Biometrical Journal最新文献

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.

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.

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.

Longitudinal data are often subject to irregular visiting times, with outcomes and visit times influenced by a latent variable. Semi-parametric joint models that account for this dependence have been proposed; among these, the Sun model is the most suitable for count data as it employs a multiplicative link function. Semi-parametric joint models define an intercept function as the mean outcome when all covariates are set to zero; this is differenced out in the course of estimation and is consequently not estimated. The Sun estimator thus provides estimates of relative covariate effects, but is unable to provide estimates of absolute effects or of longitudinal prognosis in the absence of covariates. We extend the Sun model by additionally estimating the intercept term, showing that our extended estimator is consistent and asymptotically Normal. In simulations, our estimator outperforms the original Sun estimator in terms of bias and standard error and is also more computationally efficient. We apply our estimator to a longitudinal study of tumor recurrence among bladder cancer patients. Provided the intercept term can be adequately captured using splines, we recommend that our extended Sun estimator be used in place of the original estimator, since it leads to smaller bias, smaller standard errors, and allows estimation of the mean outcome trajectories.

In longitudinal observational studies, marginal structural models (MSMs) are used to analyze the causal effect of an exposure on the (time-to-event) outcome of interest, while accounting for exposure-affected time-dependent confounding. In the applied literature, inverse probability of treatment weighting (IPTW) has been widely adopted to estimate MSMs. An essential assumption for IPTW-based MSMs is positivity, which requires that, for any combination of measured confounders among individuals, there is a nonzero probability of receiving each treatment strategy. Positivity is crucial for valid causal inference through IPTW-based MSMs, but is often overlooked compared to confounding bias. Near-positivity violations, where certain treatments are theoretically possible but rarely observed due to randomness, are common in practical applications, particularly when the sample size is small, and they pose significant challenges for causal inference. This study investigates the impact of near-positivity violations on estimates from IPTW-based MSMs in survival analysis. Two algorithms are proposed for simulating longitudinal data from hazard-MSMs, accommodating near-positivity violations, a time-varying binary exposure, and a time-to-event outcome. Cases of near-positivity violations, where remaining unexposed is rare within certain confounder levels, are analyzed across various scenarios and weight truncation (WT) strategies. Through comprehensive simulations, this study shows that even minor near-positivity violations in longitudinal survival analyses can substantially destabilize IPTW-based estimators, inflating variance and bias, especially under aggressive WT. This work aims to serve as a critical warning against overlooking the positivity assumption or naively applying WT in causal studies using longitudinal observational data and IPTW.