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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective To explore whether early treatment with integrated Chinese and western medicine can prolong the survival time of patients with advanced digestive tract malignant tumors using a new statistical analysis method and real-world data. Methods Based on the left truncated data, the Copula model was used to construct a joint model of the time from stage IV diagnosis to treatment and the time to overall survival. The estimated value and significance of the Copula dependence parameter, η, were obtained to evaluate the dependence strength between the two event times of interest. Results The numerical simulation showed that this method performed well when the left truncation rate was 20%. The estimated Copula dependence parameter η), in the real data analysis was 0.279 (P<0.05), indicating that there was a significant correlation between times to treatment and overall survival. Gender, the Eastern Cooperative Oncology Group score, targeted treatment, and metastatic site 1 were significant factors associated with overall survival. Conclusion The new statistical method provided valuable insights and analytical tools for the effectiveness of integrated traditional Chinese and western medicine in treating patients with advanced digestive tract malignant tumors. [ABSTRACT FROM AUTHOR]

Objective To explore the prognostic factors that affect patients with advanced esophageal cancer treated with Traditional Chinese medicine (TCM). Methods A total of 72 patients with stage IV esophageal cancer were selected for analysis, and the advantaged and disadvantaged groups of TCM treatment were defined according to survival time. The log-rank method was used for the univariate analysis, the rank-sum test was used for the correlation between the syndrome type and the prognostic grouping, and the Cox proportional risk model was used for multiple factors. Results The median survival of the 72 patients with esophageal cancer was 17.9 months. The univariate analysis of the result showed that the presence of organ metastasis, the presence of lung metastasis, and the post-treatment symptom self-assessment and length of herbal medication were related to the prognosis of advanced esophageal cancer treated with TCM. The rank-sum test between the TCM syndrome elements, and survival prognosis grouping showed that the syndrome element of Qi stagnation may also affect prognosis. The result of the multi-factorial analysis showed that the factor of the duration of the TCM administration was an independent factor that affects the prognosis of advanced esophageal cancer treated with TCM. Conclusion The duration of TCM treatment affected the prognosis of patients with advanced esophageal cancer. [ABSTRACT FROM AUTHOR]

In this article, we introduce the L1 regularized support vector machine (L1-SVM) as an effective feature selection technique into the modeling of financial early warning system (EWS), for the purpose of establishing compact financial EWSs for Chinese-listed companies. By introducing LASSO penalty into the SVM framework, the L1-SVM is a capable methodology to select causative features in classification problem. We evaluate the feature selection performance of L1-SVM under different circumstances through numerical simulations and find it suitable for selecting features for financial EWS. In the real study, we establish four financial EWSs with features selected by L1-SVM and compare them with those trained with full features. The empirical result illustrates that our EWSs, with only minority of features, outperform significantly than full ones in the respect of generalization performance, which indicates the feasibility of L1-SVM in real applications. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To improve the gelling property of konjac gum (KGM) and enhance the application value of Abelmoschus manihot (L.) medic gum (AMG), a novel type of gel was prepared using KGM and AMG in this study. The effects of AMG content, heating temperature and salt ions on the characteristics of KGM/AMG composite gels were studied by Fourier transform infrared spectroscopy (FTIR), zeta potential, texture analysis and dynamic rheological behavior analysis. The results indicated that the AMG content, heating temperature and salt ions could affect the gel strength of KGM/AMG composite gels. Hardness, springiness, resilience, G′, G* and η* of KGM/AMG composite gels increased when AMG content increased from 0 to 2.0 %, but they decreased when AMG increased from 2.0 % to 3.5 %. High-temperature treatment significantly enhanced the texture and rheological properties of KGM/AMG composite gels. The addition of salt ions reduced the zeta potential absolute value and weakened the texture and rheological properties of KGM/AMG composite gels. Furthermore, the KGM/AMG composite gels could be classified as non-covalent gels. The non-covalent linkages included hydrogen bonding and electrostatic interactions. These findings would help understand the properties and formation mechanism of KGM/AMG composite gels and help improve the application value of KGM and AMG. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]