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Previous research has demonstrated that different cancer survivors with the same disease history or treatment may have different experiences of fatigue. This cross-sectional study aims to explore how demographic, oncological, sociological, psychological, and serum hormone levels interact to influence the distinct experiences of fatigue (Cancer-related fatigue occurrence and fatigue degree). General information, fatigue, psychological factors was evaluated by general information questionnaire, the Revised Piper's Fatigue Scale-Chinese Version, and the self-report Hospital Anxiety and Depression Scale. Serum hormones were measured with chemiluminescent enzyme immunoassay (Zhengzhou Antobio). 306 patients were included, 229 (74.8%) were diagnosed with CRF, including 94 (41.0%) with mild fatigue, 121 (52.8%) with moderate fatigue, and 14 (6.1%) with severe fatigue. Multivariate regression analysis showed that higher depression scores, aldosterone levels may increase the risk of CRF. Patients who are obese (BMI ≥ 28 kg/m2) may help to reduce the risk of CRF. Other contributing factors for increased levels of fatigue (p

Patients with chronic diseases, such as cancer or epilepsy, are often followed through multiple stages of clinical interventions. Dynamic treatment regimes (DTRs) are sequences of decision rules that assign treatments at each stage based on measured covariates for each patient. A DTR is said to be optimal if the expectation of the desirable clinical benefit reaches a maximum when applied to a population. When there are three or more options for treatments at each decision point and the clinical outcome of interest is a time-to-event variable, estimating an optimal DTR can be complicated. We propose a doubly robust method to estimate optimal DTRs with multicategory treatments and survival outcomes. A novel blip function is defined to measure the difference in expected outcomes among treatments, and a doubly robust weighted least squares algorithm is designed for parameter estimation. Simulations using various weight functions and scenarios support the advantages of the proposed method in estimating optimal DTRs over existing approaches. We further illustrate the practical value of our method by applying it to data from the Standard and New Antiepileptic Drugs study. In this analysis, the proposed method supports the use of the new drug lamotrigine over the standard option carbamazepine. When the actual treatments match the estimated optimal treatments, survival outcomes tend to be better. The newly developed method provides a practical approach for clinicians that is not limited to cases of binary treatment options.

Patients with spontaneous intracerebral hemorrhage (ICH) have poor functional outcomes. This study investigated the status of hospitalized patients with ICH based on multicenter inpatient information.We analyzed data from the hospital information systems (HISs) of 14 tertiary hospitals, including 6 Chinese medicine hospitals and 8 western hospitals from 7 provinces and 2 municipalities. The inclusion criteria were age ≥25 years and radiologically confirmed spontaneous ICH within two weeks of symptom onset. Age groups were as follows: 25-44, 45-59, 60-74, 75-89, 90 and over 90 years old. Data are presented as the proportion (%), median (interquartile range) or mean (standard deviation). Descriptive statistical analyses were conducted to analyze demographic features, lengths of hospital stay, hospitalization expenses, hematoma locations, mortality and major comorbid conditions or complications.Patients were recruited from October 2003 to September 2011. A total of 9475 inpatients were recruited from 14 Class III Grade A hospitals. Baseline characteristics included a mean age of 58 (20) years old and 37.2% female (3303). The largest number of ICH patients were in the 44-59-year-old group. The ratio of males to females in the 25-44-year-old age group was 2.36:1, which was the highest of all groups; the lowest male to female ratio was 1.56:1 in the 45-59-year-old age group. The ratios of males to females in the 60-74, 75-89 and ≥90-year-old age groups were 1.60:1, 1.62:1 and 1.64:1, respectively. The proportion of males in all age groups was higher than that of females, significantly greater in the 25-44-year-old age group (P 0.001). The lengths of hospital stay ranged from 15 to 28 days. The hospitalization expenses of the 25-44-year-old patients were 50,000-100,000 RMB, and the costs of the remaining age groups were 10,000-20,000 RMB. Basal ganglia/thalamic hemorrhage was the most common site of ICH in different age groups. Lobar and multiple locations were higher in those aged75 years. Hypertension was the primary comorbid condition in all age groups, and it showed a rising trend with increasing age. The second most common comorbidity in the 25-44-year-old age group was vascular abnormality, while in the other age groups, comorbidities included cerebral infarction, dyslipidemia, coronary heart disease, diabetes mellitus and arrhythmia. Mortality increased with age; mortality for those under the age of 74 years fluctuated between 4.09% and 5.93%, whereas in those in the 75-89 and ≥90-year-old age groups, mortality was 13.34% and 34.09%, respectively.As age increased, the length of hospital stay, financial burden and mortality due to ICH increased. Different age groups had different distributions of neuroimaging features, major comorbid conditions or complications.

