Relevant bibliographies by topics / Time-dependent covariates

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Time-dependent covariates'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Time-dependent covariates"

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Yu, Qiqing, George Y. C. Wong, Michael P. Osborne, Yuting Hsu, and Xiaosong Ai. "The Lehmann Model with Time-dependent Covariates." Communications in Statistics - Theory and Methods 44, no. 20 (August 29, 2013): 4380–95. http://dx.doi.org/10.1080/03610926.2013.784991.

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Barnett, Adrian, and Nick Graves. "Competing risks models and time-dependent covariates." Critical Care 12, no. 2 (2008): 134. http://dx.doi.org/10.1186/cc6840.

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Marks, Gary F., Kenneth R. Hess, and James B. Young'. "80P Survival estimates for time-dependent covariates." Controlled Clinical Trials 15, no. 3 (June 1994): 122. http://dx.doi.org/10.1016/0197-2456(94)90208-9.

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Lu, Bo. "Propensity Score Matching with Time-Dependent Covariates." Biometrics 61, no. 3 (May 12, 2005): 721–28. http://dx.doi.org/10.1111/j.1541-0420.2005.00356.x.

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Gupta, Sunil. "Stochastic Models of Interpurchase Time with Time-Dependent Covariates." Journal of Marketing Research 28, no. 1 (February 1991): 1. http://dx.doi.org/10.2307/3172722.

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Gupta, Sunil. "Stochastic Models of Interpurchase Time with Time-Dependent Covariates." Journal of Marketing Research 28, no. 1 (February 1991): 1–15. http://dx.doi.org/10.1177/002224379102800101.

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Barabadi, Abbas, Javad Barabady, and Tore Markeset. "Maintainability analysis considering time-dependent and time-independent covariates." Reliability Engineering & System Safety 96, no. 1 (January 2011): 210–17. http://dx.doi.org/10.1016/j.ress.2010.08.007.

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Aydemir, Ülker, Sibel Aydemir, and Peter Dirschedl. "Analysis of time-dependent covariates in failure time data." Statistics in Medicine 18, no. 16 (August 30, 1999): 2123–34. http://dx.doi.org/10.1002/(sici)1097-0258(19990830)18:16<2123::aid-sim176>3.0.co;2-4.

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Vandormael, Alain, Frank Tanser, Diego Cuadros, and Adrian Dobra. "Estimating trends in the incidence rate with interval censored data and time-dependent covariates." Statistical Methods in Medical Research 29, no. 1 (February 19, 2019): 272–81. http://dx.doi.org/10.1177/0962280219829892.

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We propose a multiple imputation method for estimating the incidence rate with interval censored data and time-dependent (and/or time-independent) covariates. The method has two stages. First, we use a semi-parametric G-transformation model to estimate the cumulative baseline hazard function and the effects of the time-dependent (and/or time-independent covariates) on the interval censored infection times. Second, we derive the participant's unique cumulative distribution function and impute infection times conditional on the covariate values. To assess performance, we simulated infection times from a Cox proportional hazards model and induced interval censoring by varying the testing rate, e.g., participants test 100%, 75%, 50% of the time, etc. We then compared the incidence rate estimates from our G-imputation approach with single random-point and mid-point imputation. By comparison, our G-imputation approach gave more accurate incidence rate estimates and appropriate standard errors for models with time-independent covariates only, time-dependent covariates only, and a mixture of time-dependent and time-independent covariates across various testing rates. We demonstrate, for the first time, a multiple imputation approach for incidence rate estimation with interval censored data and time-dependent (and/or time-independent) covariates.
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Murad, Havi, Rachel Dankner, Alla Berlin, Liraz Olmer, and Laurence S. Freedman. "Imputing missing time-dependent covariate values for the discrete time Cox model." Statistical Methods in Medical Research 29, no. 8 (November 3, 2019): 2074–86. http://dx.doi.org/10.1177/0962280219881168.

