, the complications were reduced, and the patient's satisfaction was higher. CONCLUSION: Through retrospective analysis and investigation of patients, this paper discusses the clinical application of a detailed nursing management model in neurosurgery, which can reduce the probability of infection and other complications and improve the quality of life and treatment effect of patients. [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]

Objective: To evaluate the clinical efficacy of Langenlianqiao (LGLQ) oral liquid treatment and provide a reference basis for the clincal treatment of coronavirus disease 2019 (COVID-19). Design: An experimental clinical study was conducted on three groups with confirmed diagnoses of COVID-19. Site: This study was conducted at Changde Hospital. Participants: A total of 253 patients were enrolled in this study. Methods: The patients were divided into the LGLQ treatment group (100 cases), the Lianhuaqingwen (LHQW) treatment group (100 cases) and the placebo control group (53 cases), according to the treatment each group received. The occurrence of major clinical symptoms, the duration of symptom disappearance, the number of days in hospitalisation and the duration of infection were compared among the three groups. Results: Compared with the placebo control group (10.0 [1.2] d, 9.4 [1.3] d), the duration of infection and hospitalisation effectively decreased in the LGLQ group (6.8 [0.6] d, 7.4 [0.8] d) and the LHQW group (6.8 [1.0] d, 7.3 [1.0] d). Furthermore, the incidence of fatigue in the LGLQ group (4.0%) was lower compared to the LHQW group (14.0%) and the placebo control group (15.1%), but this difference was not statistically significant (P = 0.580 for LGLQ vs. LHQW, P = 0.246 for LGLQ vs. placebo). In the treatment of cough, the LGLQ group showed a significantly different effect compared to both the LHQW group (P = 0.014) and the placebo group (P = 0.016). Additionally, for dry cough specifically, LHQW was effective in reducing its incidence compared to the placebo control group (P < 0.05), while LGLQ showed no statistically significant difference from either LHQW (P = 0.39) or the placebo group (P = 0.14). However, neither the LGLQ group nor the LHQW group showed a reduction in the duration of symptom disappearance in patients with pre-existing symptoms (P > 0.05). Conclusions: Compared with the placebo control group, the LGLQ group showed an improvement in the clinical symptoms of COVID-19 and a decrease in the duration of hospitalisation and infection, which confirmed that the LGLQ treatment had the same antiviral effect as the LHQW treatment. This may provide in-depth insights for antiviral therapy research. [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]

Abstract 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

Abstract 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

Abstract Objective To evaluate the clinical efficacy of Langenlianqiao (LGLQ) oral liquid treatment and provide a reference basis for the clincal treatment of coronavirus disease 2019 (COVID-19). Design An experimental clinical study was conducted on three groups with confirmed diagnoses of COVID-19. Site This study was conducted at Changde Hospital. Participants A total of 253 patients were enrolled in this study. Methods The patients were divided into the LGLQ treatment group (100 cases), the Lianhuaqingwen (LHQW) treatment group (100 cases) and the placebo control group (53 cases), according to the treatment each group received. The occurrence of major clinical symptoms, the duration of symptom disappearance, the number of days in hospitalisation and the duration of infection were compared among the three groups. Results Compared with the placebo control group (10.0 [1.2] d, 9.4 [1.3] d), the duration of infection and hospitalisation effectively decreased in the LGLQ group (6.8 [0.6] d, 7.4 [0.8] d) and the LHQW group (6.8 [1.0] d, 7.3 [1.0] d). Furthermore, the incidence of fatigue in the LGLQ group (4.0%) was lower compared to the LHQW group (14.0%) and the placebo control group (15.1%), but this difference was not statistically significant (P = 0.580 for LGLQ vs. LHQW, P = 0.246 for LGLQ vs. placebo). In the treatment of cough, the LGLQ group showed a significantly different effect compared to both the LHQW group (P = 0.014) and the placebo group (P = 0.016). Additionally, for dry cough specifically, LHQW was effective in reducing its incidence compared to the placebo control group (P 0.05). Conclusions Compared with the placebo control group, the LGLQ group showed an improvement in the clinical symptoms of COVID-19 and a decrease in the duration of hospitalisation and infection, which confirmed that the LGLQ treatment had the same antiviral effect as the LHQW treatment. This may provide in-depth insights for antiviral therapy research.

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 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.

Objective Elastic strip is a critical component of rail vibration reduction fasteners, its mechanical performance and fatigue-load bearing capacity are crucial for urban rail transit train operational safety and track service life. Given the frequent occurrence of elastic strip failures, it is necessary to analyze the fracture causes and develop reasonable treatment measures. Method Based on the concentrated fracture of elastic strips in an urban rail transit line section, the fracture type of elastic strips is determined as fatigue fracture through analysis of the breakage. Since chemical composition and physical property analysis of elastic strips fails to reveal any abnormal conditions, the natural frequency of the elastic strips is obtained through simulation and experimental analysis. The vibration characteristics of the track and elastic strips are determined through track dynamic testing in elastic strip fractured section. Based on this, rail corrugation is identified as the main cause of repeated elastic strip fractures. Reasonable treatment measures are formulated accordingly, and the number of elastic strip fractures before and after treatment is statistically analyzed. Result & Conclusion Rail grinding measures can effectively suppress rail corrugation, significantly reducing elastic strip fracture occurrence, further confirming rail corrugation as the main cause of elastic strip fractures.

