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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.

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.

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.

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

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

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.

Abstract 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.

IntroductionIn 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.MethodsThe 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.ResultsWe 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.DiscussionTherefore, it is essential to thoroughly consider the characteristics of the data and select appropriate methods to ensure reliable conclusions in practical data analysis.

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

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

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

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

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

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

Introduction: SCLC is an aggressive malignancy with poor outcome. Most patients have disease recurrence despite treatments with multiple modalities. Subtyping of SCLC has been proposed recently, and novel agents targeting specific subtypes are actively being investigated. In this study, we evaluated the plasticity of subtypes in paired pre- and post-treatment samples. The aim was to understand possible subtype evolution after chemotherapy resistance that could lead to alternate targeted therapy strategies. Methods: A total of 68 samples from 32 patients with sufficient paired specimens were identified from 1998 to 2022. ASCL1, NEUROD1, and POU2F3 immunohistochemistry studies were performed on all cases, and subtyping by predominant expression was determined. Subtype comparison in each patient was performed, and expression analysis was performed on the basis of subtypes. Results: Of 32 cases, 28 (88%) had the same subtype in pre- and first post-treatment specimens. Protein expression level of subtype-specific transcription factor remained stable after chemotherapy. Two of five (40%) NEUROD1-predominant SCLC switched to ASCL1-predominant phenotype after treatment. One case had a pitfall of scoring ASCL1 on specimen with marked crushing artifacts. One case revealed the challenge of proper subtyping for samples with borderline POU2F3 expression. Conclusions: Subtype of SCLC generally remains the same after acquiring chemotherapy resistance. Plasticity was observed with rare cases switching from NEUROD1-predominant to ASC1-predominant SCLC. Resubtyping is unnecessary for the consideration of novel subtype-specific targeted agents, except cases with NEUROD1-predominant subtype.

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

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.

The purpose of this paper was to introduce the five limitations of the PROC CAUSALGRAPH procedure and estimate the causal effect of the data by using the adjustment set based on the causal graph model. The five limitations were as follows： ①the PROC CAUSALGRAPH procedure could not deal with the causal graph model of directed circles； ② the PROC CAUSALGRAPH procedure could not evaluate dynamic processing scheme； ③ causal effect identification was a population concept； ④ causal effect identification was a nonparametric concept； ⑤ the PROC CAUSALGRAPH procedure could not identify the causal effect in some causal graph models. The example was for a simulated data set， using the conventional multiple Logistic regression model analysis and the causal graph model analysis， respectively. By comparing the analysis results of the two， the following conclusions were drawn： ① causal graph theory was useful in identifying causal effects in confounding situations； ② by implementing hierarchical estimation of causal effects， a good statistical estimation of causal effects could be achieved based on the identification results of the PROC CAUSALGRAPH procedure.

Abstract Background The classical linear model is widely used in the analysis of clinical trials with continuous outcomes. However, required model assumptions are frequently not met, resulting in estimates of treatment effect that can be inefficient and biased. In addition, traditional models assess treatment effect only on the mean response, and not on other aspects of the response, such as the variance. Distributional regression modelling overcomes these limitations. The purpose of this paper is to demonstrate its usefulness for the analysis of clinical trials, and superior performance to that of traditional models. Methods Distributional regression models are demonstrated, and contrasted with normal linear models, on data from the LIPID randomized controlled trial, which compared the effects of pravastatin with placebo in patients with coronary heart disease. Systolic blood pressure (SBP) and the biomarker midregional pro-adrenomedullin (MR-proADM) were analysed. Treatment effect was estimated in models that used response distributions more appropriate than the normal (Box-Cox-t and Johnson’s S u for MR-proADM and SBP, respectively), applied censoring below the detection limit of MR-proADM, estimated treatment effect on distributional parameters other than the mean, and included random effects for longitudinal observations. A simulation study was conducted to compare the performance of distributional regression models with normal linear regression, under conditions mimicking the LIPID study. The R package gamlss (Generalized Additive Models for Location, Scale and Shape), which implements maximum likelihood estimation for distributional regression modelling, was used throughout. Results In all cases the distributional regression models fit the data well, in contrast to poor fits obtained for traditional models; for MR-proADM a small but significant treatment effect on the mean was detected by the distributional regression model and not the normal model; and for SBP a beneficial treatment effect on the variance was demonstrated. In the simulation study distributional models strongly outperformed normal models when the response variable was non-normal and heterogeneous; and there was no disadvantage introduced by the use of distributional regression modelling when the response satisfied the normal linear model assumptions. Conclusions Distributional regression models are a rich framework, largely untapped in the clinical trials world. We have demonstrated a sample of the capabilities of these models for the analysis of trials. If interest lies in accurate estimation of treatment effect on the mean, or other distributional features such as variance, the use of distributional regression modelling will yield superior estimates to traditional normal models, and is strongly recommended. Trial registration The LIPID trial was retrospectively registered on ANZCTR on 27/04/2016, registration number ACTRN12616000535471 .

