Biometrics最新文献

Although the Cox proportional hazards (PH) model is well established and extensively used in the analysis of survival data, the PH assumption may not always hold in practical scenarios. The class of semiparametric transformation models extends the Cox model and also includes many other survival models as special cases. This paper introduces a deep partially linear transformation model as a general and flexible regression framework for right-censored data. The proposed method is capable of avoiding the curse of dimensionality while still retaining the interpretability of some covariates of interest. We derive the overall convergence rate of the maximum likelihood estimators, the minimax lower bound of the nonparametric deep neural network estimator, and the asymptotic normality and the semiparametric efficiency of the parametric estimator. Comprehensive simulation studies demonstrate the impressive performance of the proposed estimation procedure in terms of both the estimation accuracy and the predictive power, which is further validated by an application to a real-world dataset.

The US Food and Drug Administration (FDA) launched Project Optimus to shift the objective of dose selection from the maximum tolerated dose to the optimal biological dose (OBD), optimizing the benefit-risk tradeoff. One approach recommended by the FDA's guidance is to conduct randomized trials comparing multiple doses. In this paper, using the selection design framework, we propose a Randomized Optimal SElection (ROSE) design, which minimizes sample size while ensuring the probability of correct selection of the OBD at pre-specified accuracy levels. The ROSE design is simple to implement, involving a straightforward comparison of the difference in response rates between two dose arms against a predetermined decision boundary. We further consider a two-stage ROSE design that allows for early selection of the OBD at the interim when there is sufficient evidence, further reducing the sample size. Simulation studies demonstrate that the ROSE design exhibits desirable operating characteristics in correctly identifying the OBD. A sample size of 15-40 patients per dosage arm typically results in a percentage of correct selection of the optimal dose ranging from 60% to 70%.

We develop here a semiparametric Gaussian Mixture Model (SGMM) for unsupervised learning with valuable spatial information taken into consideration. Specifically, we assume for each instance a random location. Then, conditional on this random location, we assume for the feature vector a standard Gaussian Mixture Model (GMM). The proposed SGMM allows the mixing probability to be nonparametrically related to the spatial location. Compared with a classical GMM, SGMM is considerably more flexible and allows the instances from the same class to be spatially clustered. To estimate the SGMM, novel EM algorithms are developed and rigorous asymptotic theories are established. Extensive numerical simulations are conducted to demonstrate our finite sample performance. For a real application, we apply our SGMM method to the CAMELYON16 dataset of whole-slide images for breast cancer detection. The SGMM method demonstrates outstanding clustering performance.

There has been substantial progress in predictive modeling for cognitive impairment in neurodegenerative disorders such as Alzheimer's disease (AD), based on neuroimaging biomarkers. However, existing approaches typically do not incorporate heterogeneity that may potentially arise due to interactions between the spatially varying imaging features and supplementary demographic, clinical and genetic risk factors in AD. Unfortunately, ignoring such heterogeneity may potentially result in poor prediction and biased estimation. Building on existing scalar-on-image regression framework, we address this issue by incorporating spatially varying interactions between brain image and supplementary risk factors to model cognitive impairment in AD. The proposed Bayesian method tackles spatial interactions via hierarchical representation for the functional regression coefficients depending on supplementary risk factors, which is embedded in a scalar-on-function framework involving a multi-resolution wavelet decomposition. To address the curse of dimensionality, we induce simultaneous sparsity and clustering via a spike and slab mixture prior, where the slab component is characterized by a latent class distribution. We develop an efficient Markov chain Monte Carlo algorithm for posterior computation. Extensive simulations and application to the longitudinal Alzheimer's Disease Neuroimaging Initiative study illustrate significantly improved prediction of cognitive impairment in AD across multiple visits by our model in comparison with alternate approaches. The proposed approach also identifies key brain regions in AD that exhibit significant association with cognitive abilities, either directly or through interactions with risk factors.

