A recursive method for the health assessment of systems using the proportional hazards model

Zheng, Rui; Najafi, Seyedvahid; Zhang, Yingzhi

doi:10.1016/j.ress.2022.108379

Cited by 19 publications

(6 citation statements)

References 31 publications

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“…We note that the issue of dependent censoring in survival prognostic prediction is not restricted to medical research, which can be seen in the reliability analysis of mechanical items or systems [ 68 , 69 , 70 , 71 , 72 , 73 ]. In the reliability prediction of mechanical equipment, the survival probability is usually modeled by parametric distributions, such as the Weibull distribution.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

2023

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Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package “compound.Cox”. The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

2023

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“…However, it is not trivial to extend this prediction method under competing risks. This risk prediction scheme is equivalent to dynamic prediction [48][49][50][51][52][53]. Moreover, the accelerated life test plans may be considered including the case of one-shot devices with competing risks [54,55].…”

Section: Discussionmentioning

confidence: 99%

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Michimae

Emura

2022

Mathematics

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Survival and reliability analyses deal with incomplete failure time data, such as censored and truncated data. Recently, the classical left-truncation scheme was generalized to analyze “field data”, defined as samples collected within a fixed period. However, existing competing risks models dealing with left-truncated field data are not flexible enough. We propose copula-based competing risks models for latent failure times, permitting a flexible parametric form. We formulate maximum likelihood estimation methods under the Weibull, lognormal, and gamma distributions for the latent failure times. We conduct simulations to check the performance of the proposed methods. We finally give a real data example. We provide the R code to reproduce the simulations and data analysis results.

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“…As a statistical procedure, PHM estimates equipment failure risk based on conditionmonitoring information [8,9]. The way to describe Cox's PHM is the baseline hazard function that normally is used by assuming Weibull distribution due to its flexibility and closed-form risk and reliability function [10,11], but others like the Gamma distribution could be used as well [12]. Similarly, ref.…”

Section: Proportional Hazards Model (Phm)mentioning

confidence: 99%

Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions

Godoy,

Álvarez,

Mena

et al. 2024

Machines

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The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox’s partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes.

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A recursive method for the health assessment of systems using the proportional hazards model

Cited by 19 publications

References 31 publications

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions

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