Randomized controlled trials (RCT) are widely used in clinical efficacy evaluation studies. Linear regression is a general method to evaluate treatment efficacy considering the existence of confounding variables. However, when residuals are not normally distributed, parameter estimation based on ordinary least squares (OLS) is inefficient. This study introduces an exponential squared loss (ESL) model to evaluate treatment effect. The proposed method provides robust estimation for non-normal data. Simulation results show that it outperforms ordinary least squares regression with contaminated data. In the mild cognitive impairment (MCI) efficacy evaluation study with traditional Chinese medicine, our method is applied to construct a linear efficacy evaluation model for the difference in Alzheimer’s disease assessment scale-cognitive (ADAS-cog) scores between the final and baseline records (ADASFA), with the existence of confounding factors and non- normal residuals. The results coincide with existing medical literatures. This proposed method overcomes the limitation of confounding variables and non-normal residuals in RCT efficacy studies. It outperforms OLS on estimation efficiency in situations where the percentage of non-normal contamination reaches 30%. These advantages make it a good method for real-world clinical studies.

The network-constrained criterion is one of the fundamental variable selection models for high-dimensional data with correlated features. It is distinguished from others in that it can select features and simultaneously encourage global smoothness of the coefficients over the network via penalizing the weighted sum of squares of the scaled difference of the coefficients between neighbor vertices. However, because more features were selected while it was applied for the process of analysis of the "China Stock Market Financial Database-Financial?Ratios", the so-called adaptive network-constrained criterion was proposed to tackle the problem via assigning various weights to the lasso penalty. Similar to the adaptive lasso, the proposed model enjoys consistency in variable selection if the weights have been given correctly in advance. The simulations show that the proposed model performed better than the other variable selection techniques mentioned in the paper with regards to model fitting; meanwhile, it selected fewer features than the network-constrained criterion. Furthermore, the mean value of the cross-validation likelihood and the number of selected features are tested to be accurate enough for practical applications.

In this paper, we propose an adaptive stochastic gradient boosting tree for classification studies with imbalanced data. The adjustment of cost-sensitivity and the predictive threshold are integrated together with a composite criterion into the original stochastic gradient boosting tree to deal with the issues of the imbalanced data structure. Numerical study shows that the proposed method can significantly enhance the classification accuracy for the minority class with only a small loss in the true negative rate for the majority class. We discuss the relation of the cost-sensitivity to the threshold manipulation using simulations. An illustrative example of the analysis of suboptimal health-state data in traditional Chinese medicine is discussed.

Supplemental Digital Content is available in the text, Rationale: To establish TCM Pectoral-qi comprehensive indicators and highlight the inner structure among different variables in an objective way, the article uses Partial Least Square Second-order Latent Variable Model (PLS-SLVM) and accomplishes 3 different comprehensive indicators based on both experts diagnosis and self-test data. SLVM includes a measurement model that defines the relationship between observed variables and latent variables and a structure model that imputes relationships between latent variables. The article focuses on PLS as the estimation method. Without normal distribution and independence assumptions, PLS uses objective weighting methods based on the data. Bootstrap method (B = 200) is used to calculate the mean value and standard errors of the PLS estimates. The article chooses the percentile interval to obtain the confidence interval of PLS parameters. Patient concerns: The patients were diagnosed by the means of experts diagnosis and self-test technology. On the one hand, the patients want to know the effect of self-test by wearing a kind of instrument. On the other hand, we want to establish TCM Pectoral-qi comprehensive indicators and highlight the inner structure among different variables in an objective way. Diagnoses: The group of 59 subjects are the same no matter whether they were diagnosed through TCM Pectoral-qi Assessment Questionnaire of self-test technology. Interventions: The same group of 59 subjects keep wearing the instrument for hours and get the self-test data consequently. Outcomes: As one of comprehensive indicator establishing methods, PLS-SLVM highlights the structure state among variables and improves the evaluation efficiency. Furthermore, it provides a new tool and method in TCM diseases prevention and health security. Lessons: As expected, PLS-SLVM is a useful tool due to its nonassumption of normal distribution and independence with consideration of correlation among different variables. Thus PLS-SLVM can be applied in ordinal data from assessment questionnaire and continuous data about physicochemical indexes for the same group of people. It displays that PLS-SLVM builds a connection between TCM experts diagnosis and the self-testing technology.