In functional magnetic imaging (fMRI) in Parkinson’s disease (PD), a paradigm consisting of blocks of finger tapping and rest along with a corresponding general linear model (GLM) is often used to assess motor activity. However, this method has three limitations: (i) Due to the strong magnetic field and the confined environment of the cylindrical bore, it is troublesome to accurately monitor motor output and, therefore, variability in the performed movement is typically ignored. (ii) Given the loss of dopaminergic neurons and ongoing compensatory brain mechanisms, motor control is abnormal in PD. Therefore, modeling of patients’ tapping with a constant amplitude (using a boxcar function) and the expected Parkinsonian motor output are prone to mismatch. (iii) The motor loop involves structures with distinct hemodynamic responses, for which only one type of modeling (e.g., modeling the whole block of finger tapping) may not suffice to capture these structure’s temporal activation. The first two limitations call for considering results from online recordings of the real motor output that may lead to significant sensitivity improvements. This was shown in previous work using a non-magnetic glove to capture details of the patients' finger movements in a so-called kinematic approach. For the third limitation, modeling motion initiation instead of the whole tapping block has been suggested to account for different temporal activation signatures of the motor loop’s structures. In the present study we propose improvements to the GLM as a tool to study motor disorders. For this, we test the robustness of the kinematic approach in an expanded cohort (n = 31), apply more conservative statistics than in previous work, and evaluate the benefits of an event-related model function. Our findings suggest that the integration of the kinematic approach offers a general improvement in detecting activations in subcortical structures, such as the basal ganglia. Additionally, modeling motion initiation using an event-related design yielded superior performance in capturing medication-related effects in the putamen. Our results may guide adaptations in analysis strategies for functional motor studies related to PD and also in more general applications.

BackgroundCOVID-19 is associated with various symptoms and psychological involvement in the long term. In view of the multifactorial triggering and maintenance of the post-COVID syndrome, a multimodal therapy with somatomedical and psychotherapeutic content is expedient. This paper compares the psychological stress of post-COVID patients and their course in rehabilitation to psychosomatic and psychocardiological patients.MethodObservational study with control-groups and clinical, standardized examination: psychological testing (BDI-II, HELATH-49), 6-MWT as somatic parameter, two measurement points (admission, discharge). Sample characteristics, including work related parameters, the general symptom-load and the course of symptoms during rehabilitation are evaluated.ResultsAt admission in all measures post-COVID patients were significantly affected, but less pronounced than psychosomatic or psychocardiological patients (BDI-II post-COVID = 19.29 ± 9.03, BDI-II psychosomatic = 28.93 ± 12.66, BDI-II psychocardiology = 24.47 ± 10.02). During rehabilitation, in all complaint domains and sub-groups, symptom severity was significantly reduced (effect sizes ranging from d = .34 to d = 1.22). Medium positive effects were seen on self-efficacy (d = .69) and large effects on activity and participation (d = 1.06) in post-COVID patients. In the 6-MWT, the walking distance improved by an average of 76.43 ± 63.58 meters (d = 1.22). Not a single patient deteriorated in walking distance, which would have been a possible sign of post exercise malaise (PEM).ConclusionPost-COVID patients have a slighter psychological burden as psychocardiological or psychosomatic patients. Although rehabilitation is not curative, post-COVID patients benefit significantly from the interventions and there were no signs of PEM.

