Analysis Of Treatment Effects Research Articles

A Comparative Analysis of Treatment Effects of PowerScope and AdvanSync2 in Class II Division 1 Malocclusion: A Retrospective Study.

Fixed functional appliances are widely used for the treatment of retrognathic mandibles in skeletal class II malocclusion. The primary objective of the present investigation was to evaluate and compare the treatment effects induced by PowerScope (American Orthodontics, Sheboygan, WI) and AdvanSync2 (Ormco, Orange, CA). The hypothesis posited that there were no notable disparities in the therapeutic impacts of the two appliances. A retrospective study was undertaken involving 90 subjects with retrognathic mandibles. Group 1 was treated with AdvanSync2, group 2 received PowerScope treatment, and group 3 consisted of an untreated class II control sample. Lateral cephalograms were traced at pre-treatment (T0) and post-treatment (T1), to measure various skeletal, dental, and soft tissue parameters. The comparison between the groups was done using analysis of variance (ANOVA) and post-hoc analysis by Tukey's test. Significant changes were observed in all the parameters, comparing the effects of both appliances to the control group (p < 0.05). AdvanSync2 displayed statistically significant skeletal effects on the maxillaand mandible (p <0.05). The statistically significant differences were also seen for dental effects such as reduction in overbite and overjet. On the other hand, PowerScope exhibited effects that were not considered statistically significant on the maxilla, instead primarily manifesting dento-alveolar changes that led to a considerable reduction in overbite. In comparison to the control group, both appliances notably produced soft tissue changes. Our study rejected the null hypothesis. AdvanSync2 yielded superior skeletal outcomes with greater mandibular advancement, compared to PowerScope, which exhibited enhanced dento-alveolar alterations. AdvanSync2 took less treatment time, compared toPowerScope.

Research on fiscal policies supporting green and low-carbon transition to promote energy conservation and emission reduction in cities: Empirical evidence from China

Cities have emerged as critical and challenging places for promoting the green and low-carbon transformation of the economy and society in the context of the “dual carbon” targets. Meanwhile, fiscal policy is also an indispensable component of the public policy framework supporting the green and low-carbon transformation. This paper uses the pilot project of “Comprehensive Demonstration Cities for Energy Conservation and Emission Reduction Fiscal Policies” (“ECER-CD” policy) implemented in China since 2011 as a quasi-natural experiment. This research theoretically analyzes and empirically tests the energy conservation and emission reduction effects of fiscal policies supporting green and low-carbon transformation utilizing methodologies such as time-varying DID and multiple periods and multiple groups of DID. The study finds that the pilot cities effectively reverse the behavioural bias of local governments and promote energy conservation and emission reduction efforts. This conclusion holds after undergoing robustness tests such as parallel trend analysis and heterogeneity treatment effects. The effectiveness of the pilot policy is attributed to the upgrade of industrial structure, improvement of energy efficiency, and the effects of green technology innovation. However, cities’ geographic location and resource dependency can interfere with the effective implementation of policies. This paper also further explores the environmental dividends and economic consequences of the policy and finds that the pilot cities have certain environmental dividends and do not significantly restrain economic and social development. This study broadens the research perspective on fiscal policies in energy conservation and emission reduction and clarifies the pathways through which fiscal policies promoting green, low-carbon transformation drive energy conservation and emission reduction in cities. Moreover, it also improves the use of the Difference-in-Differences method in evaluating policy effects in scenarios such as the overlapping implementation of pilot policies and policy withdrawal. Finally, this paper proposes policy recommendations to provide more empirical evidence for China to accelerate the green and low-carbon transformation of the economy and society.

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. Here we present a causal-inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (causal independent effect module attribution + optimal transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment-effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment-effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly generated datasets: (1) rhinovirus and cigarette-smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette-smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.

A New, Effective, Safe, and Accurate Method of Cervical Selective Nerve Root Block

