Application of Multiplicative and Additive Hazards Models to Injury Prevention among Healthcare Workers

Sarker, Sabuj; Min, Dae Kee; Black, Tim; Lim, Hyun J.

doi:10.9734/air/2015/18021

Cited by 2 publications

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“…Seja ̃= min( , ) e ∆ = Ι( ≤ ) , I (.) função indicadora que assume o valor 1 caso uma falha tenha ocorrido e 0 caso contrário (ERIKSSON et al, 2015;GRAW et al, 2009;KUK e VARADHAN, 2013;LI et al, 2015;SARKER et al, 2015;SCHEIKE e ZHANG, 2011;SUN et al, 2006;ZHOU et al, 2011;ZHOU, 2010). As n realizações independentes e identicamente distribuídas de {̃, ∆ , ∆ , } para = 1, … , , formam uma amostra aleatória.…”

Section: Notação Básicaunclassified

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Tomaz

Filho

2019

Rev. Bras. Biom.

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 RESUMO: O objetivo deste trabalho foi avaliar o efeito da censura sobre a função de incidência cumulativa na presença de riscos competitivos. Para tanto, conjuntos de tempos de sobrevivência com riscos competitivos considerando diferentes níveis de censura (0%, 10%, 20%, 30%, 40% e 50%) foram simulados. A simulação foi baseada nos riscos específicos por causa utilizando a distribuição exponencial. Para a simulação dos tempos de censura utilizou-se a distribuição uniforme. Além dos distintos níveis de censura considerou-se diferentes valores para os riscos específicos por causa para os dois eventos competitivos simulados. Comparações entre as curvas de incidência do conjunto de dados sem censura e o conjunto com diferentes porcentagens de censura foram realizadas. Confrontou-se, ainda, as estimativas da função de incidência cumulativa com base na teoria de riscos competitivos com àquela obtida pelo uso do estimador de Kaplan-Meier. Os resultados indicam que as porcentagens de censura bem como relação entre os riscos específicos por causa afetam a função de incidência cumulativa.

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Section: Notação Básicaunclassified

Untitled

Tomaz

Filho

2019

Rev. Bras. Biom.

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“…Specifically, we describe a structure that simultaneously models the survival probabilities and discharge hazards, without pre‐specifying the correlation structure between the two, by extending an approach as advocated by Sun et al ( 2004 , 2006 ), Zhao et al ( 2011 , 2013 ), Lee et al ( 2015 ), and Wang et al ( 2015 ). To enhance the model's flexibility, we allow independent variable effects to modify both the linear predictor and also the baseline hazard function, as suggested by Lin et al ( 1995 ), Liu et al ( 2010 ), Martinussen and Scheike ( 2007 ), and Sarker et al ( 2015 ). To illustrate the proposed method, we analyse data from two clinical applications, one is a randomised clinical trial of anti‐thrombotic therapies in patients suffering from stroke, and the other is a group of patients hospitalised for treatment of COVID.…”

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confidence: 99%

Semi-Parametric Time-to-Event Modelling of Lengths of Hospital Stays

Líu

Wang

et al. 2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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Length of stay (LOS) is an essential metric for the quality of hospital care. Published works on LOS analysis have primarily focused on skewed LOS distributions and the influences of patient diagnostic characteristics. Few authors have considered the events that terminate a hospital stay: Both successful discharge and death could end a hospital stay but with completely different implications. Modelling the time to the first occurrence of discharge or death obscures the true nature of LOS. In this research, we propose a structure that simultaneously models the probabilities of discharge and death. The model has a flexible formulation that accounts for both additive and multiplicative effects of factors influencing the occurrence of death and discharge. We present asymptotic properties of the parameter estimates so that valid inference can be performed for the parametric as well as nonparametric model components. Simulation studies confirmed the good finite‐sample performance of the proposed method. As the research is motivated by practical issues encountered in LOS analysis, we analysed data from two real clinical studies to showcase the general applicability of the proposed model.

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Application of Multiplicative and Additive Hazards Models to Injury Prevention among Healthcare Workers

Cited by 2 publications

References 0 publications

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Semi-Parametric Time-to-Event Modelling of Lengths of Hospital Stays

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