Practical operation and theoretical basis of difference-in-difference regression in science of science:The comparative trial on the scientific performance of Nobel laureates versus their coauthors

Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.

Tree: Reducing the use of restrictive practices on psychiatric wards through virtual reality immersive technology training

BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to isolate and reduce the sensory stimulation to safeguard the patient and those within the vicinity.Using interventions by way of virtual reality(VR)could assist with reducing the need for RPs as it could help reduce anxiety or agitation by way of placing users into realistic and immersive environments.This could also aid staff to and change current RPs.AIM To assess the feasibility and effectiveness of using a VR platform to provide reduction in RP training.METHODS A randomised controlled feasibility study,accompanied by evaluations at 1 month and 6 months,was conducted within inpatient psychiatric wards at Southern Health National Health Service Foundation Trust,United Kingdom.Virti VR scenarios were used on VR headsets to provide training on reducing RPs in 3 inpatient psychiatric wards.Outcome measures included general self-efficacy scale,generalised anxiety disorder assessment 7(GAD-7),Burnout Assessment Tool 12,the Everyday Discrimination Scale,and the Compassionate Engagement and Action Scale.RESULTS Findings revealed statistically significant differences between the VR and treatment as usual groups,in the Everyday Discrimination Scale items Q8 and Q9:P=0.023 and P=0.040 respectively,indicating higher levels of perceived discrimination in the VR group.There were no significant differences between groups in terms of general self-efficacy,generalised anxiety disorder assessment 9,and Burnout Assessment Tool 12 scores.A significant difference was observed within the VR group for compassionate engagement from others(P=0.005)over time.Most respondents recorded System Usability Scale scores above 70,with an average score of 71.79.There was a significant reduction in rates of RPs in the VR group vs treatment as usual group with a fluctuating variability observed in the VR group likely due to external factors not captured in the study.CONCLUSION Ongoing advancement of VR technology enables the possibility of creating scenarios and simulations tailored to healthcare environments that empower staff by providing more comprehensive and effective training for handling situations.

Neuro-11:a new questionnaire for the assessment of somatic symptom disorder in general hospitals

Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offers promise in screening and evaluating psychosomatic symptoms,comprising 11 concise items across three dimensions:somatic symptoms,negative emotions and adverse events.Prior research has validated the scale’s reliability,validity and theoretical framework in somatoform disorders,indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability,validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD.Through this comparative analysis,we aimed to validate the effectiveness and precision of the Neuro-11,enhancing its utility in clinical settings.Methods Between November 2020 and December 2021,data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts.The patients completed multiple questionnaires,including the Neuro-11,Short Form 36 Health Survey,Patient Health Questionnaire 15 items,Hamilton Anxiety Scale and Hamilton Depression Scale.Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency,correlating significantly with internationally recognised and widely used questionnaires.Despite its brevity,the Neuro-11 exhibited significant correlations with other questionnaires.A test-retest analysis yielded a correlation coefficient of 1.00,Spearman-Brown coefficient of 0.64 and Cronbach’sαcoefficient of 0.72,indicating robust content reliability and internal consistency.Confirmatory factor analysis confirmed the validity of the three-dimensional structure(p<0.001,comparative fit index=0.94,Tucker-Lewis index=0.92,root mean square error of approximation=0.06,standardised root mean square residual=0.04).The threshold of the Neuro-11 is set at 10 points based on the maximum Youden’s index from the receiver operating characteristic curve analysis.In terms of diagnostic efficacy,the Neuro-11 has an area under the curve of 0.67.

Evaluation of a culturally adapted cognitive behavior therapy-based,third-wave therapy manual

BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.The use of CBT for severe mental illness is now more common globally.AIM To investigate the feasibility and acceptability of a culturally adapted,CBT-based,third-wave therapy manual using the Comprehend,Cope,and Connect approach with individuals from a diverse population presenting to primary and secondary healthcare services.METHODS A pilot study was used to assess the feasibility and acceptability of the manualised intervention.Outcome measures were evaluated at baseline,post-intervention and 12 wk-follow up.32 participants with mental health conditions aged 20-53 years were recruited.Assessments were completed at three time points,using Clinical Outcomes in Routine Evaluation(CORE),Hospital Anxiety and Depression Scale(HADS),Bradford Somatic Inventory and World Health Organization Disability Assessment Schedule 2.0(WHODAS).The Patient Experience Questionnaire was completed post-treatment.RESULTS Repeated measures of analysis of variance associated with HADS depression,F(2,36)=12.81,P<0.001,partialη^(2)=0.42 and HADS anxiety scores,F(2,26)=9.93,P<0.001,partialη^(2)=0.36;CORE total score and WHODAS both showed significant effect F(1.25,18.72)=14.98,P<0.001,partialη^(2)=0.5.and F(1.29,14.18)=6.73,P<0.001,partialη^(2)=0.38 respectively.CONCLUSION These results indicate the effectiveness and acceptability of the culturally adapted,CBT-based,third-wave therapy manual intervention among minoritized groups with moderate effect sizes.Satisfaction levels and acceptability were highly rated.The viability and cost-effectiveness of this approach should be explored further to support universal implementation across healthcare systems.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Preoperative serum carbohydrate antigen 19-9 levels predict early recurrence after the resection of early-stage pancreatic ductal adenocarcinoma

BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,who are expected to have a better prognosis,often experience recurrence.Thus,it is important to identify the risk factors for early recurrence and to develop an adequate treatment plan.AIM To evaluate the predictive factors associated with the early recurrence of earlystage PDAC.METHODS This study enrolled 407 patients with stage I PDAC undergoing upfront surgical resection between January 2000 and April 2016.Early recurrence was defined as a diagnosis of recurrence within 6 mo of surgery.The optimal cutoff values were determined by receiver operating characteristic(ROC)analyses.Univariate and multivariate analyses were performed to identify the risk factors for early recurrence.RESULTS Of the 407 patients,98 patients(24.1%)experienced early disease recurrence:26(26.5%)local and 72(73.5%)distant sites.In total,253(62.2%)patients received adjuvant chemotherapy.On ROC curve analysis,the optimal cutoff values for early recurrence were 70 U/mL and 2.85 cm for carbohydrate antigen 19-9(CA 19-9)levels and tumor size,respectively.Of the 181 patients with CA 19-9 level>70 U/mL,59(32.6%)had early recurrence,compared to 39(17.4%)of 226 patients with CA 19-9 level≤70 U/mL(P<0.001).Multivariate analysis revealed that CA 19-9 level>70 U/mL(P=0.006),tumor size>2.85 cm(P=0.004),poor differentiation(P=0.008),and non-adjuvant chemotherapy(P=0.025)were significant risk factors for early recurrence in early-stage PDAC.CONCLUSION Elevated CA 19-9 level(cutoff value>70 U/mL)can be a reliable predictive factor for early recurrence in early-stage PDAC.As adjuvant chemotherapy can prevent early recurrence,it should be recommended for patients susceptible to early recurrence.

ARIMA and Facebook Prophet Model in Google Stock Price Prediction

We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.

Recent advances in statistical methodologies in evaluating program for high-dimensional data

The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.

Robotic versus laparoscopic distal pancreatectomy for pancreatic ductal adenocarcinoma: A propensity score-matched analysis

Background: Minimally invasive surgery is becoming increasingly popular in the field of pancreatic surgery. However, there are few studies of robotic distal pancreatectomy(RDP) for pancreatic ductal adenocarcinoma(PDAC). This study aimed to investigate the efficacy and feasibility of RDP for PDAC. Methods: Patients who underwent RDP or laparoscopic distal pancreatectomy(LDP) for PDAC between January 2015 and September 2020 were reviewed. Propensity score matching analyses were performed. Results: Of the 335 patients included in the study, 24 underwent RDP and 311 underwent LDP. A total of 21 RDP patients were matched 1:1 with LDP patients. RDP was associated with longer operative time(209.7 vs. 163.2 min;P = 0.003), lower open conversion rate(0% vs. 4.8%;P < 0.001), higher cost(15 722 vs. 12 699 dollars;P = 0.003), and a higher rate of achievement of an R0 resection margin(90.5% vs. 61.9%;P = 0.042). However, postoperative pancreatic fistula grade B or C showed no significant intergroup difference(9.5% vs. 9.5%). The median disease-free survival(34.5 vs. 17.3 months;P = 0.588) and overall survival(37.7 vs. 21.9 months;P = 0.171) were comparable between the groups. Conclusions: RDP is associated with longer operative time, a higher cost of surgery, and a higher likelihood of achieving R0 margins than LDP.

