Additive and multiplicative hazards modeling for recurrent event data analysis

<p>Abstract</p> <p>Background</p> <p>Sequentially ordered multivariate failure time or recurrent event duration data are commonly observed in biomedical longitudinal studies. In general, standard hazard regression methods cannot be applied because of correlation between recurrent failure times within a subject and induced dependent censoring. Multiplicative and additive hazards models provide the two principal frameworks for studying the association between risk factors and recurrent event durations for the analysis of multivariate failure time data.</p> <p>Methods</p> <p>Using emergency department visits data, we illustrated and compared the additive and multiplicative hazards models for analysis of recurrent event durations under (i) a varying baseline with a common coefficient effect and (ii) a varying baseline with an order-specific coefficient effect.</p> <p>Results</p> <p>The analysis showed that both additive and multiplicative hazards models, with varying baseline and common coefficient effects, gave similar results with regard to covariates selected to remain in the model of our real dataset. The confidence intervals of the multiplicative hazards model were wider than the additive hazards model for each of the recurrent events. In addition, in both models, the confidence interval gets wider as the revisit order increased because the risk set decreased as the order of visit increased.</p> <p>Conclusions</p> <p>Due to the frequency of multiple failure times or recurrent event duration data in clinical and epidemiologic studies, the multiplicative and additive hazards models are widely applicable and present different information. Hence, it seems desirable to use them, not as alternatives to each other, but together as complementary methods, to provide a more comprehensive understanding of data.</p

A Fluorescent Cage for Supramolecular Sensing of 3-Nitrotyrosine in Human Blood Serum

3-Nitrotyrosine (NT) is generated by the action of peroxynitrite and other reactive nitrogen species (RNS), and as a consequence it is accumulated in inflammation-associated conditions. This is particularly relevant in kidney disease, where NT concentration in blood is considerably high. Therefore, NT is a crucial biomarker of renal damage, although it has been underestimated in clinical diagnosis due to the lack of an appropriate sensing method. Herein we report the first fluorescent supramolecular sensor for such a relevant compound: Fluorescence by rotational restriction of tetraphenylethenes (TPE) in a covalent cage is selectively quenched in human blood serum by 3-nitrotyrosine (NT) that binds to the cage with high affinity, allowing a limit of detection within the reported physiological concentrations of NT in chronic kidney disease.This work was financially supported by Ministerio de Ciencia e Innovación (PGC2018-094503-B-C21, PID2019-110430GB-C21, PID2019-107335RA-I00 and PGC2018-096880-A-I00 MCIU/ AEI/FEDER, UE) and Gobierno Autónomo de Canarias (ProID2020010067). L.A.P.-M. and M.D.P thanks the ACIISI of the Consejería de Economía, Industria, Comercio y Conocimiento and the European Social Fund (ESF) Canary Islands Integrated Operational Program 2014–2020, Area 3 Priority Theme 74 (85 %), for a predoctoral grant. R.G.R. thanks the EU (ESF) for Ramon y Cajal contract (R. G.-R., RYC-2015-19035). The authors thank Prof. Carlos Perretti V. for his support with the statistical analysis, Prof. Javier Hernandez-Borges for helpful discussions, and “Servicio de análisis y determinación estructural” at IPNA-CSIC, particularly Nieves M. Rodríguez and Manuel Cabrera for their technical support in this research.Peer reviewe

Cover Design by Cliff Hasham Improving Design Processes through Structured Reflection A Domain-independent Approach

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