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We describe a procedure for imputing missing values of time-dependent covariates in a discrete time Cox model using the chained equations method. The procedure multiply imputes the missing values for each time-period in a time-sequential manner, using covariates from the current and previous time-periods as well as the survival outcome. The form of the outcome variable used in the imputation model depends on the functional form of the time-dependent covariate(s) and differs from the case of Cox regression with only baseline covariates. This time-sequential approach provides an approximation to a fully conditional approach. We illustrate the procedure with data on diabetics, evaluating the association of their glucose control with the risk of selected cancers. Using simulations we show that the suggested estimator performed well (in terms of bias and coverage) for completely missing at random, missing at random and moderate non-missing-at-random patterns. However, for very strong non-missing-at-random patterns, the estimator was seriously biased and the coverage was too low. The procedure can be implemented using multiple imputation with the Fully conditional Specification (FCS) method (MI procedure in SAS with FCS statement or similar packages in other software, e.g. MICE in R). For use with event times on a continuous scale, the events would need to be grouped into time-intervals.
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Dissertations / Theses on the topic "Time-dependent covariates"

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Li, Guilin. "Re-analyses of Framingham data using time-dependent covariates." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ55075.pdf.

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Li, Guilin 1973. "Re-analyses of Framingham data using time-dependent covariates." Thesis, McGill University, 1999. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29907.

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I propose a new approach, based on time-dependent covariates, to assess the impact of within-subject changes in predictors on subsequent mortality, and apply it to reevaluate the impact of changes in serum cholesterol and smoking status on the coronary heart mortality in the Framingham Heart Study. Time-dependent covariates, representing updated risk factor value or its changes from either the baseline or the most recent measurement are included in two types of multivariable Cox regression analyses. The results reveal that in order to avoid confounding of the effects of changes in risk factor, the model should include a time-dependent variable identifying subjects who developed coronary disease during the follow-up. After adjusting for this variable, a within-subject decrease in cholesterol was associated with a significant reduction of corollary mortality, in contrast to the results of previous studies that did not prevent such confounding.
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Nwoko, Onyekachi Esther. "Approaches for Handling Time-Varying Covariates in Survival Models." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31187.

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Survival models are used in analysing time-to-event data. This type of data is very common in medical research. The Cox proportional hazard model is commonly used in analysing time-to-event data. However, this model is based on the proportional hazard (PH) assumption. Violation of this assumption often leads to biased results and inferences. Once non-proportionality is established, there is a need to consider time-varying effects of the covariates. Several models have been developed that relax the proportionality assumption making it possible to analyse data with time-varying effects of both baseline and time-updated covariates. I present various approaches for handling time-varying covariates and time-varying effects in time-to-event models. They include the extended Cox model which handles exogenous time-dependent covariates using the counting process formulation introduced by cite{andersen1982cox}. Andersen and Gill accounts for time varying covariates by each individual having multiple observations with the total-at-risk follow up for each individual being further divided into smaller time intervals. The joint models for the longitudinal and time-to-event processes and its extensions (parametrization and multivariate joint models) were used as it handles endogenous time-varying covariates appropriately. Another is the Aalen model, an additive model which accounts for time-varying effects. However, there are situations where all the covariates of interest do not have time-varying effects. Hence, the semi-parametric additive model can be used. In conclusion, comparisons are made on the results of all the fitted models and it shows that choice of a particular model to fit is influenced by the aim and objectives of fitting the model. In 2002, an AntiRetroviral Treatment (ART) service was established in the Cape Town township of Gugulethu, South Africa. These models will be applied to an HIV/AIDS observational dataset obtained from all patients who initiated ART within the programme between September 2002 and June 2007.
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Wang, Xu. "Joint inference for longitudinal and survival data with incomplete time-dependent covariates." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27842.

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In many longitudinal studies, individual characteristics associated with their repeated measures may be covariates for the time to an event of interest. Thus, it is desirable to model both the survival process and the longitudinal process together. Statistical analysis may be complicated with missing data or measurement errors in the time-dependent covariates. This thesis considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the survival process. We provide a method based on the joint likelihood for nonignorable missing data, and we extend the method to the case of time-dependent covariates. We adapt a Monte Carlo EM algorithm to estimate the model parameters. We compare the method with the existing two-step method with some interesting findings. A real example from a recent HIV study is used as an illustration.
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Abdel, Hamid Hisham. "Flexible parametric survival models with time-dependent covariates for right censored data." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/360380/.