NHL (non-Hodgkin lymphoma) consists of over 60 subtypes, ranging from slow-growing to very aggressive. The three largest subtypes are DLBCL (diffuse large B-cell lymphoma), FL (follicular lymphoma), and CLL/SLL (chronic lymphocytic leukemia/small lymphocytic lymphoma). For each subtype, different racial groups have different presentations, etiologies, and prognosis patterns.SEER (Surveillance, Epidemiology, and End Results) data on DLBCL, FL, and CLL/SLL patients diagnosed between 1992 and 2010 were analyzed. Racial groups studied included NHW (non-Hispanic whites), HW (Hispanic whites), blacks, and API (Asians and Pacific Islanders). Patient characteristics, age-adjusted incidence rate, and survival were compared across races. Stratification and multivariate analysis were conducted.There are significant racial differences for patients' characteristics, including gender, age at diagnosis, stage, lymph site, and age, and the patterns vary across subtypes. NHWs have the highest incidence rates for all three subtypes, followed by HWs (DLBCL and FL) and blacks (CLL/SLL). The dependence of the incidence rate on age and gender varies across subtypes. For all three subtypes, NHWs have the highest five-year relative survival rates, followed by HWs. When stratified by stage, racial difference is significant in multiple multivariate Cox regression analyses.Racial differences exist among DLBCL, FL, and CLL/SLL patients in the U.S. in terms of characteristics, incidence, and survival. The patterns vary across subtypes. More data collection and analysis are needed to more comprehensively describe and interpret the across-race and subtype differences.

Clustering algorithms are important methods widely used in mining data streams because of their abilities to deal with infinite data flows. Although these algorithms perform well to mining latent relationship in data streams, most of them suffer from loss of cluster purity and become unstable when the inputting data streams have too many noisy variables. In this article, we propose a clustering algorithm to cluster data streams with noisy variables. The result from simulation shows that our proposal method is better than previous studies by adding a process of variable selection as a component in clustering algorithms. The results of two experiments indicate that clustering data streams with the process of variable selection are more stable and have better purity than those without such process. Another experiment testing KDD-CUP99 dataset also shows that our algorithm can generate more stable result.

It is well known that statistical classifiers trained from imbalanced data lead to low true positive rates and select inconsistent significant variables. In this article, an improved method is proposed to enhance the classification accuracy for the minority class by differentiating misclassification cost for each group. The overall error rate is replaced by an alternative composite criterion. Furthermore, we propose an approach to estimate the tuning parameter, the composite criterion, and the cut-point simultaneously. Simulations show that the proposed method achieves a high true positive rate on prediction and a good performance on variable selection for both continuous and categorical predictors, even with highly imbalanced data. An illustrative example of the analysis of the suboptimal health state data in traditional Chinese medicine is discussed to show the reasonable application of the proposed method.

Background In China, the basic insurance system consists of three schemes: the UEBMI (Urban Employee Basic Medical Insurance), URBMI (Urban Resident Basic Medical Insurance), and NCMS (New Cooperative Medical Scheme), across which significant differences have been observed. Since 2009, the central government has been experimenting with consolidating these schemes in selected areas. This study examines whether differences still exist across schemes after the consolidation. Methods A survey was conducted in the city of Suzhou, collecting data on subjects 45 years old and above with at least one inpatient or outpatient treatment during a period of twelve months. Analysis on 583 subjects was performed comparing subjects’ characteristics across insurance schemes. A resampling-based method was applied to compute the predicted gross medical cost, OOP (out-of-pocket) cost, and insurance reimbursement rate. Results Subjects under different insurance schemes differ in multiple aspects. For inpatient treatments, subjects under the URBMI have the highest observed and predicted gross and OOP costs, while those under the UEBMI have the lowest. For outpatient treatments, subjects under the UEBMI and URBMI have comparable costs, while those under the NCMS have much lower costs. Subjects under the NCMS also have a much lower reimbursement rate. Conclusions Differences still exist across schemes in medical costs and insurance reimbursement rate post-consolidation. Further investigations are needed to identify the causes, and interventions are needed to eliminate such differences.

