Relevant bibliographies by topics / Parametric survival models

Academic literature on the topic 'Parametric survival models'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Parametric survival models"

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Wey, Andrew, John Connett, and Kyle Rudser. "Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models." Biostatistics 16, no. 3 (February 5, 2015): 537–49. http://dx.doi.org/10.1093/biostatistics/kxv001.

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H, Alexis Selvaraj. "Non-Parametric Survival Models." INTERNATIONAL JOURNAL OF COMPUTING ALGORITHM 3, no. 3 (December 31, 2014): 232–35. http://dx.doi.org/10.20894/ijcoa.101.003.003.016.

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Liu, Xing-Rong, Yudi Pawitan, and Mark Clements. "Parametric and penalized generalized survival models." Statistical Methods in Medical Research 27, no. 5 (September 1, 2016): 1531–46. http://dx.doi.org/10.1177/0962280216664760.

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We describe generalized survival models, where g( S( t| z)), for link function g, survival S, time t, and covariates z, is modeled by a linear predictor in terms of covariate effects and smooth time effects. These models include proportional hazards and proportional odds models, and extend the parametric Royston–Parmar models. Estimation is described for both fully parametric linear predictors and combinations of penalized smoothers and parametric effects. The penalized smoothing parameters can be selected automatically using several information criteria. The link function may be selected based on prior assumptions or using an information criterion. We have implemented the models in R. All of the penalized smoothers from the mgcv package are available for smooth time effects and smooth covariate effects. The generalized survival models perform well in a simulation study, compared with some existing models. The estimation of smooth covariate effects and smooth time-dependent hazard or odds ratios is simplified, compared with many non-parametric models. Applying these models to three cancer survival datasets, we find that the proportional odds model is better than the proportional hazards model for two of the datasets.
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TSIATIS, A. A., H. BOUCHER, and K. KIM. "Sequential methods for parametric survival models." Biometrika 82, no. 1 (March 1, 1995): 165–73. http://dx.doi.org/10.1093/biomet/82.1.165.

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Adeboye, Nureni Olawale, Ilesanmi A. Ajibode, and Olubisi L. Aako. "On the Survival Assessment of Asthmatic Patients Using Parametric and Semi-Parametric Survival Models." Occupational Diseases and Environmental Medicine 08, no. 02 (2020): 50–63. http://dx.doi.org/10.4236/odem.2020.82004.

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Hjort, Nils Lid. "On Inference in Parametric Survival Data Models." International Statistical Review / Revue Internationale de Statistique 60, no. 3 (December 1992): 355. http://dx.doi.org/10.2307/1403683.

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Gutierrez, Roberto G. "Parametric Frailty and Shared Frailty Survival Models." Stata Journal: Promoting communications on statistics and Stata 2, no. 1 (March 2002): 22–44. http://dx.doi.org/10.1177/1536867x0200200102.

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Hemming, K., and J. E. H. Shaw. "A Class of Parametric Dynamic Survival Models." Lifetime Data Analysis 11, no. 1 (March 2005): 81–98. http://dx.doi.org/10.1007/s10985-004-5641-5.

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Slud, Eric V., and Jiraphan Suntornchost. "Parametric survival densities from phase-type models." Lifetime Data Analysis 20, no. 3 (August 22, 2013): 459–80. http://dx.doi.org/10.1007/s10985-013-9278-0.

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Jakobsen, Lasse Hjort, Martin Bøgsted, and Mark Clements. "Generalized parametric cure models for relative survival." Biometrical Journal 62, no. 4 (January 20, 2020): 989–1011. http://dx.doi.org/10.1002/bimj.201900056.

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Dissertations / Theses on the topic "Parametric survival models"

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Hemming, Karla. "Parametric dynamic survival models." Thesis, University of Warwick, 2000. http://wrap.warwick.ac.uk/73391/.

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A non-proportional hazards model is developed. The model can accommodate right censored, interval censored and double interval censored data sets. There is also an extension of the model to include multiplicative gamma frailties. The basic model is an extension of the dynamic Bayesian survival model developed by Gamerman (1987), but with some alterations and using a different method of model fitting. The model developed here, the Normal Dynamic Survival Model, models both the log-baseline hazard and covariate effects by a piecewise constant and correlated process, based on some division of the time axis. Neighbouring piecewise constant parameters are related by a simple evolution equation: normal with mean zero and unknown variance to be estimated. The method of estimation is to use Markov chain Monte Carlo simulations: Gibbs sampling with a Metropolis-Hastings step. For double interval censored data an iterative data augmentation procedure is considered: exploiting the comparative ease at which interval censored observations may be modelled. The model is applied within a range of well known, and illustrative data sets, with convincing results. In addition the impact of censoring is investigated by a simulation study.
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Gong, Zhaojing. "Parametric Potential-Outcome Survival Models for Causal Inference." Thesis, University of Canterbury. Mathematics and Statistics, 2008. http://hdl.handle.net/10092/1803.