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In survival studies the values of some covariates may change over time. It is natural to incorporate such time dependent covariates into the model to be used in the survival analysis. A standard approach is to use the semi parametric extended Cox proportional hazard model. An alternative is to extend a standard parametric model, such as a Weibull regression model, to include time-dependent covariates. However, the use of such simple parametric models may be too restrictive. Therefore in this thesis we further extend the Weibull regression model with time dependent covariates by using splines to give greater flexibility. The use of Cox, simple parametric and Weibull spline models is illustrated with and without time dependent covariates on two large survival data sets supplied by NHS Blood and Transplant. One data set involves times to graft failure of patients who have undergone a corneal transplant and contains many fixed covariates and one time-dependent covariate with at most one change point. The other data set concerns time to death of heart transplant patients and contains many fixed covariates and a time-dependent covariate with possibly many change points. A simulation study is used to evaluate and compare likelihood-based methods of inference for the competing models. In the first stage attention is focused on selection of the number of knots in the Weibull spline model in the simple case with no covariates. Stage two examines the results of inferences from the Weibull splines model with fixed covariates. Stage three compares the results of inferences for parameters in the extended Cox model and two simple parametric models with time-dependent covariates. Finally, stage four examines the Weibull splines model with time-dependent covariates.
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Qian, Chunlin. "Time-Dependent Covariates in a General Survival Model With Any Finite Number of Intermediate and Final Events /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487931512617614.

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Chen, I.-Chen. "Improved Methods and Selecting Classification Types for Time-Dependent Covariates in the Marginal Analysis of Longitudinal Data." UKnowledge, 2018. https://uknowledge.uky.edu/epb_etds/19.

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Generalized estimating equations (GEE) are popularly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless an independence working correlation structure is employed. Moreover, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches using the generalized method of moments or quadratic inference functions have been proposed for utilizing all valid moment conditions. However, we have found that such methods will not always provide valid inference and can also be improved upon in terms of finite-sample regression parameter estimation. Therefore, we propose a modified GEE approach and a selection method that will both ensure the validity of inference and improve regression parameter estimation. In addition, these modified approaches assume the data analyst knows the type of time-dependent covariate, although this likely is not the case in practice. Whereas hypothesis testing has been used to determine covariate type, we propose a novel strategy to select a working covariate type in order to avoid potentially high type II error rates with these hypothesis testing procedures. Parameter estimates resulting from our proposed method are consistent and have overall improved mean squared error relative to hypothesis testing approaches. Finally, for some real-world examples the use of mean regression models may be sensitive to skewness and outliers in the data. Therefore, we extend our approaches from their use with marginal quantile regression to modeling the conditional quantiles of the response variable. Existing and proposed methods are compared in simulation studies and application examples.
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Lowther, Alan B. "Development, expansion, and evaluation of release-recapture survival models for Snake River juvenile salmonids, with new algorithms allowing time-dependent individual covariates /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6378.

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Danardono. "Multiple Time Scales and Longitudinal Measurements in Event History Analysis." Doctoral thesis, Umeå : Dept. of Statistics, Umeå Univ, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-420.

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Cortese, Giuliana. "Dynamic models for competing risks and relative survival." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3427193.

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The thesis concerns regression models related to the competing risks setting in survival analysis and deals with both the case of known specific causes and the case of unknown (even if present) specific causes of the event of interest. In the first part, dealing with events whose specific cause is known, competing risks modelling has been applied to a breast cancer study and some of the dynamic aspects such as time-dependent variables are tackled within the context of the application. The aim of the application was to detect an optimal chemotherapy dosage for different typologies of patients with advanced breast cancer in order to control the risk of cardiotoxicity. The attention was concentrated on the cumulative incidence probability of getting cardiotoxicity in a well-defined time period, conditional on risk factors. This probability was estimated as a function of the time-dependent covariate dosage. Within the context of the application, some problems of goodness-of-fit related to time-dependent covariates are discussed. The previous application gave rise to investigating the role of time-dependent covariates in competing risks regression models. There exist various types of time-dependent covariates, which differ in their random or deterministic development in time. For so-called internal covariates, predictions based on the model are not allowed, or they meet with difficulties. We describe a general overview of the state of the art, problems and future directions. Moreover, a possible extension of the competing risks model, that allows us to include a simple random binary time-dependent variable, in a multi-state framework, is presented. Inclusion of the sojourn time of an individual in a certain state as a time-dependent covariate into the model, is also studied. In the second part of the thesis, dealing with events whose specific cause is unavailable, regression models for relative survival are discussed. We study the nonparametric additive excess hazards models, where the excess hazard is on additive form. We show how recent developments can be used to make inferential statements about this models, and especially to test the hypothesis that an excess risk effect is time-varying in contrast to being constant over time. We also show how a semiparametric additive risk model can be considered in the excess risk setting. These two additive models are easy to fit with estimators on explicit form and inference including tests for time-constant effects can be carried out based on a resampling scheme. We analyze a real dataset using different approaches and show the need for more flexible models in relative survival. Finally, we describe a new suggestion for goodness-of-fit of the additive and proportional models for relative survival, which avoids some disadvantages of recent proposals in the literature. The method consists of statistical and graphical tests based on cumulative martingale residuals and it is illustrated for testing the proportional hazards assumption in the semiparametric proportional excess hazards model.
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Books on the topic "Time-dependent covariates"

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Chu, Chi Wing. Semiparametric Inference of Censored Data with Time-dependent Covariates. [New York, N.Y.?]: [publisher not identified], 2021.