This article investigates the relative effects of percentage body fat (PBF) and visceral fat index (VFI) on the prevalence of hypertension and prehypertension, and evaluates if excess visceral deposition of body fat increased the risk of hypertension or prehypertension.A general population-based sample of men (n = 5297) and women (n = 6232), aged 35-64 years, was studied. Based on blood pressure (BP) levels, normotensive, prehypertensive, and hypertensive study participants were identified. Multivariate linear regression models with SBP or DBP as a dependent variable and multivariate ordinal logistic regression models with BP categories as a dependent variable were performed.In the normotensive, prehypertensive, and hypertensive participants, the mean (standard deviation) PBF was 27.83 (6.85), 28.91 (6.65), and 30.2 (6.73), respectively, whereas VFI was 6.97 (3.78), 8.89 (4.35), and 10.75 (5.05), respectively. After adjusting for age, smoking, alcohol consumption, education, regions (north or south), areas (urban or rural), diabetes, and family history, PBF and VFI (in separate models) showed a strong positive association with hypertension or prehypertension (P 0.0001). However, when both PBF and VFI were adjusted for in the same multivariate model, VFI but not PBF remained positively associated with hypertension and prehypertension for both genders (P 0.0001). A similar positive association was reported with the VFI/PBF ratio (P 0.0001).As excess visceral body fat is strongly associated with higher risk of hypertension and prehypertension, measurements of VFI or VFI/PBF ratio, in addition to PBF, will provide a better understanding of adiposity-related risks for hypertension and prehypertension.

With the continuous improvement of international "pharmacovigilance" technology and methods,it becomes the key part of the post-marketing evaluation. This issue is based on this research background, and also means to find out the Chinese medicine safety monitor which consistents with the reality. A common problem is that those who choose a career in pharmacovigilance know how the complex data presented to us are a source of both fascination and frustration. In the 70's, for the first time data mining technology in the international pharmacovigilance turn up, we try to establish new signal detection method to make contributes to post-marketing evaluation of Chinese medicine and establishment of registration. Building the national adverse reaction reporting database is widely used in western country. Nature of the problem is that pharmacovigilance issues can come through a lot of assumptions into the statistical problems, different assumptions are for different statistical tests. Through the traditional imbalance between the proportion of fourfold table for other assumptions, few countries use in practice, this does not involve evidence, but this issue provides the introduce of the principle. Methods include the ratio of the report of the Netherlands (ROR), the proportion of reports than the UK ratio (PRR),WHO's information points (IC), the U.S. Food and Drug Administration empirical Bayes (EBS), etc. Because there is no international gold standard of the signal detection method, at first we use the simulation comparing these four methods of data mining, From the point of specificity, the sample size demand, this issue views the advantages and disadvantages of four methods and application conditions,and from a technical point of view and try to propose a new signal detection method, for example, Hierarchical Bayesian.

To analyze the rules of medicinal groups that doctors treat angina pectoris using Danhong injection.Two thousands and seventy cases from hospital information system of 10 hospitals in Beijing 3a grade hospital were collected. Then use frequencies to find the most common drug and use association rules with apriori algorithm to analyze doctor's prescription compatibility rules curing coronary heart disease.The compatibility among aspirin, oral nitrate, oral dose of clopidogrel and oral dose of Xintong was the most frequently used medicinal groups with Danhong injection in real world HIS database.Danhong injection was used at treatment of coronary heart disease is often combination with similar pharmacological effects chemical drugs. Doctors should balance the benefits and the risks.

With the continuous increasement of adverse Chinese drug reaction reporting, especially Chinese traditional medicine injection, it becomes the key part of the post-marketing evaluation. The issue based on this research background becomes the hot research direction. But the truth is that the history of Chinese medicine and western medicine is different and the theory is not a system either, here different means between pharmacovigilance of Chinese medicine and western medicine, there are still many gaps. This becomes a barrier for Chinese medicine entering the international market. This article summarizes try to order the SFDA's adverse reaction reporting database of traditional Chinese medicine, and using three kinds of data mining methods used by WHO, the U. S. Food and Drug Administration, to establish suitable system for Chinese market after the evaluation of pharmacovigilance.

