Some recent developments in modeling quantile treatment effects

This paper provides a selective review of the recent developments on econometric/statistical modeling in quantile treatment effects under both selection on observables and on unobservables.First,we discuss identification,estimation and inference of quantile treatment effects under the framework of selection on observables.Then,we consider the case where the treatment variable is endogenous or self-selected,for which an instrumental variable method provides a powerful tool to tackle this problem.Finally,some extensions are discussed to the data-rich environments,to the regression discontinuity design,and some other approaches to identify quantile treatment effects are also discussed.In particular,some future research works in this area are addressed.

Nitrogen Plasma Treatment Effect on Graphene Sheeted Vertically Aligned Carbon Nanofibers

Nitrogen plasma treatment effect on GS-CNFs （graphene seeted vertically aligned carbon nanofibers） has been studied. GS-CNFs were grown on nickel coated cupper substrates by DC-plasma CVD （chemical vapor deposition） at relatively low temperature. GS-CNFs were studied by SEM （scanning electron microscopy）, HR-TEM （high-resolution transmission electron microscopy）, XPS and Raman measurements. GS-CNFs are composed of cylindrical shaped having pure graphite sheets with about 5 μm length and nanometer size tips and roots diameter. Nitrogen plasma treatment causes nitrogen chemical etching on the graphene seeted carbon nanofibers were disordered its fine shape and increase the graphetization due to nitrogen incorporation.

Pharmacological Treatment of Adult Attention-Deficit/Hyperactivity Disorder(ADHD)in a Longitudinal Observational Study:Estimated Treatment Effect Strengthened by Improved Covariate Balance

An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.

Improving Model Specifications When Estimating Treatment Effects across Alternative Medical Interventions

Objective: The purpose of this paper is to critique the list of independent variables commonly used in observational research and test the impact of variables for prior use and treatment history on estimates of treatment effects. Methods: Using data from the California Medicaid program, this study generated a series of OLS estimates of the effect of atypical antipsychotic medications on costs and duration of therapy to illustrate the impact of alternative model specifications on treatment effects. The first sequence of estimates consisted of six model specifications, the last of which included variables reflecting the type of episode defined according to prior treatment history and compliance. The second sequences repeated the specification of the first 6 models but were carried out separately by episode type to examine the heterogeneity of treatment effect. The second sequence of models documented the impact of additional drug history variables. Results: Estimates of the impact of atypical antipsychotic use on total costs and duration on initial drug were statistically significant in the first 6 models. Estimates changed significantly when dummy variables indicating prior use of inpatient service and nursing home care were included in the model specification. Estimated effects changed substantially when prior total cost was included in cost analysis, or when prior treatment duration was included in duration analysis. Significant variation also existed in estimated effects across episode types, and it was particularly pronounced before controlling for prior cost/duration. Conclusion: It is important to add prior measures of the outcome variable to control for unobserved bias in retrospective studies. Also, the accuracy and utility of results to clinicians can be improved significantly if analyses are performed by episode type.

Variations in quantifying patient reported outcome measures to estimate treatment effect

In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.

Robust Estimation of Average Treatment Effects with Observational Studies

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

Evaluation of the Effectiveness and Efficiency of Extracorporeal Shock Wave Combined with Rehabilitation Training in the Treatment of Muscle Articulation Chronic Pain

Objective: To analyze the effect of combined extracorporeal shock wave and rehabilitation training treatment in patients with muscle articulation chronic pain (MACP). Methods: Ninety-seven MACP patients admitted to our hospital from September 2021 to September 2023 were randomly selected and were divided into Group A (control group, 46 cases, rehabilitation training treatment) and Group B (observation group, 51 cases, extracorporeal shock wave with rehabilitation training treatment), and outcomes of the two groups were compared. Results: The treatment efficiency, post-treatment clinical indexes (upper and lower limb function scores, activities of daily living (ADL) scores, visual analog scale (VAS) scores), and short-form 36 (SF-36) scores of Group B were better than those of Group A (P < 0.05). Conclusion: Combined extracorporeal shock wave and rehabilitation training treatment for MACP patients improved their limb function, daily activities, quality of life, and reduced pain.

