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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 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]