Abstract 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.

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]

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]

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]

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]

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]

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]

• New disease-modifying therapies benefit type II SMA patients. • Shift analysis reveals gains in activities across age groups. • Notable gains in activities, especially in children aged 2.5 to 13 years. • Treated patients show lower loss risk, higher gain likelihood. • Effectiveness linked to strength and function improvements. Several studies have shown the efficacy of new disease-modifying therapies in slowing down type II SMA progression using the Hammersmith Functional Motor Scale Expanded (HFMSE). This research aims to enhance understanding of activity changes across age groups post-nusinersen treatment using shift analysis, compared with untreated individuals. Retrospective data from the, international SMA consortium (iSMAc) dataset were analyzed, assessing individual item changes over 12 months. Shift analysis was used to determine the gain or loss of abilities, defining "gain" as a positive change between scores from 0 to either 1 or 2 and "loss" as a negative change from either 2 or 1 to 0. The cohort included 130 SMA II patients who underwent 12-month assessments from their first nusinersen dose, with age range between 0.6 and 49.6 years. One-third of the entire cohort experienced at least a loss in one activity, while 60% experienced a gain, particularly notable in children aged 2.5 to 5 years and 5 to 13 years. Overall, the study demonstrates a positive impact of nusinersen treatment on SMA II patients, showing a trend of increased activity gains and decreased probability of ability loss across different age groups. [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]

AbstractTo 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.

IntroductionMachine 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.MethodsWe 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.ResultsML 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.DiscussionWe 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 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.

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

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.

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]

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]

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]

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]

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]

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]

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]

Abstract.Experimenters have limited flexibility in their choice of cross-over designs, in terms of, number of treatments, subjects, and repeated measurements. Also, cross-over design balanced with higher-order carryover effects presented in the literature requires a large number of experimental subjects, which is impractical for its implementation. Most experimental studies demand a smaller number of subjects, which is possible only in a minimally balanced cross-over design. In this article, two simple methods have been presented, which construct minimal balanced cross-over designs having less, equal, and more periods than the number of treatments. The first method constructs cross-over designs with more periods, while the second method constructs cross-over designs with less or equal periods. As a result, four new families of cross-over designs have been obtained, in which one family of cross-over designs has periods greater than the number of treatments, two families of cross-over designs have three periods, and one family of cross-over designs has periods equal to the number of treatments. All the designs possess good efficiency of separability, and therefore they are suitable for the estimation of treatments and first-second carryover effects of treatments at lower cost of experiments due to a smaller number of experimental subjects as compared to the designs available in the literature. [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]

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]

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]

In a randomised controlled trial (RCT), the between-arm difference in the average probability of an event per unit of time (i.e., yearly incidence risk difference, YIRD) is an easy-to-interpret treatment effect metric. We aimed to quantify the YIRD in cardiorenal RCTs of GLP-1RAs or SGLT-2is. We digitally searched for RCTs published up to March 1st, 2023, including subjects with type 2 diabetes randomised to GLP-1RAs or SGLT-2is and investigating cardiorenal outcomes or death. We extracted information from Kaplan-Meier (KM) plots to obtain time-to-event individual data and estimate within-arm yearly incidence risk and YIRD. Data from 19 RCTs (28 kM plots) were analysed: comparing treatment to placebo, in GLP-1RA RCTs the YIRD ranged from 0.2 % (95 % CI: −0.7 %, 1.1 %) to −1.9 % (−3.1, −0.7), for primary outcome; and from −0.2 % (−0.5, 0.2) to −0.4 % (−0.7 %, −0.0 %), for mortality. With the exception of SOLOIST-WHF (YIRD 11.9 % for primary outcome), corresponding estimates in SGLT-2is RCTs were: from −0.1 % (−0.4, 0.1) to −5.0 % (−7.7, −2.6), for primary outcome; and from −0.1 % (−0.2, 0.1) to −1.9 % (−4.4 %, 0.6 %), for mortality. The YIRD metric complements other relative treatment effect estimates and helps quantify the absolute benefit of GLP-1RAs and SGLT-2is. • Yearly incidence risk difference (YIRD) can help interpreting treatment effect. • Using reconstructed time-to-event data, we estimated YIRD in cardiorenal trials. • YIRDs was estimated for primary outcomes and death in GLP1-RAs and SGLT2-is trials. • YIRD is easy-to-interpret and could add insights in clinical decision-making. [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]

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]