Guosheng Yin

Bio: Guosheng Yin is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Estimator & Bayesian probability. The author has an hindex of 35, co-authored 192 publications receiving 4141 citations. Previous affiliations of Guosheng Yin include University of Texas Health Science Center at Houston & University of North Carolina at Chapel Hill.

Outlier detection for high-dimensional data

Abstract: Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data.

Bayesian Model Averaging Continual Reassessment Method in Phase I Clinical Trials

Abstract: The continual reassessment method (CRM) is a popular dose-finding design for phase I clinical trials. This method requires that practitioners prespecify the toxicity probability at each dose. Such prespecification can be arbitrary, and different specifications of toxicity probabilities may lead to very different design properties. To overcome the arbitrariness and further enhance the robustness of the design, we propose using multiple parallel CRM models, each with a different set of prespecified toxicity probabilities. In the Bayesian paradigm, we assign a discrete probability mass to each CRM model as the prior model probability. The posterior probabilities of toxicity can be estimated by the Bayesian model averaging (BMA) approach. Dose escalation or deescalation is determined by comparing the target toxicity rate and the BMA estimates of the dose toxicity probabilities. We examine the properties of the BMA-CRM approach through extensive simulation studies, and also compare this new method and its vari...

Root canal filled versus non-root canal filled teeth: a retrospective comparison of survival times.

Abstract: OBJECTIVE This matched cohort study used data from a large dental HMO in the Pacific Northwest to evaluate the degree to which pulpal involvement and subsequent endodontic therapy affects tooth survival. Root canal filled (RCF) teeth were used as an indicator of pulpal involvement. Our hypothesis was that RCF teeth would be extracted sooner than non-RCF teeth matched within subjects, controlling for tooth-level variables of interest. METHODS The HMO's treatment databases and a subsequent chart audit were used to identify 202 eligible subjects, each of whom had one tooth endodontically treated in 1987-88 and a similar contralateral tooth that was non-RCF at that time. Both teeth were followed from the endodontic access date through the extraction date, the endodontic access date (for initially non-RCF teeth), or 12/31/94, whichever was earliest. Time-to-event analyses were carried out, with Kaplan-Meier curves generated and multivariable marginal proportional hazards regression models fitted to describe the effect of RCF status on tooth survival. All statistical analyses accounted for the complex sampling strategy used in generating the dataset. RESULTS Teeth were followed for up to eight (median = 6.7) years. RCF teeth had substantially worse survival than their non-RCF counterparts (p < 0.001), with a greater effect of RCF status evident among molars than non-molars. Adjusted hazard ratios (95% confidence intervals) for loss of RCF versus non-RCF molars and non-molars were 7.4 (3.2-15.1) and 1.8 (0.7-4.6), respectively. CONCLUSION Though endodontic therapy can prolong tooth survival, pulpal involvement still may hasten tooth loss, underscoring the importance of caries prevention and prompt restorative care.

Bayesian dose finding in oncology for drug combinations by copula regression

Abstract: Summary. Treating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in single-agent trials, which naturally offers rich prior information. We propose a Bayesian adaptive design for dose finding that is based on a copula-type model to account for the synergistic effect of two or more drugs in combination. To search for the maximum tolerated dose combination, we continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying at the same doses is determined by comparing the posterior estimates of the probabilities of toxicity of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios.

Cure rate models: A unified approach

Abstract: The authors propose a novel class of cure rate models for right-censored failure time data. The class is formulated through a transformation on the unknown population survival function. It includes the mixture cure model and the promotion time cure model as two special cases. The authors propose a general form of the covariate structure which automatically satisfies an inherent parameter constraint and includes the corresponding binomial and exponential covariate structures in the two main formulations of cure mod els. The proposed class provides a natural link between the mixture and the promotion time cure models, and it offers a wide variety of new modelling structures as well. Within the Bayesian paradigm, a Markov chain Monte Carlo computational scheme is implemented for sampling from the full conditional distribu tions of the parameters. Model selection is based on the conditional predictive ordinate criterion. The use of the new class of models is illustrated with a set of real data involving a melanoma clinical trial.

Convergence of Probability Measures

Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

EMT, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer

Abstract: Tumors are cellularly and molecularly heterogeneous, with subsets of undifferentiated cancer cells exhibiting stem cell-like features (CSCs). Epithelial to mesenchymal transitions (EMT) are transdifferentiation programs that are required for tissue morphogenesis during embryonic development. The EMT process can be regulated by a diverse array of cytokines and growth factors, such as transforming growth factor (TGF)-β, whose activities are dysregulated during malignant tumor progression. Thus, EMT induction in cancer cells results in the acquisition of invasive and metastatic properties. Recent reports indicate that the emergence of CSCs occurs in part as a result of EMT, for example, through cues from tumor stromal components. Recent evidence now indicates that EMT of tumor cells not only causes increased metastasis, but also contributes to drug resistance. In this review, we will provide potential mechanistic explanations for the association between EMT induction and the emergence of CSCs. We will also highlight recent studies implicating the function of TGF-β-regulated noncoding RNAs in driving EMT and promoting CSC self-renewal. Finally we will discuss how EMT and CSCs may contribute to drug resistance, as well as therapeutic strategies to overcome this clinically.