Impact of the Political Risk on Food Reserve Ratio:Evidence Across Countries

Using an unbalanced panel data covering 75 countries from 1991 to 2019,the authors explore how the political risk impacts on food reserve ratio.The empirical findings show that an increasing political risk negatively affects food reserve ratio,and the same effects hold for both internal risk and external risk.Moreover,the authors find that the increasing external or internal risks both negatively affect food production and food exports,but external risk does not significantly impact food imports and it positively impacts food consumption,while internal risk negatively impacts food imports and food consumption.The results suggest that most governments have difficulty raising subsequent food reserve ratios in face of an increasing political risk,no matter if it is an internal risk or an external risk although the mechanisms behind the impacts are different.

Flexible,efficient,and accurate tests for epidemics

Group testing involves discovering a small subset of distinguished subjects from a large population while efficiently reducing the total number of tests.It has been widely used for industrial testing,information technology,and biology,especially epidemic screening.Tests,in reality,are noisy for the presence of false outcomes.Some tests are accurate but time-consuming,while others are cheaper but less accurate.Exactly which test to use is constrained by various considerations,such as availability,cost,accuracy,and efficiency.In this paper,we propose flexible,efficient,and accurate tests(FEATs).FEATs are based on group testing with simple but careful designs by incorporating ideas such as close contact cliques and repeated tests.FEATs could dramatically improve the efficiency or accuracy of existing tests.For example,for accurate but slow tests,the FEAT can improve efficiency multiple times without compromising accuracy.On the other hand,for fast but inaccurate tests,the FEAT can sharply reduce the false-negative rate(FNR)and significantly increase efficiency.Theoretical justifications are provided.We point out some scenarios where the FEAT can be effectively employed.

Applying Machine Learning Approach to Explore Childhood Circumstances and Self-Rated Health in Old Age-China and the US,2020-2021

Introduction:Childhood circumstances impact senior health,prompting the introduction of machine learning methods to assess their individual and collective contributions to senior health.Methods:Using health and retirement study(HRS)and China Health and Retirement Longitudinal Study(CHARLS),we analyzed 2,434 American and 5,612 Chinese participants aged 60 and above.Conditional inference trees and forests were employed to estimate the influence of childhood circumstances on self-rated health(SRH).Results:The conventional method estimated higher inequality of opportunity(IOP)values in both China(0.039,accounting for 22.67%of the total Gini coefficient 0.172)and the US(0.067,accounting for 35.08%of the total Gini coefficient 0.191).In contrast,the conditional inference tree yielded lower estimates(China:0.022,accounting for 12.79%of 0.172;US:0.044,accounting for 23.04%of 0.191),as did the forest(China:0.035,accounting for 20.35%of 0.172;US:0.054,accounting for 28.27%of 0.191).Childhood health,financial status,and regional differences were key determinants of senior health.The conditional inference forest consistently outperformed others in predictive accuracy,as demonstrated by lower out-of-sample mean squared error(MSE).Discussion:The findings emphasize the need for early-life interventions to promote health equity in aging populations.Machine learning showcases the potential in identifying contributing factors.

Forecasting Tesla’s Stock Price Using the ARIMA Model

The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.

Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning

Background.Continuous glucose monitoring(CGM)offers an opportunity for patients with diabetes to modify their lifestyle tobetter manage their condition and for clinicians to provide personalized healthcare and lifestyle advice.However,analytic toolsare needed to standardize and analyze the rich data that emerge from CGM devices.This would allow glucotypes of patients tobe identified to aid clinical decision-making.Methods.In this paper,we develop an analysis pipeline for CGM data and applyit to 148 diabetic patients with a total of 8632 days of follow up.The pipeline projects CGM data to a lower-dimensional spaceof features representing centrality,spread,size,and duration of glycemic excursions and the circadian cycle.We then useprincipal components analysis and k-means to cluster patients’records into one of four glucotypes and analyze clustermembership using multinomial logistic regression.Results.Glucotypes differ in the degree of control,amount of time spent inrange,and on the presence and timing of hyper-and hypoglycemia.Patients on the program had statistically significantimprovements in their glucose levels.Conclusions.This pipeline provides a fast automatic function to label raw CGM datawithout manual input.

