Search for: All records

Abstract Stemming from the high-profile publication of Nissen and Wolski (N Engl J Med 356:2457–2471, 2007) and subsequent discussions with divergent views on how to handle observed zero-total-event studies, defined to be studies that observe zero number of event in both treatment and control arms, the research topic concerning the common odds ratio model with zero-total-event studies remains to be an unresolved problem in meta-analysis. In this article, we address this problem by proposing a novel repro samples method to handle zero-total-event studies and make inference for the common odds ratio. The development explicitly accounts for the sampling scheme that generates the observed data and does not rely on any large sample approximations. It is theoretically justified with a guaranteed finite-sample performance. Simulation studies are designed to demonstrate the empirical performance of the proposed method. It shows that the proposed confidence set, although a little conservative, achieves the desired empirical coverage rate in all situations. The development also shows that the zero-total-event studies contain meaningful information and impact the inference for the common odds ratio. The proposed method is used to perform a meta-analysis of the 48 trials reported in Nissen and Wolski (N Engl J Med 356:2457–2471, 2007) as well 

Biometric authentication systems are increasingly needed across a broad range of applications including in smart city environments (e.g., entering hotels), and in smart home environments (e.g., controlling smart devices). Traditional methods, such as face-based and fingerprint-based authentication, usually incur high costs to be installed in all this kind of environments. In this paper, we develop a ubiquitous low-effort user authentication approach, mmPalm, based on palm recognition using millimeter wave (mmWave) signals. mmWave technology has been adopted by WiGig and 5G, making mmPalm a low-cost solution that can be widely adopted in public places. In addition, the high resolution of mmWave signals allows mmPalm to extract detailed palm characteristics (e.g., palm geometry, skin thickness, and texture) that can assemble distinctive palmprints for user authentication. Our innovative virtual antennas design further increases the spatial resolution of a commercial mmWave device, enabling it to fully capture the comprehensive palmprint features. Moreover, to address the challenge of small-scale environmental changes (e.g., variations in palm-device distances and palm orientations), we design a novel palm profile augmentation method, utilizing a Conditional Generative Adversarial Network (cGAN) to generate synthetic palm profiles for mitigating palm instability. Furthermore, we design a cross-environment adaptation framework based on transfer learning to address the challenge of large-scale environmental changes, including multipath variations introduced by human bodies and nearby furniture. Extensive experiments with 30 participants through 6 months demonstrate that mmPalm achieves 99% authentication accuracy with resilience against different types of attacks, including random, impersonation, and counterfeit. 

Generalized linear mixed models are commonly used to describe relationships between correlated responses and covariates in medical research. In this paper, we propose a simple and easily implementable regularized estimation approach to select both fixed and random effects in generalized linear mixed model. Specifically, we propose to construct and optimize the objective functions using the confidence distributions of model parameters, as opposed to using the observed data likelihood functions, to perform effect selections. Two estimation methods are developed. The first one is to use the joint confidence distribution of model parameters to perform simultaneous fixed and random effect selections. The second method is to use the marginal confidence distributions of model parameters to perform the selections of fixed and random effects separately. With a proper choice of regularization parameters in the adaptive LASSO framework, we show the consistency and oracle properties of the proposed regularized estimators. Simulation studies have been conducted to assess the performance of the proposed estimators and demonstrate computational efficiency. Our method has also been applied to two longitudinal cancer studies to identify demographic and clinical factors associated with patient health outcomes after cancer therapies.