AimTo evaluate the treatment effect Fand pharmacoeconomic value of Dugaglutide in women with type 2 diabetes.MethodsWomen (n=96) with type 2 diabetes recruited from June 2019 to December 2021 were randomized into two equal groups. The control group was treated with Liraglutide, and the observation group was treated with Dulaglutide, both for 24 weeks. The blood glucose levels, biochemical index, insulin resistance index (HOMA-IR), cost-effect ratio (CER), and drug safety were determined and compared between the two groups.ResultsBlood glucose levels, the biochemical index, and HOMA-IR were lower in both groups after the treatment (P < 0.05), and there was no statistical difference in the blood glucose levels, biochemical index and HOMA-IR between the two groups (P > 0.05). The CER levels did not differ statistically between the two groups (P > 0.05). Both the cost and the incidence of drug side effects during solution injection were lower in the observation group than in the control group after 24 weeks of treatment (P < 0.05).ConclusionBoth Dulaglutide and Liraglutide can reduce blood glucose levels, improve biochemical index, and HOMA-IR levels in women with type 2 diabetes. Dulaglutide is more cost-effective and safe.Clinical trial registrationhttps://www.chictr.org.cn/index.aspx, identifier ChiCTR1900026514.

ObjectiveTo analyze and summarize the clinical characteristics and treatment effects for subungual exostosis in children.MethodsClinical data for children with subungual exostosis treated in our department from January 2008 to September 2022 were evaluated.ResultsForty children with subungual exostosis were evaluated, comprising 31 boys (77.5%) and 9 girls (22.5%) with a median age of 9 years (4–17 years). The median disease course was 6 months (1–48 months). Seven patients (17.5%) had definite trauma history and 5 (12.5%) had infection. The toe or finger nail appearance was abnormal in 36 patients and normal in 4 patients. Twenty-seven patients (67.5%) had pain when wearing shoes and walking, and 25 (62.5%) had toenail tenderness. The lesions were located in the distal phalanxes of the toes in 37 patients (92.5%), with 14 patients affected on the left side and 23 on the right side. Twenty-two patients had lesions in the great toe, 6 in the second toe, 6 in the third toe, and 3 in the fourth toe. The lesions in the other 3 patients (7.5%) were located in the distal phalanxes of the fingers, with 2 patients affected in the second finger and 1 in the third finger. Regarding the relationship between lesion location and nail bed, 4 patients were type I, 21 were type II, and 15 were type III. All 40 patients received surgical treatment, with nail removal in 15. The median maximum lesion diameter was 1.0 cm (0.8–2 cm), median operation time was 25 min (20–45 min), median blood loss was 1 ml (1–2 ml), and median postoperative hospital stay was 2 days (1–4 days). All cases were histopathologically confirmed as subungual exostosis. The median follow-up time was 24 months (3–60 months), with normal appearance of the toe or finger nail. There were no complications in 38 patients (95.0%). Two patients (5.0%) relapsed at 3 months postoperatively and underwent a secondary operation, with no subsequent recurrence during 12 months of follow-up.ConclusionSubungual exostosis in children is a rare benign disease that often occurs in the toes. Selection of the appropriate incision and nail bed treatment based on the relationship between lesion location and nail bed is helpful for improving the treatment effect.

We propose a simple-to-implement dynamic panel data method to evaluate the impacts of place-based policies. The idea is to exploit both the cross sectional dependence and the serial correlation within a panel and implement a semi difference-in-difference decomposition. Different from the method by Hsiao et al. (2012), the proposed model is easy to implement statistical inferences and can incorporate multiple treated groups at different time thresholds. We use the proposed method to explore the effect of the Free Trade Zone (FTZ) policy implemented in Shanghai at the end of 2013 on local GDP growth rate and compare it with that from Hsiao et al. (2012). The empirical results show that the FTZ does have a positive effect on the local GDP growth rate and it adds 1.2−1.8 percent point to the growth rate of the GDP per capita for Shanghai in 2014 which is about one fourth to one third of the total GDP growth rate per capita. This effect largely comes from the growth of the tertiary sector. The results lend support to the duplication of FTZ policy to other cities and provinces in China.

This paper proposes a new quantile regression model to characterize the heterogeneity for distributional effects of maternal smoking during pregnancy on infant birth weight across different the mother's age. By imposing a parametric restriction on the quantile functions of the potential outcome distributions conditional on the mother's age, we estimate the quantile treatment effects of maternal smoking during pregnancy on her baby's birth weight across different age groups of mothers. The results show strongly that the quantile effects of maternal smoking on low infant birth weight are negative and substantially heterogenous across different ages.

