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Rationale: Observational studies report a significant protective effect of antifibrotics on mortality among patients with idiopathic pulmonary fibrosis (IPF). Many of these studies, however, were subject to immortal time bias because of the mishandling of delayed antifibrotic initiation. Objectives: To evaluate the antifibrotic effect on mortality among patients with IPF using appropriate statistical methods that avoid immortal time bias. Methods: Using a large administrative database, we identified 10,289 patients with IPF, of whom 2,300 used antifibrotics. Treating delayed antifibrotic initiation as a time-dependent variable, three statistical methods were used to control baseline characteristics and avoid immortal time bias. Stratified analysis was performed for patients who initiated antifibrotics early and those who initiated treatment late. For comparison, methods that mishandle immortal time bias were performed. A simulation study was conducted to demonstrate the performance of these models in a wide range of scenarios. Results: All three statistical methods yielded nonsignificant results for the antifibrotic effect on mortality, with the stratified analysis for patients with early antifibrotic initiation suggesting evidence for reduced mortality risk (for all patients, hazard ratio, 0.89; 95% confidence interval, 0.79–1.01; P = 0.08; for patients who were 65 years or older, hazard ratio, 0.85; 95% confidence interval, 0.73–0.98; P = 0.03). Methods that mishandle immortal time bias demonstrated significantly lower mortality risk for antifibrotic users. Bias of these methods was evident in the simulation study, where appropriate methods performed well with little to no bias. Conclusions: Findings in this study did not confirm an association between antifibrotics and mortality, with a stratified analysis showing support for a potential treatment effect with early treatment initiation. [ABSTRACT FROM AUTHOR]

The Fellegi-Sunter model is a latent class model widely used in probabilistic linkage to identify records that belong to the same entity. Record linkage practitioners typically employ all available matching fields in the model with the premise that more fields convey greater information about the true match status and hence result in improved match performance. In the context of model-based clustering, it is well known that such a premise is incorrect and the inclusion of noisy variables could compromise the clustering. Variable selection procedures have therefore been developed to remove noisy variables. Although these procedures have the potential to improve record matching, they cannot be applied directly due to the ubiquity of the missing data in record linkage applications. In this paper, we modify the stepwise variable selection procedure proposed by Fop, Smart, and Murphy and extend it to account for missing data common in record linkage. Through simulation studies, our proposed method is shown to select the correct set of matching fields across various settings, leading to better-performing algorithms. The improved match performance is also seen in a real-world application. We therefore recommend the use of our proposed selection procedure to identify informative matching fields for probabilistic record linkage algorithms. [ABSTRACT FROM AUTHOR]

Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of small modular reactors (SMRs) and in the context of massive integration of intermittent renewables, it is required to operate the nuclear plants more reliably, efficiently, flexibly and smartly, motivating the recent exciting progress in nuclear plant modeling and control. In this paper, the main progress during the last several years in dynamical modeling and control of nuclear plants is reviewed. The requirement of nuclear plant operation to the subject of modeling and control is first given. By categorizing the results to the aspects of mechanism-based, data-based and hybrid modeling methods, the advances in dynamical modeling are then given, where the modeling of SMR plants, learning-based modeling and state-observers are typical hot topics. In addition, from the directions of intelligent control, nonlinear control, online control optimization and multimodular coordinated control, the advanced results in nuclear plant control methods are introduced, where the hot topics include fuzzy logic inference, neural-network control, reinforcement learning, sliding mode, feedback linearization, passivation and decoupling. Based upon the review of recent progress, the future directions in nuclear plant modeling and control are finally given. [ABSTRACT FROM AUTHOR]