An adaptive gBOIN design with shrinkage boundaries for phase I dose-finding trials

Background With the emergence of molecularly targeted agents and immunotherapies, the landscape of phase I trials in oncology has been changed. Though these new therapeutic agents are very likely induce multiple low- or moderate-grade toxicities instead of DLT, most of the existing phase I trial designs account for the binary toxicity outcomes. Motivated by a pediatric phase I trial of solid tumor with a continuous outcome, we propose an adaptive generalized Bayesian optimal interval design with shrinkage boundaries, gBOINS, which can account for continuous, toxicity grades endpoints and regard the conventional binary endpoint as a special case. Result The proposed gBOINS design enjoys convergence properties, e.g., the induced interval shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose with increased sample size. Conclusion The proposed gBOINS design is transparent and simple to implement. We show that the gBOINS design has the desirable finite property of coherence and large-sample property of consistency. Numerical studies show that the proposed gBOINS design yields good performance and is comparable with or superior to the competing design.

Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model

This paper provides a new insight into an economical and effective sampling design method relying on the outcome-dependent sampling (ODS) design in large-scale cohort research. Firstly, the importance and originality of this paper is that it explores how to fit the covariate-adjusted additive Hazard model under the ODS design; secondly, this paper focused on estimating the distortion function through nonparametric regression and required observation of the covariate on the confounding factors of distortion; moreover, this paper further calibrated the contaminated covariates and proposed the estimators of the parameters by analyzing the calibrated covariates; finally, this paper established the large sample property and asymptotic normality of the proposed estimators and conducted many more simulations to evaluate the finite sample performance of the proposed method. Empirical research demonstrates that the results from both artificial and real data verified good performance and practicality of the proposed ODS method in this paper.

Towards the high-throughput synthesis of bulk materials: thermoelectric PbTe–PbSe–SnTe–SnSe alloys

Despite extensive research, much of PbSnTeSe alloying space is unexplored. High-throughput bulk synthesis augments literature with high-resolution (121 sample) property maps.

Input Impedance Adjustment and Property Optimization for Microwave Absorbing Slabs

It is generally believed that materials with high imaginary permeability and high imaginary permittivity are effective absorbers. For specific samples, however, the adjustment of input impedance is still necessary for the sample property optimization. By analyzing the relation between the diffraction rate and the input impedance, a method of absorption maximization for slab sample are presented in this sample. Medium choosing and volume rate controlling is the key of this method.

Spectral Methods in Spatial Statistics

When the spatial location area increases becoming extremely large, it is very difficult, if not possible, to evaluate the covariance matrix determined by the set of location distance even for gridded stationary Gaussian process. To alleviate the numerical challenges, we construct a nonparametric estimator called periodogram of spatial version to represent the sample property in frequency domain, because periodogram requires less computational operation by fast Fourier transform algorithm. Under some regularity conditions on the process, we investigate the asymptotic unbiasedness property of periodogram as estimator of the spectral density function and achieve the convergence rate.

Test of Multiple Breaks in Long Memory Process: An Unknown Mean Breaks Case

This paper extends Bai and Perron (1998) multi-unknown breaks method to long memory process, analyzes the finite sample property of the method (mean, variance, the test performance of Sup-F statistics) and the relation with the fractional integration parameter, size and location of break point. One and two unknown mean break and various long memory situations are considered. It found that expect for the condition that fractional integrated parameter d approaches to 0.5, BP method is fairly exact to estimate the break point, the bias is relatively small and the power and size of the Sup-F test is acceptable. Especially when d is negative, this method shows outstanding statistical property, small bias and standard deviation, and perfect test power and size.

Multi-Unknown Breaks Estimates in Presence of Fractional White Noise Disturbance

This paper analyzes the distribution of breaks estimators and finite sample property of the method based on different mean break and long memory situations based on Bai and Perron (1998) multi-unknown breaks method. As to the distribution of the multiple break estimates, the property of the estimates under the alternative hypothesis is better when d is close to-0.5 that the bias and variance are smaller and the distribution are smoother even better than the BP (1998) case. Further work can be made on the limit distribution of break number test statistics in the case of d (-0.5, 0), d (0, 0.5) and to the case of three and more mean, trend breaks.