High-dimensional error-prone survival data are prevalent in biomedical studies, where numerous clinical or genetic variables are collected for risk assessment. The presence of measurement errors in covariates complicates parameter estimation and variable selection, leading to non-convex optimization challenges. We propose an error-in-variables additive hazards regression model for high-dimensional noisy survival data. By employing the nearest positive semi-definite matrix projection, we develop a fast Lasso approach (semi-definite projection Lasso, SPLasso) and its soft thresholding variant (SPLasso-T), both with theoretical guarantees. Under mild assumptions, we establish model selection consistency, oracle inequalities, and limiting distributions for these methods. Simulation studies and two real data applications demonstrate the methods' superior efficiency in handling high-dimensional data, particularly showcasing remarkable performance in scenarios with missing values, highlighting their robustness and practical utility in complex biomedical settings.

Latent factor models that integrate data from multiple sources/studies or modalities have garnered considerable attention across various disciplines. However, existing methods predominantly focus either on multi-study integration or multi-modality integration, rendering them insufficient for analyzing the diverse modalities measured across multiple studies. To address this limitation and cater to practical needs, we introduce a high-dimensional generalized factor model that seamlessly integrates multi-modality data from multiple studies, while also accommodating additional covariates. We conduct a thorough investigation of the identifiability conditions to enhance the model's interpretability. To tackle the complexity of high-dimensional nonlinear integration caused by 4 large latent random matrices, we utilize a variational lower bound to approximate the observed log-likelihood by employing a variational posterior distribution. By profiling the variational parameters, we establish the asymptotical properties of estimators for model parameters using M-estimation theory. Furthermore, we devise a computationally efficient variational expectation maximization (EM) algorithm to execute the estimation process and a criterion to determine the optimal number of both study-shared and study-specific factors. Extensive simulation studies and a real-world application show that the proposed method significantly outperforms existing methods in terms of estimation accuracy and computational efficiency.

An optimal experimental design is a structured data collection plan aimed at maximizing the amount of information gathered. Determining an optimal experimental design, however, relies on the assumption that a predetermined model structure, relating the response and covariates, is known a priori. In practical scenarios, such as dose-response modeling, the form of the model representing the "true" relationship is frequently unknown, although there exists a finite set or pool of potential alternative models. Designing experiments based on a single model from this set may lead to inefficiency or inadequacy if the "true" model differs from that assumed when calculating the design. One approach to minimize the impact of the uncertainty in the model on the experimental plan is known as model robust design. In this context, we systematically address the challenge of finding approximate optimal model robust experimental designs. Our focus is on locally optimal designs, so allowing some of the models in the pool to be nonlinear. We present three Semidefinite Programming-based formulations, each aligned with one of the classes of model robustness criteria introduced by Läuter. These formulations exploit the semidefinite representability of the robustness criteria, leading to the representation of the robust problem as a semidefinite program. To ensure comparability of information measures across various models, we employ standardized designs. To illustrate the application of our approach, we consider a dose-response study where, initially, seven models were postulated as potential candidates to describe the dose-response relationship.

In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case of model misspecification. In this work, we challenge such a claim and show that additional unlabeled samples are beneficial in high-dimensional settings. Initially focusing on a dense scenario, we introduce robust semi-supervised estimators for the regression coefficient without relying on sparse structures in the population slope. Even when the true underlying model is linear, we show that leveraging information from large-scale unlabeled data helps reduce estimation bias, thereby improving both estimation accuracy and inference robustness. Moreover, we propose semi-supervised methods with further enhanced efficiency in scenarios with a sparse linear slope. The performance of the proposed methods is demonstrated through extensive numerical studies.

Information from frequency bands in biomedical time series provides useful summaries of the observed signal. Many existing methods consider summaries of the time series obtained over a few well-known, pre-defined frequency bands of interest. However, there is a dearth of data-driven methods for identifying frequency bands that optimally summarize frequency-domain information in the time series. A new method to identify partition points in the frequency space of a multivariate locally stationary time series is proposed. These partition points signify changes across frequencies in the time-varying behavior of the signal and provide frequency band summary measures that best preserve nonstationary dynamics of the observed series. An $L_2$-norm based discrepancy measure that finds differences in the time-varying spectral density matrix is constructed, and its asymptotic properties are derived. New nonparametric bootstrap tests are also provided to identify significant frequency partition points and to identify components and cross-components of the spectral matrix exhibiting changes over frequencies. Finite-sample performance of the proposed method is illustrated via simulations. The proposed method is used to develop optimal frequency band summary measures for characterizing time-varying behavior in resting-state electroencephalography time series, as well as identifying components and cross-components associated with each frequency partition point.