Biometrical Journal最新文献

We develop a variable selection method for interactions in regression models on large data in the context of genetics. The method is intended for investigating the influence of single-nucleotide polymorphisms (SNPs) and their interactions on health outcomes, which is a $��p�\gg �n�$ problem. We introduce cross leverage scores (CLSs) to detect interactions of variables while maintaining interpretability. Using this method, it is not necessary to consider every possible interaction between variables individually, which would be very time-consuming even for moderate amounts of variables. Instead, we calculate the CLS for each variable and obtain a measure of importance for this variable. Calculating the scores remains time-consuming for large data sets. The key idea for scaling to large data is to divide the data into smaller random batches or consecutive windows of variables. This avoids complex and time-consuming computations on high-dimensional matrices by performing the computations only for small subsets of the data, which is less costly. We compare these methods to provable approximations of CLS based on sketching, which aims at summarizing data succinctly. In a simulation study, we show that the CLSs are directly linked to the importance of a variable in the sense of an interaction effect. We further show that the approximation approaches are appropriate for performing the calculations efficiently on arbitrarily large data while preserving the interaction detection effect of the CLS. This underlines their scalability to genome wide data. In addition, we evaluate the methods on real data from the HapMap project.

Ordinary differential equations (ODEs) are foundational tools in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise. Rooted in the model misspecification framework, we adapt classical statistical paradigms (Vuong and Hotelling) to the ODE context, allowing for the comparison and ranking of diverse causal explanations without the constraints of nested models. Our simulation studies numerically investigate the statistical properties of the test, demonstrating its attainment of the nominal size and power across various settings. Real-world data examples further underscore the algorithm's applicability in practice. To foster accessibility and encourage real-world applications, we provide a user-friendly Python implementation of our model selection algorithm, bridging theoretical advancements with hands-on tools for the scientific community.

Prediction models are used among others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: for example, an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred, and reevaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions.

A concern surrounding marijuana legalization is that driving after marijuana use may become more prevalent. Survey data are valuable for estimating policy effects, however their observational nature and unequal sampling probabilities create challenges for causal inference. To estimate population-level effects using survey data, we propose a matched design and implement sensitivity analyses to quantify how robust conclusions are to unmeasured confounding. Both theoretical justification and simulation studies are presented. We found no support that marijuana legalization increased tolerant behaviors and attitudes toward driving after marijuana use, and these conclusions seem moderately robust to unmeasured confounding.

Gaussian processes and the Kullback–Leibler divergence have been deeply studied in statistics and machine learning. This paper marries these two concepts and introduce the local Kullback–Leibler divergence to learn about intervals where two Gaussian processes differ the most. We address subtleties entailed in the estimation of local divergences and the corresponding interval of local maximum divergence as well. The estimation performance and the numerical efficiency of the proposed method are showcased via a Monte Carlo simulation study. In a medical research context, we assess the potential of the devised tools in the analysis of electrocardiogram signals.

Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, for example, inverse probability of treatment weighting (IPTW). It is important to evaluate the performance of statistical methods in different scenarios, and simulation studies are a key tool for such evaluations. In such simulation studies, it is common to generate data in such a way that the model of interest is correctly specified, but this is not always straightforward when the model of interest is for potential outcomes, as is an MSM. Methods have been proposed for simulating from MSMs for a survival outcome, but these methods impose restrictions on the data-generating mechanism. Here, we propose a method that overcomes these restrictions. The MSM can be, for example, a marginal structural logistic model for a discrete survival time or a Cox or additive hazards MSM for a continuous survival time. The hazard of the potential survival time can be conditional on baseline covariates, and the treatment variable can be discrete or continuous. We illustrate the use of the proposed simulation algorithm by carrying out a brief simulation study. This study compares the coverage of confidence intervals calculated in two different ways for causal effect estimates obtained by fitting an MSM via IPTW.