Parametric and Non-Parametric Survival Models for “Time to Failure” of Water Pipelines: Case Study

2008 ◽  
Author(s):  
Ahmad Asnaashari ◽  
Isam Shahrour ◽  
Bahram Gharabaghi ◽  
Edward McBean
Keyword(s):  
Survival Analysis ◽  
Survival Time ◽  
Failure Process ◽  
Survival Models ◽  
Time To Failure ◽  
Priority List ◽  
Water Pipelines ◽  
Water Mains ◽  
Parametric Survival ◽  
Non Parametric

An application of survival analysis on Iranian water pipelines failure dataset is employed to shed additional light on the pipeline failure process as well as to extract useful information that can be helpful in future asset management planning. Survival analysis characterizes the distribution of the survival time for different groups of pipes, to compare this survival time among different type of materials. A parametric model is developed to simulate time to failure in the pipe network. The model was calibrated on the historical failure data collected over the period 1995 – 2001, and then it was tested using data since 2002. Using both parametric and non-parametric survival models makes it possible to establish a priority list for future water pipelines rehabilitation undertakings in accordance with their material type. Accordingly, it is recommended that implementation of pipeline rehabilitation projects proceeds firstly on metallic water mains, then on plastic water mains, and finally on cement water pipelines.

scholarly journals Pitfalls and perils of survival analysis under incorrect assumptions: the case of Covid-19 data

Biomédica ◽  
2021 ◽  
Vol 41 (Sp. 2) ◽  
Author(s):  
Daniele Piovani ◽  
Georgios K. Nikolopoulos ◽  
Stefanos Bonovas
Keyword(s):  
Decision Making ◽  
Survival Analysis ◽  
Preventive Measures ◽  
Clinical Course ◽  
Virus Disease ◽  
Current Medical Research ◽  
Basic Assumptions ◽  
Parametric Survival ◽  
Parametric Survival Analysis ◽  
Non Parametric

Non-parametric survival analysis has become a very popular statistical method in current medical research. Employing, however, survival methodology when its fundamental assumptions are not fulfilled can severely bias the results. Currently, hundreds of clinical studies are using survival methods to investigate factors potentially associated with the prognosis of Corona Virus Disease 2019 (Covid-19), and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever that decision-making is evidence-based and relies on solid statistical methods. However, this is not always the case. Serious methodologic errors have been identified in recent seminal studies about Covid-19: one reporting outcomes of patients treated with remdesivir, and another one on the epidemiology, clinical course and outcomes of critically-ill patients. High-quality evidence is essential to inform clinicians about optimal Covid-19 therapies, and policymakers about the true effect of preventive measures aiming to tackle the pandemic. Though timely evidence is needed, we should encourage the appropriate application of survival analysis methods and careful peer-review to avoid publishing flawed results, which could affect decision-making. In this paper, we recapitulate the basic assumptions underlying non-parametric survival analysis and frequent errors in its application, and discuss how to handle data of Covid-19.

scholarly journals ANALISIS SINTASAN PARAMETRIK PADA PASIEN STROKE DENGAN PENDEKATAN DISTRIBUSI WEIBULL

2019 ◽  
Vol 8 (1) ◽  
pp. 55
Author(s):  
NI MADE SRI WAHYUNI ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Body Mass Index ◽  
Survival Analysis ◽  
Weibull Distribution ◽  
Survival Data ◽  
The Body ◽  
Predictor Variables ◽  
Survival Models ◽  
Stroke Patients ◽  
Parametric Survival ◽  
Parametric Survival Analysis

Parametric survival analysis is one of the survival analysis that has a distribution of survival data that follows a certain distribution. Weibull distribution is a distribution that is often used in parametric survival analysis. The purpose of this study is to determine parametric survival models using the Weibull distribution and to determine  the factors that can influence the recovery of stroke patients. This study uses data on stroke patients in the Wangaya hospital, Denpasar in 2017. The best model obtained in this study is a model that consists of two predictor variables, namely the age and the body mass index (BMI).Therefore the  factors that can influence the recovery of stroke patients are age and BMI.

scholarly journals Comparison of survival models and assessment of risk factors for survival of cardiovascular patients at Addis Ababa Cardiac Center, Ethiopia: a retrospective study

2021 ◽  
Vol 21 (3) ◽  
pp. 1201-1213
Author(s):  
Belaynesh Yeniew Enyew ◽  
Zeytu Gashaw Asfaw
Keyword(s):  
Retrospective Study ◽  
Addis Ababa ◽  
The Other ◽  
Cardiac Patients ◽  
Survival Models ◽  
Cardiovascular Patients ◽  
Male Patients ◽  
Parametric Survival ◽  
Cardiac Center ◽  
Non Parametric