Examining the welfare impact of agricultural development interventions that incorporate diffusion of improved production technologies to farmers within extension delivery programs can be very challenging, because of the difficulty in disentangling the individual impacts of the production technology and the extension delivery program. Using recent farm level survey data from extension dissemination program of legume inoculant technology of 600 farmers in Ghana, we employ a recent methodological approach to investigate, simultaneously, the impact of the inoculant technology adoption and the extension program participation on farmers' productivity, efficiency and welfare. We decompose each of these impact measures into subcomponents whose causal paths can be traced to both the adoption of the production technology and the extension delivery program. We find that, in terms of yields and net revenue, direct contribution of improved technology adoption alone is 34 and 64%, respectively, and 35 and 66% indirectly due to improved farmer efficiency, leading to 36 and 74% improvement in farmers' welfare, respectively. On the other hand, direct contribution of extension delivery program participation alone is 66 and 36%, respectively, with 66 and 34% indirectly due to improved farmer efficiency, resulting in 64 and 26% improvement in farmers' welfare, respectively. Based on the findings, we recommend that policymakers should invest in research and development to produce yield enhancing agricultural technologies suitable for poor and degraded soil conditions in developing countries which can contribute immensely to poverty and food insecurity reduction. The development of new agricultural technologies must be pursued with vigorous provision of extension services to farmers to be able to exploit the full potentials of the new technologies.

Recent studies have expanded the focus of machine learning methods like random forests beyond prediction. They have found utility in the area of causal inference by using it to estimate propensity scores. It has also been established in the literature that tuning the hyperparameter values of random forests can improve the estimates of causal treatment effects. We thus address the issue of getting the best out of random forest models by proposing to tune the random forest hyperparameters while maximizing covariate balance. We consider variants of tuning based on a model fit criterion and compare with tuning to chase covariate balance. In a simulation study and empirical application in two case studies, we studied the performance of different tuning implementations, relative to the random forest with default hyperparameters. We find that tuning to chase balance rather than model fit when estimating propensity scores induced better balance in the covariates and produced more accurate treatment effect estimates.

Abstract Foreign direct investment (FDI) facilitates modernization of domestic agri-food systems in emerging economies through increased use of vertical coordination. This paper sheds lights on how international brewer investments in African food systems affect smallholder market participation and value chain development. In particular, we analyze the impact of contracts among malt barley producers in Ethiopia. Using cross-sectional survey data, we employ inverse probability-weighted regression adjustment (IPWRA) and propensity score matching (PSM) techniques to analyze the economic impact of contracting. We find that contrary to popular belief, contracting has positive and significant impact on malt barley production, intensification, commercialization, quality improvement, and farm gate prices, ultimately resulting in increased net income and spillover into the productivity of other food crops.

Abstract Background Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. Methods We discuss six methods commonly used in literature: one way analysis of variance (“ANOVA” ), analysis of covariance main effect and interaction models on the post-treatment score (“ANCOVA I” and “ANCOVA II”), ANOVA on the change score between the baseline and post-treatment scores (“ANOVA-Change”), repeated measures (“RM”) and constrained repeated measures (“cRM”) models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. Results ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVA I in the homogeneous scenario and to ANCOVA II in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. Conclusions ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs.

Abstract Background This retrospective study evaluates patient-reported outcomes in patients with multiple sclerosis (MS) spasticity who were treated with a cannabinoid oromucosal spray (Sativex®, USAN name: nabiximols) after not sufficiently responding to previous anti-spasticity medications. Methods Of 276 patients from eight centers in Belgium who began treatment prior to 31 December 2017, effectiveness assessment data were available for 238 patients during the test period of 4 to 8/12 weeks, and for smaller patient cohorts with continued treatment for 6/12 months. Results Mean 0–10 spasticity Numerical Rating Scale (NRS) scores improved from 8.1 at baseline to 5.2 (week 4), 4.6 (week 8) and 4.1 (week 12). Mean EuroQoL Visual Analogue Scale (EQ VAS) scores increased from 39 at baseline to 52 (week 4), 57 (week 8) and 59 (week 12). Mean NRS and EQ VAS scores remained in the same 12 weeks’ range in patients with longer-term data. The average dose of cannabinoid oromucosal spray was 6 sprays/day. Most of the 93 out of 276 patients, with initial prescription (33.7%), who discontinued treatment by week 12 did so within the first 8 weeks, mainly due to lack of effectiveness. By week 12, 171 (74%) of the 230 effectiveness evaluable patients reported a clinically meaningful response, corresponding to ≥30% NRS improvement. The tolerability of cannabinoid oromucosal spray was consistent with its known safety profile. Conclusions More than 60% of the patients with MS who started add-on treatment with cannabinoid oromucosal spray reported a clinically relevant symptomatic effect and continued treatment after 12 weeks.