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Wilson, Jeffrey R., Elsa Vazquez-Arreola, and (Din) Ding-Geng Chen. Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48904-5.

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Chen, Ding-Geng, Jeffrey R. Wilson, and Elsa Vazquez. Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. Springer, 2020.

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Wilson, Jeffrey R., (Din) Ding-Geng Chen, and Elsa Vazquez-Arreola. Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. Springer International Publishing AG, 2021.

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Book chapters on the topic "Time-dependent covariates"

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Moore, Dirk F. "Time Dependent Covariates." In Use R!, 101–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31245-3_8.

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Slud, Eric V., and Leonid Kopylev. "Dependent Competing Risks with Time-Dependent Covariates." In Lifetime Data: Models in Reliability and Survival Analysis, 323–30. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5654-8_42.

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Beyersmann, Jan, Martin Schumacher, and Arthur Allignol. "Time-dependent covariates and multistate models." In Competing Risks and Multistate Models with R, 211–26. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2035-4_11.

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Lalonde, Trent L. "Modeling Time-Dependent Covariates in Longitudinal Data Analyses." In ICSA Book Series in Statistics, 57–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18536-1_4.

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Ispány, Márton, Valdério A. Reisen, Glaura C. Franco, Pascal Bondon, Higor H. A. Cotta, Paulo R. P. Filho, and Faradiba S. Serpa. "On Generalized Additive Models with Dependent Time Series Covariates." In Time Series Analysis and Forecasting, 289–308. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96944-2_20.

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Galimberti, Giuliano, and Angela Montanari. "Regression Trees for Longitudinal Data with Time-Dependent Covariates." In Classification, Clustering, and Data Analysis, 391–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_43.

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Wu, Lang, Wei Liu, and Hongbin Zhang. "Mixed Effects Models with Measurement Errors in Time-Dependent Covariates." In Handbook of Measurement Error Models, 343–58. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315101279-16.

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Gao, Xiaoming, Michael G. Hudgens, and Fei Zou. "Case-Cohort Studies with Time-Dependent Covariates and Interval-Censored Outcome." In Emerging Topics in Modeling Interval-Censored Survival Data, 221–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12366-5_11.

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Arreola, Elsa Vazquez, and Jeffrey R. Wilson. "Partitioned GMM Marginal Model for Time Dependent Covariates: Applications to Survey Data." In Emerging Topics in Statistics and Biostatistics, 511–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42196-0_22.

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Wu, Feixiang, Yifan Zhou, and Jingjing Liu. "Modelling the Effect of Time-Dependent Covariates on the Failure Rate of Wind Turbines." In Lecture Notes in Mechanical Engineering, 727–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95711-1_71.

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Conference papers on the topic "Time-dependent covariates"

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Rød, B., A. Barabadi, Y. Ayele, D. Lange, D. Honfi, and E. Droguett. "Probabilistic metric of infrastructure resilience considering time-dependent and time-independent covariates." In The 2nd International Conference on Engineering Sciences and Technologies. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315210469-134.

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Brenière, Léa, Laurent Doyen, and Christophe Bérenguer. "Simulation and Parameter Estimation for Virtual Age Models with Time-Dependent Covariates: Methodology and Performance Evaluation." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Singapore: Research Publishing Services, 2019. http://dx.doi.org/10.3850/978-981-11-2724-3_0191-cd.

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Balekelayi, Ngandu, and Solomon Tesfamariam. "Time Dependent Reliability Analysis for Oil and Gas Pipelines: A Bayesian Spectral Analysis-Based Deterioration Model." In 2020 13th International Pipeline Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/ipc2020-9284.