Objective: Cervical selective nerve root block (SNRB) is an effective procedure that has long been performed for patients with radiating pain or paresthesia. However, there are several delicate structures around the cervical nerve root, damage to which could lead to irreversible neurovascular injury. Therefore, cervical SNRB requires a high skill level and extensive experience to execute it safely. To overcome these disadvantages, we used biplane computed tomography (CT)-guided fluoroscopy and a needle guidance program to achieve symptom improvement in a safer and shorter procedure. This study aimed to investigate the new method and report on its excellent clinical effects.Methods: A total of 57 patients who experienced radiating pain due to cervical spinal stenosis resulting from a herniated disc were enrolled. From September 2020 to September 2021 subjects underwent a biplane CT-guided fluoroscopic nerve block at an outpatient clinic. All procedures were performed by biplane CT-guided fluoroscopy. We analyzed treatment effects with a numerical rating scale (NRS) at 2 and 4 weeks after the procedure. Logistic regression was performed to reveal the significance of changes in NRS after the injection.Results: Pain improved in 56 of 57 patients (98%) after 2 and 4 weeks of follow-up compared to pre-injection pain. At the 2-week follow-up, the mean reduction based on NRS scores was 3.226 (2.782-3.674, 0; 95% confidence interval [CI], p &lt; 0.001) indicating a 48% decrease in pain scores compared with the initial NRS. The mean reduction in NRS 4 weeks after the procedure was 3.544 (3.090-3.998, 0; 95% CI, p &lt; 0.001) indicating a 52% reduction compared with the initial NRS. The average duration of the procedure was 4 minutes. Conclusion: We demonstrate a clinically effective, safe, and accurate method using a biplane CT-guided fluoroscopy and needle guide program. This new method can be an easy and relatively uncomplicated alternative to treatment for patients with cervical radiculopathy and provides safe and accurate targeting, making it easy for inexperienced surgeons.

Analysis of children's sub-health treatment effect based on multi-scale feature fusion network from the perspective of medical informatization

Analysis of children's sub-health treatment effect based on multi-scale feature fusion network from the perspective of medical informatization

Comparison of safety and healthcare utilization following sleeve gastrectomy or gastric bypass among medicare beneficiaries using sex as a biologic variable.

Bariatric surgery is a common operation, but differences in outcomes between males and females are unknown. To compare the risk of mortality, complications, reintervention, and healthcare utilization after sleeve gastrectomy or gastric bypass using sex as a biologic variable. United States. Retrospective cohort study of adults undergoing sleeve gastrectomy or gastric bypass from January 1, 2012 to December 31, 2018 using Medicare claims data. We performed a heterogeneity of treatment effect analysis to determine the impact of sleeve gastrectomy versus gastric bypass comparing males to females. The primary outcome was safety (mortality, complications, and reinterventions) up to 5 years after surgery. The secondary outcome was healthcare utilization (hospitalization and emergency department use). Among 95,405 patients the majority (n = 71,348; 74.8%) were female and most (n = 57,008; 59.8%) underwent sleeve gastrectomy. For all patients, compared to gastric bypass, sleeve gastrectomy was associated with a lower risk of complications and reintervention but a higher risk of revision. Compared to gastric bypass, sleeve gastrectomy was associated with a lower risk of mortality for females (adjusted hazard ratio .86, 95% CI .75-.96) but not males. We found no difference in procedure treatment effect by sex for mortality, hospitalization, emergency department use, or overall reintervention when comparing sleeve to gastric bypass. Females and males have similar outcomes following bariatric surgery. Females have a lower risk of complications but a higher risk of reintervention. Decisions surrounding treatment for this common procedure should be tailored to include a discussion of sex-specific differences in treatment outcome.

Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes

Missing data are unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.

Enhanced antitumour immunity following neoadjuvant chemoradiotherapy mediates a favourable prognosis in women with resected pancreatic cancer

BackgroundThis study investigates sex disparities in clinical outcomes and tumour immune profiles in patients with pancreatic ductal adenocarcinoma (PDAC) who underwent upfront resection or resection preceded by gemcitabine-based neoadjuvant chemoradiotherapy...

Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data

BackgroundReal-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks.MethodsFor exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented.ResultsOur study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results.ConclusionsExtending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

Does Tax Simplification Motivate Small Businesses to be More Compliant? Evidence from a Regression Discontinuity in Indonesia

This research investigates how the tax simplification program in Indonesia affects tax compliance among small businesses, by employing a regression discontinuity design and examining various parameters of tax compliance. The results indicate that tax simplification does not significantly influence tax payment behaviour. However, it does have a positive effect on overall tax compliance, demonstrated by more frequent reporting and fewer penalties imposed. Analysis of heterogeneous treatment effects reveals considerable variation across geographic areas and internet access levels. Taxpayers in areas with decent internet access and near a tax office appear to benefit more from the program.

The Effect of Applying Sustainability (Maqasid Shariah) and Competition on Islamic Bank Financing

In the last decade, the Islamic Bank (IB) has been contributing to global financial development. There are inconclusive results about the function of IB to meet Islamic Finance ethics, particularly sustainability from the IB perspective (called Maqasid Shariah-MS). This paper aims to contribute to the basic research question: does sustainability matter in improving competition in IB financing? This study developed an advanced empirical model from previous theoretical studies that separated IB behavior using MS or less MS. To distinguish this effect, this study employed causal inference with Instrumental Variable (IV) analysis. The IV-Probit was applied to conduct a simulated treatment effect analysis accordingly. The dataset was obtained and combined from two global IB sustainability indicators and comprehensive financial statements of IB across 35 banks in 12 countries. The results confirm that applying IB in different MS with similar increasing level of competition produce different growth of financing. The IB with MS has larger financing than less MS. These results confirm that sustainability matters for improving competition and financing growth.