Plain English summary To help people with eating disorders (EDs) recover it is important to know what makes therapies effective or not. Therefore, we summarized the effects of two common therapies for eating disorders, cognitive behavior therapy (CBT) and psychodynamic-interpersonal therapy (PIT) and examined how ED diagnosis, comorbid personality disorder and changes in psychopathology could influence ED remission. We found that CBT was most consistently effective, with about 1/3 of patients in remission for anorexia, bulimia, and mixed samples, and 50% for patients with binge eating disorder. The effects of PIT were uncertain due to a lack of studies and could be only marginally effective. In CBT, changing the patients’ eating disordered thoughts or their depression or anxiety was not associated with ED remission. We discuss why this may be and suggest that CBT may be more effective because it manages to engage a subgroup of patients who are motivated to change and less depressed or anxious. Thus, patients with more severe symptoms may benefit less. We conclude that CBT may be necessary to help people recover from eating disorders, but that some patients may still require interventions aimed at strengthening self-functions to substitute eating disordered behavior in the long-term.

BackgroundAmide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients, but the initial results from recent studies are different.AimThe aim of this study is to systematically review and quantify the diagnostic performance of APT in assessing treatment response in patients with post-treatment gliomas.MethodsA systematic search in PubMed, EMBASE, and the Web of Science was performed to retrieve related original studies. For the single and added value of APT imaging in distinguishing TR from TEs, we calculated pooled sensitivity and specificity by using Bayesian bivariate meta-analyses.ResultsSix studies were included, five of which reported on single APT imaging parameters and four of which reported on multiparametric MRI combined with APT imaging parameters. For single APT imaging parameters, the pooled sensitivity and specificity were 0.85 (95% CI: 0.75–0.92) and 0.88 (95% CI: 0.74–0.97). For multiparametric MRI including APT, the pooled sensitivity and specificity were 0.92 (95% CI: 0.85–0.97) and 0.83 (95% CI: 0.55–0.97), respectively. In addition, in the three studies reported on both single and added value of APT imaging parameters, the combined imaging parameters further improved diagnostic performance, yielding pooled sensitivity and specificity of 0.91 (95% CI: 0.80–0.97) and 0.92 (95% CI: 0.79–0.98), respectively, but the pooled sensitivity was 0.81 (95% CI: 0.65-0.93) and specificity was 0.82 (95% CI: 0.61–0.94) for single APT imaging parameters.ConclusionAPT imaging showed high diagnostic performance in assessing treatment response in patients with post-treatment gliomas, and the addition of APT imaging to other advanced MRI techniques can improve the diagnostic accuracy for distinguishing TR from TE.