On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence

In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix.The underlying population is allowed to have a general dependence structure.The result no longer follows the generalized Marcenko-Pastur law,which is brand new.It is the first result on rank correlation matrices with dependence.As applications,we study Kendall’s rank correlation matrix for multivariate normal distributions with a general covariance matrix.From these results,we further gain insights into Kendall’s rank correlation matrix and its connections with the sample covariance/correlation matrix.

Composite Quantile Estimation for Kink Model with Longitudinal Data

Kink model is developed to analyze the data where the regression function is two-stage piecewise linear with respect to the threshold covariate but continuous at an unknown kink point.In quantile regression for longitudinal data,kink point where the kink effect happens is often assumed to be heterogeneous across different quantiles.However,the kink point tends to be the same across different quantiles,especially in a region of neighboring quantile levels.Incorporating such homogeneity information could increase the estimation efficiency of the common kink point.In this paper,we propose a composite quantile estimation approach for the common kink point by combining information from multiple neighboring quantiles.Asymptotic normality of the proposed estimator is studied.In addition,we also develop a sup-likelihood-ratio test to check the existence of the kink effect at a given quantile level.A test-inversion confidence interval for the common kink point is also developed based on the quantile rank score test.The simulation studies show that the proposed composite kink estimator is more efficient than the single quantile regression estimator.We also illustrate the proposed method via an application to a longitudinal data set on blood pressure and body mass index.

Penalized M-Estimation Based on Standard Error Adjusted Adaptive Elastic-Net

When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot of data, enjoying high dimension, strong correlation and redundancy, has been generated in real life. So it is necessary to find an effective variable selection method for dealing with collinearity based on the robust method. This paper proposes a penalized M-estimation method based on standard error adjusted adaptive elastic-net, which uses M-estimators and the corresponding standard errors as weights. The consistency and asymptotic normality of this method are proved theoretically. For the regularization in high-dimensional space, the authors use the multi-step adaptive elastic-net to reduce the dimension to a relatively large scale which is less than the sample size, and then use the proposed method to select variables and estimate parameters. Finally, the authors carry out simulation studies and two real data analysis to examine the finite sample performance of the proposed method. The results show that the proposed method has some advantages over other commonly used methods.

On the MLE of the Waring distribution

The two-parameter Waring is an important heavy-tailed discrete distribution,which extends the famous Yule Simon distribution and provides more flexibility when modelling the data.The commonly used EFF(Expectation-First Frequency)for parameter estimation can only be applied when the first moment exists,and it only uses the information of the expectation and the first frequency,which is not as efficient as the maximum likelihood estimator(MLE).However,the MLE may not exist for some sample data.We apply the profle method to the log-likelihood function and derive the necessary and sufficient Conditions for the existence of the MLE of the Waring parameters.We use extensive simulation studies to compare the MLE and EFF methods,and the goodness-of-fit comparison with the Yule Simon distribution.We also apply the Waring distribution to fit an insurance data.

Exploring Apple’s Stock Price Volatility Using Five GARCH Models

The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.

SPARSE RECOVERY BASED ON THE GENERALIZED ERROR FUNCTION

In this paper,we offer a new sparse recovery strategy based on the generalized error function.The introduced penalty function involves both the shape and the scale parameters,making it extremely flexible.For both constrained and unconstrained models,the theoretical analysis results in terms of the null space property,the spherical section property and the restricted invertibility factor are established.The practical algorithms via both the iteratively reweighted■_(1)and the difference of convex functions algorithms are presented.Numerical experiments are carried out to demonstrate the benefits of the suggested approach in a variety of circumstances.Its practical application in magnetic resonance imaging(MRI)reconstruction is also investigated.