Guosheng Yin

HER Family Receptor Abnormalities in Lung Cancer Brain Metastases and Corresponding Primary Tumors

Purpose: To compare the characteristics of deregulation of HER receptors and their ligands between primary tumor and corresponding brain metastases of non–small cell lung carcinoma (NSCLC). Experimental Design: Fifty-five NSCLC primary tumors and corresponding brain metastases specimens were examined for the immunohistochemical expression of epidermal growth factor receptor (EGFR), phosphorylated EGFR, Her2, Her3, and phosphorylated Her3, and their ligands EGF, transforming growth factor-α, amphiregulin, epiregulin, betacellulin, heparin-binding EGFR-like growth factor, neuregulin (NRG) 1, and NRG2. Analysis of EGFR copy number using fluorescence in situ hybridization and mutation by PCR-based sequencing was also done. Results: Metastases showed significantly higher immunohistochemical expression of EGF (membrane: brain metastases 66.0 versus primary tumors 48.5; P = 0.027; nucleus: brain metastases 92.2 versus 67.4; P = 0.008), amphiregulin (nucleus: brain metastases 53.7 versus primary tumors 33.7; P = 0.019), phosphorylated EGFR (membrane: brain metastases 161.5 versus primary tumors 76.0; P < 0.0001; cytoplasm: brain metastases 101.5 versus primary tumors 55.9; P = 0.014), and phosphorylated Her3 (membrane: brain metastases 25.0 versus primary tumors 3.7; P = 0.001) than primary tumors did. Primary tumors showed significantly higher expression of cytoplasmic transforming growth factor-α(primary tumors 149.8 versus brain metastases 111.3; P = 0.008) and NRG1 (primary tumors 158.5 versus brain metastases 122.8; P = 0.006). In adenocarcinomas, a similar high frequency of EGFR copy number gain (high polysomy and amplification) was detected in primary (65%) and brain metastasis (63%) sites. However, adenocarcinoma metastases (30%) showed higher frequency of EGFR amplification than corresponding primary tumors (10%). Patients whose primary tumors showed EGFR amplification tended to develop brain metastases at an earlier time point. Conclusions: Our findings suggest that NSCLC brain metastases have some significant differences in HER family receptor–related abnormalities from primary lung tumors.

Phase 3 study comparing the use of docetaxel on an every-3-week versus weekly schedule in the treatment of metastatic breast cancer†

Previous studies have evaluated 3‐week and weekly docetaxel schedules in patients with metastatic breast cancer (MBC). The varying efficacy results and toxicity profiles noted in these earlier studies led to a comparison of the schedules to determine which was safer and more efficacious.

Bayesian Model Averaging Continual Reassessment Method in Phase I Clinical Trials

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 variants with the original CRM. The results demonstrate that our BMA-CRM is competitive and robust, and eliminates the arbitrariness of the prespecification of toxicity probabilities.

Semiparametric Transformation Models for Survival Data With a Cure Fraction

We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic variances attain the semiparametric efficiency bound. Simulation studies are conducted to examine the finite-sample properties of the proposed estimators. The method is illustrated on data from a clinical trial involving the treatment of melanoma.

A latent contingency table approach to dose finding for combinations of two agents.

Two-agent combination trials have recently attracted enormous attention in oncology research. There are several strong motivations for combining different agents in a treatment: to induce the synergistic treatment effect, to increase the dose intensity with nonoverlapping toxicities, and to target different tumor cell susceptibilities. To accommodate this growing trend in clinical trials, we propose a Bayesian adaptive design for dose finding based on latent 2 x 2 tables. In the search for the maximum tolerated dose combination, we continuously update the posterior estimates for the unknown parameters associated with marginal probabilities and the correlation parameter based on the data from successive patients. By reordering the dose toxicity probabilities in the two-dimensional space, we assign each coming cohort of patients to the most appropriate dose combination. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. Finally, we illustrate our dose-finding procedure with a clinical trial of agent combinations at M. D. Anderson Cancer Center.

Bayesian quantile regression for longitudinal studies with nonignorable missing data.

We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a l(2) penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial.

Sequential continual reassessment method for two‐dimensional dose finding

It is common to encounter two-dimensional dose finding in phase I trials, for example, in trials combining multiple drugs, or in single-agent trials that simultaneously search for the maximum tolerated dose (MTD) and the optimal treatment schedule. In these cases, the traditional single-agent dose-finding methods are not directly applicable. We propose a simple and adaptive two-dimensional dose-finding design that can accommodate any type of single-agent dose-finding method. In particular, we convert the two-dimensional dose-finding trial to a series of one-dimensional dose-finding subtrials along shortened line search segments by fixing the dose level of one drug. We then conduct the subtrials sequentially. Based on the MTD obtained from the completed one-dimensional trial, we eliminate the doses that lie outside of the search range based on the partial order, and thereby efficiently shrink the two-dimensional dose-finding space. The proposed design dramatically reduces the sample size and still maintains good performance. We illustrate the design through extensive simulation studies motivated by clinical trials evaluating multiple drugs or dose and schedule combinations.

Bayesian generalized method of moments

We propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are di-cult. By de- riving the moments and concatenating them together, we build up a weighted quadratic objective function in the GMM framework. As in a normal density function, we take the negative GMM quadratic function divided by two and ex- ponentiate it to substitute for the usual likelihood. After specifying the prior dis- tributions, we apply the Markov chain Monte Carlo procedure to sample from the posterior distribution. We carry out simulation studies to examine the proposed Bayesian GMM procedure, and illustrate it with a real data example.

Clinical Trial Design: Bayesian and Frequentist Adaptive Methods

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Maximum Likelihood Estimation for the Proportional Odds Model With Random Effects

In this article we study the semiparametric proportional odds model with random effects for correlated, right-censored failure time data. We establish that the maximum likelihood estimators for the parameters of this model are consistent and asymptotically Gaussian. Furthermore, the limiting variances achieve the semiparametric efficiency bounds and can be consistently estimated. Simulation studies show that the asymptotic approximations are accurate for practical sample sizes and that the efficiency gains of the proposed estimators over those of Cai, Cheng, and Wei can be substantial. A real example is provided to illustrate the proposed methods.