To analyze the clinical application of Shenmai injection and to learn the rule of it in the real word so that we can provide inferences to doctors.Cases using Shenmai injection in hospital information system from 10 grade A hospitals in Beijing were collected. Descriptive statistic methods were used to analyze the usage, dosage and medication time.For the clinical application of Shenmai injection, the average age of the patients using Shenmai injection was about 64.68, but most of them came from cardiovascular diseases department Shenmai injection was usually used to treat coronary disease in Western medicine and Xiongbi (Chinese medicine syndrome name) in traditional Chinese medicine. The course of treatment was from one day to seven days, which was in accord with the drug instruction as well as the dosage.Shenmai injection was usually used for the old people. In clinical practice, the use of Shenmai injection was basically in accord with the drug instruction by doctors. However, there are still some of the doctors who did not follow the instruction and should be careful of this.

To know how Danhong injection is used in clinical practice and to provide a reference for guiding clinical use of Danhong Injection.Extract Danhong injection's post-marketing re-evaluation data from the Hospital Information System of ten three grade III-A General Hospitals in Beijing, use basic statistical analysis methods to analyze Danhong injection's indications, usage and dosage, days of treatment etc. in clinical practice.In patients using Danhong injection, there were more than 60 percent patients were prescribed based on main-diagnosis, first-visit and other diagnosis, which were also coincided with Danhong injection's instruction. In clinical practice, 95.5 percent of Danhong injection's administration routes conformed to the instruction and more than 90 percent of Danhong injection's dosage were within the limits prescribed by the instruction. Danhong injection was used less than 20 days successively. Danhong injection was used in combination with drugs with the action of removing blood stasis, antianginal drug, antiplatelet drug, drugs for cerebrovascular disease and so on.Danhong injection has been used according to instruction in practice almostly.

Background. We illustrated an example of structure equation modelling (SEM) in the research on SHS to explore the diagnosis of the Sub optimal health status (SHS) and provide evidence for the standardization of traditional Chinese medicine (TCM) patterns in SHS. And the diagnosis of 4 TCM patterns in SHS was evaluated in this analysis.Methods. This study assessed data on 2807 adults (aged 18 to 49) with SHS from 6 clinical centres. SEM was used to analyze the patterns of SHS in TCM. Parameters in the introduced model were estimated by the maximum likelihood method.Results. The discussed model fits the SHS data well with CFI = 0.851 and RMSEA = 0.075. The direct effect of Qi deficiency pattern on dampness pattern had the highest magnitude (value of estimate is 0.822). With regard to the construct of “Qi deficiency pattern”, “fire pattern”, “stagnation pattern” and “dampness pattern”, the indicators with the highest load were myasthenia of limbs, vexation, deprementia, and dizziness, respectively. It had been shown that estimate factor should indicate the important degree of different symptoms in pattern.Conclusions. The weights of symptoms in the respective pattern can be statistical significant and theoretical meaningful for the 4 TCM patterns identification in SHS research. The study contributed to a theoretical framework, which has implications for the diagnosis points of SHS.

With the continuous increasement of adverse Chinese drug reaction reporting, especially Chinese traditional medicine injection, it becomes the key part of the post-marketing evaluation. The issue based on this research background becomes the hot research direction. But the truth is that the history of Chinese medicine and western medicine is different and the theory is not a system either, here different means between pharmacovigilance of Chinese medicine and western medicine, there are still many gaps. This becomes a barrier for Chinese medicine entering the international market. This article summarizes try to order the SFDA's adverse reaction reporting database of traditional Chinese medicine, and using three kinds of data mining methods used by WHO, the U. S. Food and Drug Administration, to establish suitable system for Chinese market after the evaluation of pharmacovigilance.