IntroductionHigher white blood cell (WBC) count is associated with poor functional outcome in acute ischemic stroke (AIS). However, little is known about whether the association is modified by treatment with intravenous alteplase.MethodsWAKE-UP was a randomized controlled trial of the efficacy and safety of magnetic resonance imaging [MRI]-based thrombolysis in unknown onset stroke. WBC count was measured on admission and again at 22–36 h after randomization to treatment (follow-up). Favorable outcome was defined by a score of 0 or 1 on the modified Rankin scale (mRS) 90 days after stroke. Further outcome were stroke volume and any hemorrhagic transformation (HT) that were assessed on follow-up CT or MRI. Multiple logistic regression analysis was used to assess the association between outcome and WBC count and treatment group.ResultsOf 503 randomized patients, WBC count and baseline parameters were available in 437 patients (μ = 64.7 years, 35.2% women) on admission and 355 patients (μ = 65.1 years, 34.1% women) on follow-up. Median WBC count on admission was 7.6 × 109/L (interquartile range, IQR, 6.1–9.4 × 109/L) and 8.2 × 109/L (IQR, 6.7–9.7 × 109/L) on follow-up. Higher WBC count both on admission and follow-up was associated with lower odds of favorable outcome, adjusted for age, National Institutes of Health (NIH) Stroke Scale Score, temperature, and treatment (alteplase vs. placebo, adjusted odds ratio, aOR 0.85, 95% confidence interval [CI] 0.78–0.94 and aOR 0.88, 95% CI 0.79–0.97). No interaction between WBC count and treatment group was observed (p = 0.11). Furthermore, WBC count on admission and follow-up was significantly associated with HT (aOR 1.14, 95% CI 1.05–1.24 and aOR 1.13, 95% CI 1.00–1.26). Finally, WBC count on follow-up was associated with larger stroke volume (aOR 2.57, 95% CI 1.08–6.07).ConclusionHigher WBC count is associated with unfavorable outcome, an increased risk of HT, and larger stroke volume, independent of treatment with alteplase. Whether immunomodulatory manipulation of WBC count improves stroke outcome needs to be tested.Trial RegistrationClinicalTrials.gov Identifier: NCT01525290.

Abstract Background Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. Methods The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphylococcus aureus related death by 12 weeks. Results For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption. Conclusions The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio.

Objective To explore the effect of locating the ulnar nerve compression sites and guiding the small incision so as to decompress the ulnar nerve in situ on the elbow by high‐frequency ultrasound before operation. Methods A retrospective analysis was conducted on 56 patients who underwent ultrasound‐assisted in situ decompression for cubital tunnel syndrome from May 2018 to August 2019. The patients' average age was 51.13 ± 7.35 years, mean duration of symptoms was 6.51 ± 1.96 months, and mean postoperative follow‐up was 6.07 ± 0.82 months. Nine patients had Dellon's stage mild, 39 had stage moderate, and eight had stage severe. Ultrasound and electromyography were completed in all patients before operation. The presence of ulnar nerve compressive lesion, the specific location, and the reason and extent of compression were determined by ultrasound. A small incision in situ surgery was given to decompress the ulnar nerve according to the pre‐defined compressive sites. Results All patients underwent in situ decompression. The compression sites around the elbow were as follows: two in the arcade of Struthers, one in the medial intermuscular septum, four in the anconeus epitrochlearis muscle, five beside the cyst of the proximal flexor carpi ulnaris (FCU), and the remaining 44 cases were all from the compression between Osborne's ligament to the two heads of the FCU. The compression localizations diagnosed by ultrasound were confirmed by operations. Preoperative ultrasound confirmed no ulnar nerve subluxation in all cases. The postoperative outcomes were satisfactory. There was no recurrence or aggravation of symptoms in this group of patients according to the modified Bishop scoring system; results showed that 43 cases were excellent, 10 were good, and three were fair. Conclusions High‐frequency ultrasound can accurately and comprehensively evaluate the ulnar nerve compression and the surrounding tissues, thus providing significant guidance for the precise minimally invasive treatment of ulnar nerve compression.

Introduction: In pivotal trials of patients with autosomal dominant polycystic kidney disease at risk of rapid progression, tolvaptan slowed estimated glomerular filtration rate (eGFR) decline in early-to-moderate (TEMPO 3:4 [NCT00428948]) and moderate- to late-stage (REPRISE [NCT02160145]) chronic kidney disease (CKD). Discontinuation was less frequent in REPRISE (15.0%) than TEMPO 3:4 (23.0%), given that in REPRISE, only subjects who tolerated tolvaptan 60/30 mg daily initiated the double-blind phase. We evaluated whether the greater treatment effect in REPRISE was attributable to different completion rates. Methods: We conducted post hoc analyses of TEMPO 3:4 and REPRISE completers, defined as subjects who took trial drug to the end of the treatment period in TEMPO 3:4 (3 years) or REPRISE (1 year). Efficacy (rate of change in eGFR for tolvaptan vs. placebo) was analyzed as in each trial. Subjects from TEMPO 3:4 and REPRISE were also matched by propensity score for age, gender, and baseline eGFR to explore potential additional determinants of treatment effect. Results: The annualized tolvaptan treatment effect in TEMPO 3:4 completers (difference vs. placebo of 0.98 ml/min per 1.73 m2/y) and REPRISE completers (difference of 1.23) was similar to that of the respective total trial populations (TEMPO 3:4: 0.94; REPRISE: 1.27). The treatment effect of tolvaptan was also similar between matched subjects. Conclusion: Greater treatment completion rate did not drive greater treatment effect in REPRISE. The more advanced CKD of REPRISE subjects may be more relevant. More rapid decline in kidney function in later-stage CKD enabled the effects of tolvaptan to be more easily discerned.