Evaluation of Treatment Effect and Mechanism Analysis of Ti6AL4V Porous Scaffolds Prepared by Selective Laser Melting with Different Chemical Polishing Processes

Evaluation of Treatment Effect and Mechanism Analysis of Ti6AL4V Porous Scaffolds Prepared by Selective Laser Melting with Different Chemical Polishing Processes

Analysis of cost and treatment effects in the care given for graves disease - a swedish cost-utility analysis

Analysis of cost and treatment effects in the care given for graves disease - a swedish cost-utility analysis

Allogeneic Adipose-Derived Mesenchymal Stem Cell Transplantation Alleviates Atherosclerotic Plaque by Inhibiting Ox-LDL Uptake, Inflammatory Reaction and Endothelial Damage in Rabbits.

Atherosclerosis (AS) is a chronic inflammatory disease of arteries fueled by lipids. It is a major cause of cardiovascular morbidity and mortality. Mesenchymal stem cells have been used for the treatment of atherosclerotic lesions. Adipose-derived stem cells (ADSCs) have been shown to regulate the activation state of macrophages and exhibit anti-inflammatory capabilities. However, the effect of allogeneic ADSCs in the treatment of AS have not been investigated. In this study, the early treatment effect and preliminary mechanism analysis of allogeneic rabbit ADSCs intravenous transplantation were investigated in a high-fat diet rabbit model. The polarization mechanism of rabbit ADSCs on the macrophage was further analyzed in vitro. Compared with the model group, blood lipid levels declined, the plaque area, oxidized low-density lipoprotein (ox-LDL) uptake, scavenger receptor A1 and cluster of differentiation (CD) 36 levels were all significantly reduced, and the accumulation of inflammatory M1 macrophages, apoptosis, interleukin (IL)-6 and tumor necrosis factor (TNF)-α expression were decreased. The endothelial cells (CD31), M2 macrophages, IL-10 and the transforming growth factor (TGF)-β levels increased. In vitro, ADSCs can promote the M1 macrophage phenotypic switch toward the M2 macrophage through their secreted exosomes, and the main mechanism includes increasing arginase 1 expression and IL-10 secretion, declining inducible nitric oxide synthase (iNOS) expression and TNF-α secretion, and activating the STAT6 pathway. Therefore, allogeneic rabbit ADSC transplantation can transmigrate to the aortic atherosclerotic plaques and show a good effect in lowering blood lipids and alleviating atherosclerotic plaque in the early stage of AS by inhibiting ox-LDL uptake, inflammatory response, and endothelial damage.

Trajectory analysis of microwave ablation treatment effect on the quality of life and emotional distress in patients with papillary thyroid cancer.

Trajectory changes of quality of life (QOL) and psychological status in patients with T1N0M0 papillary thyroid cancer (PTC) after microwave ablation (MWA) treatment. Patients with T1N0M0 PTC who underwent MWA from March to August 2021 were included in this study. Patients completed the European Organisation for Research and Treatment of Cancer questionnaire (QLQ-C30), Thyroid Cancer-Specific Quality of Life Questionnaire (THYCA-QOL) and Hospital Anxiety and Depression Scale (HADS) at baseline, 3 months, 6 months, 9 months and 12 months after MWA. 181 patients were enrolled in this study. All patients met a minimum of 12 months of follow-up. The QOL at baseline and 12 months after MWA were 14.95 ± 1.78 and 14.19 ± 1.73 (P < 0.01), respectively. Patients' anxiety was 3.33 ± 1.81 and 1.49 ± 2.14 (P < 0.01), respectively. Patients' depression was 2.14 ± 2.10 and 1.40 ± 2.22 (P < 0.01), respectively. In the trajectory analysis, 73%, 19% and 8% of patients achieved significant improvement, slight improvement and stable in QOL, respectively, with older, female, lower education and lower monthly income as the risk factors of QOL. 63%, 31% and 6% of patients achieved significant improvement, slight improvement and stable in anxiety, respectively, with younger, female, lower education, lower monthly income, unmarried or divorced and higher T3, T4 levels as the risk factors of anxiety. 72%, 21% and 7% of patients achieved significant improvement, slight improvement and stable in depression, respectively, with younger, lower monthly income and unmarried or divorced as the risk factors of depression. MWA can effectively improve the QOL and emotional distress of patients with T1N0M0 PTC.