Delayed treatment effect predicting(DTEP)model for guiding immuno-oncology trial designs

Background:The presence of delayed treatment effects(DTE)is common in immuno-oncology trials.However,conventional trial designs often overlook the potential presence of DTE,which can result in an underestimation of the required sample size and loss of statistical power.Conversely,when there is actually no apparent delay in treatment effects,alternative trial designs for addressing DTE may lead to an over-estimation of sample size and unnecessary prolongation of the trial duration.To mitigate this challenge,we propose the use of a DTE predicting(DTEP)model to better guide immuno-oncology trial designs.Methods:The DTEP model was developed and validated using data from 147 pub-lished randomized immuno-oncology trials.The eligible trials were divided into a training set(approximately 75%of the trials)and a test set(approximately 25%).We employed linear discriminant analysis(LDA)to develop the DTEP model for pre-dicting the DTE status using baseline characteristics available at the trial design stage.The receiver operating characteristic(ROC)curve was utilized to assess the ability of the model to distinguish between trials with and without DTE.We further re-conducted the JUPITER-02 trial in a simulation setting,employing three design approaches to assess the potential benefits of utilizing the DTEP model.Results:Baseline characteristics available during the trial design stage,including cancer type,line of treatment,and experimental and control arm regimens were incorporated,and high accuracy in predicting the DTE status in both the training set(area under the operating characteristic curve(AUC),0.79;95%confidence interval(CI),0.71-0.88)and test set(AUC,0.78;95%CI,0.66-0.90)was achieved.Notably,the model successfully predicted the DTE status in two randomized trials among the test sets that were conducted by our team(ESCORT-1st(absence of DTE)and JUPITER-02(presence of DTE)).In silico re-conduct of the JUPITER-02 trial further showed that the statistical power would be markedly improved when trial designs were guided by the DTEP model.Conclusions:The DTEP model can significantly enhance the precision and effectiveness of immuno-oncology trial designs,thereby facilitating the discovery of effective im-munotherapeutics in a more streamlined and expedited manner.

Evaluation of the Efficacy and Effectiveness of Hysteroscopic Electroresection in the Treatment of Submucosal Uterine Fibroids

Objective:To evaluate the effect of hysteroscopic electroresection in treating submucosal uterine fibroids.Methods:Using the random number table method,70 patients with submucosal uterine fibroids were divided into two groups,35 cases/group.The control group underwent laparoscopic myomectomy,and the observation group underwent hysteroscopic electroresection.Surgical indicators,sex hormone indicators,inflammation indicators,and treatment effects were compared between the two groups.Results:The surgical indicators,sex hormone indicators,and inflammatory indicators three months after operation in the observation group were all more ideal than those of the control group.The total effective rate of the observation group(97.14%)was higher than that of the control group(P<0.05).Conclusion:Hysteroscopic electroresection for the treatment of submucosal uterine fibroids was less invasive,accelerates recovery,and is more effective.

Analysis of the Effectiveness of Modified Non- Traumatic Filling Technology in the Treatment of Pediatric Dental Caries

Objective:To analyze the effectiveness of modified non-traumatic filling technology in the treatment of pediatric dental caries.Methods:Ninety-seven children with dental caries who were treated in our hospital(Panyu Maternal and Child Care Service Center of Guangzhou)from January 2022 to December 2023 were selected and randomly divided into two groups,with 48 cases in the experimental group and 49 in the control group.The experimental group was treated with modified non-traumatic filling techniques,while the control group was treated with conventional filling techniques.Observation indicators such as the total effectiveness of the treatment,incidence of adverse events,treatment compliance,and pain scores were analyzed after the intervention.Result:After intervention,the total effectiveness of the treatment in the experimental group was higher than that in the control group(P<0.05).The incidence of adverse events in the experimental group was lower than that of the control group(P<0.05).The treatment compliance of patients in the experimental group was higher than that in the control group(P<0.05).The pain score of the experimental group was lower than that of the control group(P<0.05).Conclusion:The application of modified non-traumatic filling technology in the treatment of pediatric dental caries showed good therapeutic outcomes.After the intervention,the child’s symptoms were significantly alleviated,the incidence of adverse events such as filling material falling off was reduced,their compliance was improved,and the pain was relieved.This procedure is worth to be promoted for clinical application.

Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects

Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects.This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic(2014),the stable balance weighting procedure by Zubizarreta(2015),the calibration balance weighting approach by Chan,et al.(2016),and the integrated propensity score technique by Sant’Anna,et al.(2020).Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods.Simulation results show that in general,the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes.Finally,the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.

Performance of a water hyacinth(Eichhornia crassipes)system in the treatment of wastewater from a duck farm and the effects of using water hyacinth as duck feed

Nowadays, intensive breeding of poultry and livestock of large scale has made the treatment of its waste and wastewater an urgent environmental issue, which motivated this study. A wetland of 688 mz was constructed on an egg duck farm, and water hyacinth （Eichhornia crassipes） was chosen as an aquatic plant for the wetland and used as food for duck production. The objectives of this study were to test the role of water hyacinth in purifying nutrient-rich wastewater and its effects on the ducks＇ feed intake, egg laying performance and egg quality. This paper shows that the constructed wetland removed as much as 64.44% of chemical oxygen demand （COD）, 21.78% of total nitrogen （TN） and 23.02% of total phosphorus （TP）. Both dissolved oxygen （DO） and the transparency of the wastewater were remarkably improved, with its transparency 2.5 times higher than that of the untreated wastewater. After the ducks were fed with water hyacinth, the average daily feed intake and the egg-laying ratio in the test group were 5.86% and 9.79% higher, respectively, than in the control group; the differences were both significant at the 0.01 probability level. The egg weight in the test group was 2.36% higher than in the control group （P 〈 0.05）, but the feed conversion ratios were almost the same. The eggshell thickness and strength were among the egg qualities significantly increased in ducks fed with water hyacinth. We concluded that a water hyacinth system was effective for purifying wastewater from an intensive duck farm during the water hyacinth growing season, as harvested water hyacinth had an excellent performance as duck feed. We also discussed the limitations of the experiment.

Impact of failure mode and effects analysis-based emergency management on the effectiveness of craniocerebral injury treatment

BACKGROUND Craniocerebral injuries encompass brain injuries,skull fractures,cranial soft tissue injuries,and similar injuries.Recently,the incidence of craniocerebral injuries has increased dramatically due to the increased numbers of traffic accidents and aerial work injuries,threatening the physical and mental health of patients.AIM To investigate the impact of failure modes and effects analysis(FMEA)-based emergency management on craniocerebral injury treatment effectiveness.METHODS Eighty-four patients with craniocerebral injuries,treated at our hospital from November 2019 to March 2021,were selected and assigned,using the random number table method,to study(n=42)and control(n=42)groups.Patients in the control group received conventional management while those in the study group received FMEA theory-based emergency management,based on the control group.Pre-and post-interventions,details regarding the emergency situation;levels of inflammatory stress indicators[Interleukin-6(IL-6),C-reactive protein(CRP),and procalcitonin(PCT)];incidence of complications;prognoses;and satisfaction regarding patient care were evaluated for both groups.RESULTS For the study group,the assessed parameters[pre-hospital emergency response time(9.13±2.37 min),time to receive a consultation(2.39±0.44 min),time needed to report imaging findings(1.15±4.44 min),and test reporting time(32.19±6.23 min)]were shorter than those for the control group(12.78±4.06 min,3.58±0.71 min,33.49±5.51 min,50.41±11.45 min,respectively;P<0.05).Pre-intervention serum levels of IL-6(78.71±27.59 pg/mL),CRP(19.80±6.77 mg/L),and PCT(3.66±1.82 ng/mL)in the study group patients were not significantly different from those in the control group patients(81.31±32.11 pg/mL,21.29±8.02 mg/L,and 3.95±2.11 ng/mL respectively;P>0.05);post-intervention serum indicator levels were lower in both groups than pre-intervention levels.Further,serum levels of IL-6(17.35±5.33 pg/mL),CRP(2.27±0.56 mg/L),and PCT(0.22±0.07 ng/mL)were lower in the study group than in the control group(30.15±12.38 pg/mL,3.13±0.77 mg/L,0.38±0.12 ng/mL,respectively;P<0.05).The complication rate observed in the study group(9.52%)was lower than that in the control group(26.19%,P<0.05).The prognoses for the study group patients were better than those for the control patients(P<0.05).Patient care satisfaction was higher in the study group(95.24%)than in the control group(78.57%,P<0.05).CONCLUSION FMEA-based craniocerebral injury management effectively shortens the time spent on emergency care,reduces inflammatory stress and complication risk levels,and helps improve patient prognoses,while achieving high patient care satisfaction levels.

