Biometrical Journal最新文献

Functional data analysis has received significant attention due to its frequent occurrence in modern applications, such as in the medical field, where electrocardiograms or electroencephalograms can be used for a better understanding of various medical conditions. Due to the infinite-dimensional nature of functional elements, the current work focuses on dimension reduction techniques. This study shifts its focus to modeling the conditional quantiles of functional data, noting that existing works are limited to quantitative predictors. Consequently, we introduce the first approach to partial dimension reduction for the conditional quantiles under the presence of both functional and categorical predictors. We present the proposed algorithm and derive the convergence rates of the estimators. Moreover, we demonstrate the finite sample performance of the method using simulation examples and a real dataset based on functional magnetic resonance imaging.

In the competing risks setting, the $�t$-year absolute risk for a specific time $�t$ (e.g., 2 years), also called the cumulative incidence function at time $�t$, is often interesting to estimate. It is routinely estimated using the nonparametric Aalen–Johansen estimator. This estimator handles right-censored data and has desirable large sample properties, as it is the nonparametric maximum likelihood estimator (NPMLE). Inference for comparing absolute risks, via either a risk difference or a risk ratio, can therefore be done via usual asymptotic normal approximations and the delta method. However, the small sample performances of this approach are not fully satisfactory. Especially, (i) coverage of confidence intervals may be inaccurate and (ii) comparisons made using a risk ratio and a risk difference can lead to inconsistent conclusions, in terms of statistical significance. We, therefore, introduce an alternative empirical likelihood approach. One advantage of this approach is that it always leads to consistent conclusions when comparing absolute risks via a risk ratio and a risk difference, in terms of significance. Simulation results also suggest that small sample inference using this approach can be more accurate. We present the computation of confidence intervals and p-values using this approach and the asymptotic properties that justify them. We provide formulas and algorithms to compute constrained NPMLE, from which empirical likelihood ratios and inference procedures are derived. The novel approach has been implemented in the timeEL package for R, and some of its advantages are demonstrated via reproducible analyses of bone marrow transplant data.

It is argued that even though censoring and competing events, technically, play similar roles when estimating hazard functions, they are conceptually different and should be treated as such when interpreting time-to-event data.

PAM50 gene expression profiling, a popular and widely used tool, is employed to identify and assess the functional relationships and pathways among genes in patients with breast cancer. Motivated by a study aimed at concurrently recovering dependency and symmetric networks for the PAM50 gene data set, we consider the graphical Gaussian model with symmetry constraints on edges and vertices. The symmetry constraints in the model are represented by imposing equality constraints on the concentration matrix. This model allows us to simultaneously explore the dependency relationships and symmetrical structure among the variables. The symmetrical structure of PAM50 gene expression can deepen our understanding of their functional similarities and the inherent symmetrical properties of gene regulatory behavior. Prioritizing candidate genes with high functional similarity will help elucidate the underlying biological mechanisms for the disease progression. To effectively capture the network's structure, we utilize a birth–death Markov Chain Monte Carlo method. This method is a continuous-time and transdimensional search algorithm that is particularly effective in this context. To further improve the efficiency of the algorithm, we propose a stepwise model learning strategy combined with an approximation method for the posterior distribution. To validate the effectiveness of our approach, we finally apply it in various simulation studies as well as in a practical application involving the PAM50 gene expression data set.

In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization. In our motivating study, a randomized controlled trial in oncology, patients assigned the investigational treatment may discontinue it due to adverse events. The ICH E9(R1) Addendum provides guidelines for handling such “intercurrent events.” The right strategy to adopt depends on the questions of interest. We propose adopting a principal stratum strategy and decomposing the overall intention-to-treat effect into principal causal effects for groups of patients defined by their potential discontinuation behaviour. We first show how to implement a principal stratum strategy to assess causal effects on a survival outcome in the presence of continuous-time treatment discontinuation, its advantages, and the conclusions that can be drawn. Our strategy allows us to properly handle the time-to-event intermediate variable, which is not defined for patients who would not discontinue, and to account for the fact that the discontinuation time and the primary endpoint are subject to censoring. We employ a flexible model-based Bayesian approach to tackle these complexities, providing easily interpretable results. We apply this Bayesian principal stratification framework to analyze synthetic data of the motivating oncology trial. Supported by a simulation study, we shed light on the role of covariates in this framework. Beyond making structural and parametric assumptions more credible, they lead to more precise inference. Also, they can be used to characterize patients' discontinuation behavior, which could help inform clinical practice and future protocols.

Antitumor activity in oncology clinical trials is typically assessed using overall survival (OS) or progression-free survival (PFS) endpoints, which are often imprecise and uninformative in small, noncomparative studies. The tumor growth inhibition (TGI) model, which captures both drug effects and natural tumor growth, quantitatively characterizes tumor size dynamics as a function of drug dosage, offering a more informative framework for comparing cancer treatments. In this work, we study the locally optimal design for a comparative oncology trial in which Dalpiciclib is the investigational agent and Capecitabine is the reference drug under an active control (AC) setting. Our novel approach avoids unrealistic distributional assumptions about response measurements. The resulting locally AC-optimal design minimizes the variance of the estimated matching dose of Dalpiciclib to Capecitabine and may unify Phase II and Phase III objectives by allowing evaluation of a higher Dalpiciclib dose with prespecified superiority.