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Abstract Oil and gas pipelines are essential infrastructures that sustain the economy of modern society. They are designed for continuous and reliable operations over their service lives. Once installed, however, their reliability is affected by several threats among which external corrosion plays a significant role. Corrosion-based pit depth growth reduces the wall thickness over time that consequently affect the mechanical strength and the hydraulic performance of the pipeline. Pipeline utility managers rely on the corrosion growth rate models to plan their maintenance, rehabilitation and/or replacement. Existing pipeline deterioration models are mostly based on the power law function that relates the pit depth with the exposure time and rarely include the soil factors that can have effect on the corrosion growth rate. Moreover, the way these factors affect the corrosion rate is complex and cannot be captured with simple linear relationship. This paper uses data found in the literature to build a nonlinear pit depth growth model based on Bayesian spectral analysis regression technique. All continuous covariates are allowed to have smooth nonlinear spectral representations of their effect function on the pit depth growth. The discrete (i.e. categorical) factors are modeled using the ordinary least squared algorithm. The final semiparametric model allows to capture all pit depth measurements, even those difficult to be modeled using high degree polynomials. The stochastic nature of the pit depth growth is captured through the Bayesian approach. A time dependent reliability analysis using subset simulation is carried out to evaluate the changes occurring in the probability of failure of the pipe over time and allow for a better planning and management of these important infrastructure. The model is applied on a bare pipe directly exposed to the soil environment over time. The Bayesian pit depth growth model is accurate enough to allow the computation of the time dependent reliability of pipelines considering both the mechanical and hydraulic reliabilities.
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Harlow, D. Gary. "Fatigue Life Modeling Using Nitinol Data." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21752.

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Abstract Nitinol is a shape memory alloy that has become very popular for medical applications. Specifically, Nitinol tubing is used as peripheral stents, cardio stents and other medical implant devices. Medical implants subjected to blood pressure experience approximately 40 million cycles per year, which necessitates a life expectancy of at least 600 million cycles. Consequently, modeling the fatigue life of Nitinol is critical because failure could result in severe consequences, if not death. The purpose of this work is to model a rather robust set of fatigue data for Nitinol tubes. A phenomenological distribution function for fatigue life is considered. The form of the distribution function includes a kernel that incorporates time dependent loading, mechanical breakdown, and statistical behavior. The approach is a form of the classical accelerated life model in which covariates are included to account for physical attributes that directly influence lifetime. Because of the generality in the formalism, the model is applicable for a variety of loading conditions. The modeling for the Nitinol data is a combination of traditional stress–life methods with a Weibull distribution function. The proposed approach incorporates stress dependencies in the distribution parameters. Also, the Weibull distribution is assumed to be in the form of a three–parameter distribution rather than the more frequently used two–parameter. To assess the validity of the proposed methodology confidence bounds will be estimated for the data.
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Culliford, David, Matt Johnson, and Lynn Josephs. "Dealing with missing longitudinal FEV1 observations when used as time-dependent covariate data in survival analysis for COPD patients within a regional UK population-level database." In Annual Congress 2015. European Respiratory Society, 2015. http://dx.doi.org/10.1183/13993003.congress-2015.pa5072.

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Batista, Bernardo Pinheiro de Senna Nogueira, Suzana Sales de Aguiar, Ana Carolina Padula Ribeiro Pereira, Rosalina Jorge Koifman, and Anke Bergmann. "IMPACT OF BREAST RECONSTRUCTION ON MORTALITY AFTER BREAST CANCER: SURVIVAL ANALYSIS IN A COHORT OF 620 CONSECUTIVE PATIENTS." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2094.

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Background: Access to breast reconstruction is a complex and poorly understood aspect of survival. In the United States, although the rate of immediate reconstruction has tripled in the past 20 years, less than 40% of women undergoing a mastectomy will do so as part of the same procedure. Although there is common understanding that breast reconstruction is oncologically safe, published data on its impact on survival show conflicting and unjustified observations. Methods: We performed a secondary survival analysis in a fixed cohort of 620 consecutive patients who underwent mastectomy between August 2001 and November 2002 in a publicly financed tertiary cancer center. Results: Median followup was 118.4 months (6–172). Of the 620 patients, 253 (40.8%) died during follow-up. And 94 (15.2%) patients underwent breast reconstruction. An unadjusted Cox regression model with breast reconstruction as a time-dependent covariate showed a 60% reduction in the risk of death for patients who underwent reconstruction (crude HR=0.4; 95%CI 0.25–0.65; p <0.001). When adjusted for potential confounders registered in the primary study, the risk reduction was 44% (adjusted HR=0.56; 95%CI 0.34–0.92; p=0.02). Conclusion: Access to breast reconstruction is associated with better survival after mastectomy. Although encouraging, these observations lack biological plausibility and inferences, suggesting that any causal effect is probably driven by confounding and/or interaction with unmeasured variables. The magnitude of the observed association, however, might suggest that, in settings where access to breast reconstruction is severely limited, patient selection for breast reconstruction could be an important drive of the observed association.
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Porta, Micaela, Bruno Leban, and Massimiliano Pau. "Simultaneous assessment of upper limb usage and sedentary behavior time among white- and blue-collar workers using wrist-worn accelerometers." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001479.