Quantile treatment effect estimation with dimension reduction

Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation.We consider their estimation in observational studies with many possible covariates under the assumption that treatment and potential outcomes are independent conditional on all covariates.To obtain valid and efficient treatment effect estimators,we replace the set of all covariates by lower dimensional sets for estimation of the quantiles of potential outcomes.These lower dimensional sets are obtained using sufficient dimension reduction tools and are outcome specific.We justify our choice from efficiency point of view.We prove the asymptotic normality of our estimators and our theory is complemented by some simulation results and an application to data from the University of Wisconsin Health Accountable Care Organization.

A new quantile treatment effect model for studying smoking effect on birth weight during Mother's pregnancy

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.

Quasi-likelihood estimation of average treatment effects based on model information

In this paper, the estimation of average treatment effects is considered when we have the model information of the conditional mean and conditional variance for the responses given the covariates. The quasi-likelihood method adapted to treatment effects data is developed to estimate the parameters in the conditional mean and conditional variance models. Based on the model information, we define three estimators by imputation, regression and inverse probability weighted methods. All the estimators are shown asymptotically normal. Our simulation results show that by using the model information, the substantial efficiency gains are obtained which are comparable with the existing estimators.

Estimation of Treatment Effects in Nonlinear Models with Unobserved Confounding

Estimation of treatment effects is one of the crucial mainstays in economics and sociology studies.The problem will become more serious and complicated if the treatment variable is endogenous for the presence of unobserved confounding.The estimation and conclusion are likely to be biased and misleading if the endogeny of treatment variable is ignored.In this article,we propose the pseudo maximum likelihood method to estimate treatment effects in nonlinear models.The proposed method allows the unobserved confounding and random error terms to exist in an arbitrary relationship(such as,add or multiply),and the unobserved confounding have different influence directions on treatment variables and outcome variables.The proposed estimator is consistent and asymptotically normally distributed.Simulation studies show that the proposed estimator performs better than the special regression estimator,and the proposed method is stable for various distribution of error terms.Finally,the proposed method is applied to the real data that studies the influence of individuals have health insurance on an individual’s decision to visit a doctor.

Empirical Likelihood Approach for Treatment Effect in Pretest-Posttest Trial

The empirical likelihood approach is suggested to the pretest-posttest trial based on the constrains, which we construct to summarize all the given information. The author obtains a log-empirical likelihood ratio test statistic that has a standard chi-squared limiting distribution. Thus, in making inferences, there is no need to estimate variance explicitly, and inferential procedures are easier to implement. Simulation results show that the approach of this paper is more efficient compared with ANCOVA II due to the sufficient and appropriate use of information.

INFLUENCE OF HEAT TREATMENT ON THE COMPRESSIVE STRESS EFFECT OF MAGNETOSTRICTION OF <112> AXIAL ALIGNED Tb-Dy-Fe ALLOYS

The magnetostrictive properties of <112> axial aligned Tb0.3Dy0.7(Fe1-xMx)1.95(M=Mn, Al, x=0～ 0.15) alloys prepared by directional solidification are reported. The influence of heat treatment on microstructure and the compressive stress effect of magnetostriction is discussed.