A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer

BackgroundMany scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used.MethodsA PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported.ResultsOf 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity.ConclusionsMost studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.

Is smart carbon emission reduction justified in China? Evidence from national big data comprehensive pilot zones

The intelligent revolution caused by new digital technologies has provided new impetus to reduce carbon emissions. However, the current research on new digital technologies and carbon emissions is still in its infancy and lacks empirical conclusions between them. Therefore, this paper studies the impact of new digital technologies on carbon emissions, identifies its mechanism, and analyzes the regional heterogeneity of its effects. This research treats the National Big Data Comprehensive Pilot Zones pilot in China as a quasi-natural experiment for the development of new digital technologies along with city-level data covering from 2011 to 2019 to conduct a staggered difference-in-difference (DID) model analysis. We find that new digital technologies significantly reduce carbon emissions. This conclusion is still valid after a series of robustness tests such as heterogeneity treatment effect analysis, ex-ante trend test, spillover effect test, and placebo test. Additionally, new digital technologies can reduce carbon emissions by promoting the transformation of industrial structure, improving the level of green technology innovation, and promoting industrial agglomeration. At the same time, the heterogeneity analysis shows that new digital technologies' carbon emission reduction effect is more evident in non-western regions, southern regions, and large cities. To expand the carbon emission reduction effect of new digital technologies, the government should promote the development and application of new digital technologies, and implement differentiated policies based on regional characteristics.

Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters.

Impact of anxiety and tourists' habits on their intention to vacation during and after the COVID-19 pandemic: Treatment effect analysis

This research aims to examine how tourists' anxiety, fear, and travel habits influenced their travel intentions to Switzerland after the first wave of the COVID-19 pandemic and the relaxation of containment measures. This study uses the endogenous treatment effect method to analyze the impact of travel habits and fear on travel intentions, surveying 1042 travelers from Western Europe, India, the United States, and Switzerland and applying the two-stage least squares (2SLS) technique. The findings indicate that non-anxious tourists visiting rural areas and usually on short to medium-duration vacations were highly willing to travel. Anxious tourists who had previously traveled to ski resorts also expressed willingness to travel after the COVID-19 pandemic. In addition, non-anxious tourists typically making individual travel arrangements had greater intent to travel. This analysis suggests that positive past travel experiences increase the desire to travel despite negative factors like health risks and social distancing constraints, and the primary motivation for travel is to engage in outdoor activities as a response to confinement These results are important for travel agencies as they can leverage them to target and encourage tourists with appropriate offers and targeted promotional campaigns.

Use of Quantile Treatment Effects Analysis to Describe Antidepressant Response in Randomized Clinical Trials Submitted to the US Food and Drug Administration

Major depressive disorder (MDD) is a leading cause of global distress and disability. Earlier studies have indicated that antidepressant therapy confers a modest reduction in depressive symptoms on average, but the distribution of this reduction requires more research. To estimate the distribution of antidepressant response by depression severity. In this secondary analysis of pooled trial data, quantile treatment effect (QTE) analysis was conducted from the US Food and Drug Administration (FDA) database of antidepressant monotherapy for patients with MDD, encompassing 232 positive and negative trials submitted to the FDA between 1979 and 2016. Analysis was restricted to participants with severe MDD (17-item Hamilton Rating Scale for Depression [HAMD-17] score ≥20). Data analysis was conducted from August 16, 2022, to April 16, 2023. Antidepressant monotherapy compared with placebo. The distribution of percentage depression response was compared between the pooled treatment arm and pooled placebo arm. Percentage depression response was defined as 1 minus the ratio of final depression severity to baseline depression severity, expressed as a percentage. Depression severity was reported in HAMD-17-equivalent units. A total of 57 313 participants with severe depression were included in the analysis. There was no significant imbalance in baseline depression severity between the pooled treatment arm and pooled placebo arm, with a mean HAMD-17 difference of 0.037 points (P = .11 by Wilcoxon rank sum test). An interaction term test for rank similarity did not reject the rank similarity governing percentage depression response (P > .99). The entire distribution of depression response was more favorable in the pooled treatment arm than in the pooled placebo arm. The maximum separation between treatment and placebo occurred at the 55th quantile and corresponded to an absolute improvement in depression due to active drug of 13.5% (95% CI, 12.4%-14.4%). The separation between treatment and placebo diminished near the tails of the distribution. In this QTE analysis of pooled clinical trial data from the FDA, antidepressants were found to confer a small reduction in depression severity that was broadly distributed across participants with severe depression. Alternatively, if the assumptions behind the QTE analysis are not met, then the data are also compatible with antidepressants eliciting more complete response in a smaller subset of participants than is suggested by this QTE analysis.