We present entropy balancing – a relatively new technique for estimating treatment effects, which has been under-utilised in the applied biomedical literature. Our objective is to share our experiences learned from using entropy balancing in nonexperimental studies, via Monte Carlo simulations and two empirical examples. We used the inverse probability of treatment weighting method for benchmarking the performance of entropy balancing. Entropy balancing had remarkably superior performance in terms of covariate balance and efficient estimation of treatment effects. Entropy balancing does not require extensive tweaking of the propensity score specification to achieve the optimal covariate balance. Instead, it directly incorporates covariate balance into the weight function that is applied to the sample units. Notably, the excellent performance of entropy balancing is not only on the first moment (mean) but also on higher-order moments. However, we also highlighted the situations where entropy balancing may fail or not have optimal performance. Entropy balancing merits more widespread adoption in biomedical research.

Abstract Background We propose a new measure of treatment effect based on the expected reduction in the number of patients to treat (RNT) which is defined as the difference of the reciprocals of clinical measures of interest between two arms. Compared with the conventional number needed to treat (NNT), RNT shows superiority with both binary and time-to-event endpoints in randomized controlled trials (RCTs). Methods Five real RCTs, two with binary endpoints and three with survival endpoints, are used to illustrate the concept of RNT and compare the performances between RNT and NNT. For survival endpoints, we propose two versions of RNT: one is based on the survival rate and the other is based on the restricted mean survival time (RMST). Hypothetical scenarios are also constructed to explore the advantages and disadvantages of RNT and NNT. Results Because there is no baseline for computation of NNT, it fails to differentiate treatment effect in the absolute scale. In contrast, RNT conveys more information than NNT due to its reversed order of differencing and inverting. For survival endpoints, two versions of RNT calculated as the difference of the reciprocals of survival rates and RMSTs are complementary to each other. The RMST-based RNT can capture the entire follow-up profile and thus is clinically more intuitive and meaningful, as it inherits the time-to-event characteristics for survival endpoints instead of using truncated binary endpoints at a specific time point. Conclusions The RNT can serve as an alternative measure for quantifying treatment effect in RCTs, which complements NNT to help patients and clinicians better understand the magnitude of treatment benefit.

A new method for estimating the conditional average treatment effect is proposed in this paper. It is called TNW-CATE (the Trainable Nadaraya–Watson regression for CATE) and based on the assumption that the number of controls is rather large and the number of treatments is small. TNW-CATE uses the Nadaraya–Watson regression for predicting outcomes of patients from control and treatment groups. The main idea behind TNW-CATE is to train kernels of the Nadaraya–Watson regression by using a weight sharing neural network of a specific form. The network is trained on controls, and it replaces standard kernels with a set of neural subnetworks with shared parameters such that every subnetwork implements the trainable kernel, but the whole network implements the Nadaraya–Watson estimator. The network memorizes how the feature vectors are located in the feature space. The proposed approach is similar to transfer learning when domains of source and target data are similar, but the tasks are different. Various numerical simulation experiments illustrate TNW-CATE and compare it with the well-known T-learner, S-learner, and X-learner for several types of control and treatment outcome functions. The code of proposed algorithms implementing TNW-CATE is publicly available.

This paper evaluates the impact of a market price support measure (TPSP) on the chemical fertilizer use in China. This policy was implemented by the government to support farmers’ income and grain production in four provinces in Northeast China. This paper collected data of 390 counties before and after the policy implementation, then applied difference-indifferences (DID) and DID-matching approaches to evaluate the treatment effects and find evidence that supports this identification strategy. The results show that an average of approximately 8.7% of chemical fertilizer was overused by each county that implemented the policy compared to the counterfactual scenario with no policy. Our findings suggest that the impact of market price support measures on the environment needs to be considered.