With the continuous improvement of international "pharmacovigilance" technology and methods,it becomes the key part of the post-marketing evaluation. This issue is based on this research background, and also means to find out the Chinese medicine safety monitor which consistents with the reality. A common problem is that those who choose a career in pharmacovigilance know how the complex data presented to us are a source of both fascination and frustration. In the 70's, for the first time data mining technology in the international pharmacovigilance turn up, we try to establish new signal detection method to make contributes to post-marketing evaluation of Chinese medicine and establishment of registration. Building the national adverse reaction reporting database is widely used in western country. Nature of the problem is that pharmacovigilance issues can come through a lot of assumptions into the statistical problems, different assumptions are for different statistical tests. Through the traditional imbalance between the proportion of fourfold table for other assumptions, few countries use in practice, this does not involve evidence, but this issue provides the introduce of the principle. Methods include the ratio of the report of the Netherlands (ROR), the proportion of reports than the UK ratio (PRR),WHO's information points (IC), the U.S. Food and Drug Administration empirical Bayes (EBS), etc. Because there is no international gold standard of the signal detection method, at first we use the simulation comparing these four methods of data mining, From the point of specificity, the sample size demand, this issue views the advantages and disadvantages of four methods and application conditions,and from a technical point of view and try to propose a new signal detection method, for example, Hierarchical Bayesian.

To find effective estimations of tail dependence, we present the estimators of upper tail dependence coefficient by using survival copula. We research to two problems by using the samples from t-copula. Firstly, do the estimators estimate effectively the upper tail dependence coefficients of copula? Which is the best among the estimators? Secondly, if sample isn't from true distribution, do the estimators have model risk? Simulation shows that the estimators can estimate effectively the tail dependence coefficients of t-copula. By comparison of estimators, a better estimator is found out among the estimators. Therefore if sample is from true distribution, the estimators can estimate effectively upper tail dependence coefficients. However, if sample isn't from underlying copula, the estimators have model risk.

In order to find effective methods measuring stock market risk, Shanghai Stock Indices are observed by positive news and negative news. The impact of news on return volatility is researched by heteroskedasticity models. Empirical research shows that under circumstance of positive news, bad news only reduces current return volatility, while the relationship between return and variance is not notable. Under the circumstance of negative news, bad news results in volatility growth, the relationship between return and variance isn't also notable. By comparing, we find out that there is large difference between values at risk under different circumstances. Finally we propose the method estimating by hierarchical level. Simulation results show that it is effective.

For the sake of finding the portfolios with low risk and high return, copula function is used to compute the tail dependence coefficient. We present the investment ratio function of value-at-risk of portfolio, and use the tail dependence coefficient to research value-at-risk of portfolio. We propose to use the curve of portfolio value and the curve of portfolio value-at-risk to analyze investment ratio. Empirical research shows that according to the curve of portfolio value and the curve of portfolio value-at-risk to analyze investment ratio, the portfolio with low risk and high return can be found out.

The syndrome is one of the most important concepts and ingredients in the theoretical and clinical research of traditional Chinese medicine (TCM). TCM doctors believe that all diseases are caused by an imbalance in the patient's body, which is called syndrome. All the therapies and formulas in TCM are decided according to the patients' syndrome situation. To quantitatively evaluate the level of syndrome, many statistical methodologies have been discussed in recent years. In this article, we introduce a second-order latent variable model to evaluate the level of patients' syndrome with many clinical symptoms. An objective evaluation score can be easily derived by the proposed model, with a high speed of convergence and without joint-distribution assumption. We illustrate the application of this model by an analysis of premenstrual disorder syndrome of liver-qi invasion syndrome evaluation research.

Background Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them. Results We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives. Conclusions The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.

To measure effectively the risk of stock market, the algorithm of expected shortfall is presented by using the extreme-value block minimum method. By transforming the distribution of standardized minimal return in an interval into the distribution of ordinary minimal return, the formula of expected shortfall is derived. By simulation and statistical analysis, an appropriate interval length is found out to make this algorithm robust. The simulation results show that the robustness of value at risk and expected shortfall based on this method is very good when the interval length isn’t more than 30. This algorithm measures effectively the expected shortfall of stock market.

ObjectiveTo investigate biological indicators of sub-optimal health status and provide means of objective assessment of sub-optimal health status.MethodsWe set the unified standards for diagnosing a SHS. We tested various laboratory indicators in 407 cases that we selected randomly from 2807 subjects and collected 15 mL of fasting venous blood from each case. We measured serum immunoglobulin A (IgA) and immunoglobulin G (IgG) concentrations, serum beta endorphins (β-EP), cortisol (C), testosterone (T), plasma adrenocorticotropic hormone (ACTH) and serum T lymphocyte subsets CD3+ and CD4+.ResultsMean serum testosterone concentrations and their ratio to cortisol (C) concentrations were significantly higher in the healthy group than in those with sub-optimal health status (P