Background: Cardiovascular diseases (CVDs) is disorders of heart and blood vessels. It is a major health problem across the world,and 82% of CVD deaths is contributed by countries with low and middle income. The aim of this study was to choose appropriate model for the survival of cardiovascular patients data and identify the factors that affect the survival of cardiovascular patients at Addis Ababa Cardiac Center. Method: A Retrospective study was conducted on patients under follow-up at Addis Ababa Cardiac Center between Sep- tember 2010 to December 2018. The patients included have made either post operation or pre-operation. Out of 1042 car- diac patients, a sample of 332 were selected for the current study using simple random sampling technique. Non-parametric, semi-parametric and parametric survival models were used and comparisons were made to select the appropriate predicting model. Results: Among the sample of 332 cardiac patients, only 67(20.2%) experienced CVD and the remaining 265(79.8%) were censored. The median and the maximum survival time of cardiac patients was 1925 and 1403 days respectively.The estimated hazard ratio of male patients to female patients is 1.926214 (95%CI: 1.111917-3.336847; p = 0.019) implying that the risk of death of male patients is 1.926214 times higher than female cardiac patients keeping the other covariates constant in the model. Even if, all semi parametric and parametric survival models fitted to the current data well, various model comparison criteria showed that parametric/weibull AFT survival model is better than the other. Conclusions: The governmental and non-governmental stakeholders should pay attention to give training on the risk fac- tors identified on the current study to optimize individual’s knowledge and awareness so that death due to CVDs can be minimized. Keywords: Cardiovascular patient; survival analysis; non-parametric; semi-parametric; parametric.

scholarly journals Estimating restricted mean survival time and expected life-years lost in the presence of competing risks within flexible parametric survival models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarwar I. Mozumder ◽  
Mark J. Rutherford ◽  
Paul C. Lambert
Keyword(s):  
Survival Time ◽  
Cause Of Death ◽  
Competing Risks ◽  
Survival Models ◽  
Mean Survival Time ◽  
Competing Risks Analysis ◽  
Life Years ◽  
Restricted Mean Survival Time ◽  
Life Years Lost ◽  
Parametric Survival

Abstract Background Royston-Parmar flexible parametric survival models (FPMs) can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. Restricted mean survival time (RMST), or restricted mean failure time (RMFT) on the mortality scale, is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. Compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure as introduced by Andersen. Methods In the presence of competing risks, prediction of RMFT and the expected life-years lost due to a cause of death are presented using Royston-Parmar FPMs. These can be predicted for a specific covariate pattern to facilitate interpretation in observational studies at the individual level, or at the population-level using standardisation to obtain marginal measures. Predictions are illustrated using English colorectal data and are obtained using the Stata post-estimation command, standsurv. Results Reporting such measures facilitate interpretation of a competing risks analysis, particularly when the proportional hazards assumption is not appropriate. Standardisation provides a useful way to obtain marginal estimates to make absolute comparisons between two covariate groups. Predictions can be made at various time-points and presented visually for each cause of death to better understand the overall impact of different covariate groups. Conclusions We describe estimation of RMFT, and expected life-years lost partitioned by each competing cause of death after fitting a single FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. These can be used to facilitate interpretation of a competing risks analysis when the proportionality assumption is in doubt.

scholarly journals Estimating restricted mean survival time and expected life-years lost in the presence of competing risks within flexible parametric survival models

2020 ◽  
Author(s):  
Sarwar Islam Mozumder ◽  
Paul Lambert ◽  
Mark Rutherford
Keyword(s):  
Survival Time ◽  
Cause Of Death ◽  
Competing Risks ◽  
Survival Models ◽  
Subdistribution Hazard ◽  
Mean Survival Time ◽  
Life Years ◽  
Restricted Mean Survival Time ◽  
Life Years Lost ◽  
Parametric Survival

Abstract We present various measures, specifically the expected life-years list due to a cause of death, that can be predicted for a specific covariate pattern. These can also be summarised at the population-level using standardisation to obtain marginal measures. The restricted mean survival time (RMST) measure can be obtained in the presence of competing risks using Royston-Parmar flexible parametric survival models (FPMs). Royston-Parmar FPMs can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which other alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. The RMST estimate is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. In addition to this, compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure. We describe estimation of RMST after fitting a FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. As an illustration of reporting such measures to facilitate interpretation of a competing risks analysis, models are fitted to English colorectal data.

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