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The use of wrist-worn accelerometers to perform assessment of physical activity features and posture recognition, has significantly increased in the last decades, but remains limited in ergonomic contexts. In particular, to our knowledge, no studies employed them to investigate symmetry of use of upper limb (UL) during actual work shifts, even though such information would be useful to identify potentially unbalanced use of dominant and non-dominant limb. In the present study we aimed to estimate intensity and symmetry of use of UL while, at the same time, analyzing the amount of time spent in sedentary behavior in workers engaged in physically demanding and sedentary tasks.2.MethodsTwenty-two full-time workers employed in a metalworking company were recruited for the study and divided into two groups (n=11 each) according to the task they usually performed as follows:1)Machine tools operators, who are required to perform several kinds of machining processes such as cutting, turning, milling, etc.2)Administrative staff, who spend most of their shift time on a desk, in a sitting position using a PC, mouse and keyboard. Upper limb activity was measured for 4 consecutive hours of a regular working shift using two wrist-worn tri-axial accelerometers (Actigraph GT3X-BT, Acticorp Co., USA). The raw accelerations were processed to calculate the following parameters:a) vector magnitude (VM) counts, a composite measure of the accelerometric counts on the three planes of motion;b) Bilateral Magnitude (BLM), which is the sum of the VM values of dominant and non-dominant limb;c) Use Ratio (UR): is the ratio between the minutes of use calculated for the non-dominant and the dominant limb respectively. UR = 1 indicates an equal use of dominant and non-dominant limb, while UR < 1 (>1) indicates longer periods of use for the dominant (non-dominant) limb;d) Magnitude Ratio (MR) is the natural logarithm of the ratio between the VM counts calculated for the non-dominant and the dominant limbs respectively. A value of MR = 0 indicates perfect symmetric use of both limbs in terms of movement intensity. MR < 0 (> 0) denotes higher intensity activity of the dominant (non-dominant) limb;e) Time spent in sedentary (sitting) behaviour calculated according to the procedure proposed by Straczkiewicz et al. (2020)We performed one-way MANCOVA and ANCOVA using the number of steps as covariate because the arm swing associated with walking represents a source of accelerometric counts. The independent variable was the group (i.e. machine tools operator or administrative staff), while the dependent variables were: 1.The three UL activity parameters (i.e., BLM, MR and UR); 2.The time spend in sedentary (sitting) behavior.The level of significance was set at p = 0.05 and the effect of size was assessed using the eta-squared coefficient. Univariate ANOVAs were carried out as a post-hoc test on the adjusted group means.3.ResultsAfter controlling for number of steps, MANCOVA detected a significant main effect of group on UL activity and symmetry parameters [F(3,17) = 5.512; p = 0.008 Wilks’ λ = 0.507; η2 = 0.493]. In particular, the follow-up analysis revealed that machine tool operators performed a more asymmetrical activity in favor of their dominant limb with respect to those engaged in office tasks both in terms of intensity (MR = -0.18 vs. -0.02, p=0.004) and minutes of use (UR = 0.89 vs. 0.99, p=0.001). As regards the sedentary behavior, the ANCOVA revealed that the administrative staff spent significantly longer time in sitting position with respect to machine tools workers (158 minutes vs. 70, p=0.021). This value represents approximately 66% of the monitoring period.4.Discussion and conclusionThe results obtained from the experimental analysis identified the existence of significant asymmetry in the machine tools workers in terms of both duration of UL use and activity intensity. In particular, their markedly higher intensity of use of dominant limb is probably due to the fact that during activities such as cutting, turning, milling, etc. the dominant arm tends to perform dynamic tasks, while the non-dominant is devoted more to stabilizing position by contrasting the forces imposed by the dominant limb. Also, as expected, they spend little time in sitting position (30% of the monitoring period) compared with administrative staff, which perform a typical sedentary work. The findings of the present study, although carried out on a restricted sample in terms of working activities and number of subjects tested, suggest that accelerometer-based data allow discriminating among important features of different job occupations, at the same time highlighting potentially harmful conditions associated with the asymmetrical use of the dominant and non-dominant limbs. This can be extremely important in properly planning suitable ergonomic interventions.
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