Biometrical Journal最新文献

Although count data are collected in many experiments, their analysis remains challenging, especially in small sample sizes. Until now, linear or generalized linear models in Poisson or Negative Binomial distributional families have often been used. However, these data frequently show signs of over-, underdispersion, or even zero-inflation, casting doubt on these distributional assumptions and leading to inaccurate test results. Since their distributions are usually skewed, data transformations (e.g., log-transformation) are not unusual. This underscores the need for statistical methods not to hinge on specific distributional assumptions. We delve into multiple contrast tests that allow general contrasts (e.g., many-to-one or all-pairs comparisons) to analyze count data in multi-arm trials. The methods vary in their effect and variance estimation, as well as in approximating the joint distribution of multiple test statistics, including frequently used methods such as linear and generalized linear models, and data transformations. An extensive simulation study demonstrates that a resampling version effectively controls the Type I error rate in various situations, while also highlighting the method's limitations, including overly liberal Type I error rates. Some standard methods, which have inflated Type I error rates, further underscore the need for alternative approaches. Real data applications further emphasize the applicability of these methods.

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.

This paper introduces a sensitivity analysis method for multiple testing procedures (MTPs) using marginal $p$ -values. The method is based on the Dirichlet process (DP) prior distribution, specified to support the entire space of MTPs, where each MTP controls either the family-wise error rate (FWER) or the false discovery rate (FDR) under arbitrary dependence between $p$ -values. This DP-MTP sensitivity analysis method provides uncertainty quantification for MTPs, by accounting for uncertainty in the selection of such MTPs and their respective threshold-based decisions regarding which number of smallest $p$ -values are significant discoveries, from a given set of null hypothesis tested, while measuring each $p$ -value's probability of significance over the DP prior predictive distribution of this space of all MTPs, and reducing the possible conservativeness of using only one such MTP for multiple testing. The DP-MTP sensitivity analysis method is illustrated through the analysis of over 28,000 $p$ -values arising from hypothesis tests performed on a 2022 dataset of a representative sample of three million U.S. high school students observed on 239 variables. They include tests which, respectively, relate variables about the disruption caused by school closures during the COVID-19 pandemic, with various mathematical cognition, academic achievement, and student background variables. R software code for the DP-MTP sensitivity analysis method is provided in the Code and Data Supplement (CDS) of this paper.

In this paper, some aspects concerning the causal interpretation of hazard contrasts are revisited. It is first investigated, in which sense the hazard ratio constitutes a causal effect. It is demonstrated that the hazard ratio at a timepoint $�t$ represents a causal effect for the population at baseline, but in general not for any population at risk at time $�t$. Moreover, the scenario is studied, in which the survival curves coincide up to some timepoint $�t$ and then separate. This investigation provides valuable insight both on the causal interpretation of the conventional hazard ratio and on properties of the recently proposed causal hazard ratio. The findings suggest that, without making further assumptions, there is in general no meaningful estimand for a treatment effect at time $��t�>�0�$. It is therefore advocated to develop alternative estimands grounded in medically plausible assumptions about the joint distribution of counterfactual survival times.