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Estimating causal effects in clinical trials is often complicated by treatment noncompliance and missing outcomes. In time-to-event studies, estimation is further complicated by censoring. Censoring is a type of missing outcome, the mechanism of which may be non-ignorable. While new estimates have recently been proposed to account for noncompliance and missing outcomes, few studies have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment. In this thesis, we develop a series of parametric potential-outcome (PPO) survival models, for the analysis of randomised controlled trials (RCT) with time-to-event outcomes and noncompliance. Both ignorable and non-ignorable censoring mechanisms are considered. We approach model-fitting from a likelihood-based perspective, using the EM algorithm to locate maximum likelihood estimators. We are not aware of any previous work that addresses these complications jointly. In addition, we give new formulations for the average causal effect (ACE) and the complier average causal effect (CACE) to suit survival analysis. To illustrate the likelihood-based method proposed in this thesis, the HIP breast cancer trial data \citep{Baker98, Shapiro88} were re-analysed using specific PPO-survival models, the Weibull and log-normal based PPO-survival models, which assume that the failure time and censored time distributions both follow Weibull or log-normal distributions. Furthermore, an extended PPO-survival model is also derived in this thesis, which permits investigation into the impact of causal effect after accommodating certain pre-treatment covariates. This is an important contribution to the potential outcomes, survival and RCT literature. For comparison, the Frangakis-Rubin (F-R) model \citep{Frangakis99} is also applied to the HIP breast cancer trial data. To date, the F-R model has not yet been applied to any time-to-event data in the literature.
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Nwi-Mozu, Isaac. "Robustness of Semi-Parametric Survival Model: Simulation Studies and Application to Clinical Data." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3618.

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An efficient way of analyzing survival clinical data such as cancer data is a great concern to health experts. In this study, we investigate and propose an efficient way of handling survival clinical data. Simulation studies were conducted to compare performances of various forms of survival model techniques using an R package ``survsim". Models performance was conducted with varying sample sizes as small ($n5000$). For small and mild samples, the performance of the semi-parametric outperform or approximate the performance of the parametric model. However, for large samples, the parametric model outperforms the semi-parametric model. We compared the effectiveness and reliability of our proposed techniques using a real clinical data of mild sample size. Finally, systematic steps on how to model and explain the proposed techniques on real survival clinical data was provided.
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Tahir, Muhammad-Ramzan. "On validation of parametric models applied in survival analysis and reliability." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14547/document.

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Le premier objectif de la thèse est de présenter un test d'ajustement pour les modèles paramétriques couramment utilisés en l'analyse de survie, la fiabilité, les sciences sociales, l'ingénierie, la santé publique et la démographie, en présence de censure à droite. Nous développons un logiciel en langue R pour les modèles paramétrique. Le modèle de Birnbaum-Saunders (BS) est utilisé pour la test d'ajustement pour les modèles AFT paramétriques et en analyse de système redondant. L'autre contribution porte sur l'analyse de système redondant composé avec une composante en état hot et l'autre en réserve fonctionnent en état warm pour augmenter la fiabilité de systeme. Nous calculons la fiabilité du système en termes de Fonction de répartition et nous donnons l'intervalle de confiance asymptotique
This is an increasing importance in survival analysis and reliability to select a suitable basic model for further inquiries of the data. Little deviation in basic model can cause serious problems in final results. The presence of censoring and accelerated stresses make this task more difficult. Chi-square type goodness of fit tests are most commonly used for model selection. Many modifications in chi-square tests have been proposed by various researcher. The first aim of the thesis is to present a goodness of fit test for wide rage of parametric models (shape-scale families) commonly used in survival analysis, social sciences, engineering, public health and demography, in presence of right censoring. We give the explicit forms of the quadratic form of the test statistic (NRR test) for various models and apply the test on real data. We develop a computer program in R-language for all models. A separate section is dedicated for the test in demography. We focus on the Birnbaum-Saunders (BS) distribution for goodness of fit test for parametric AFT-model and analysis of redundant system.The other purpose of the thesis is to give the analysis of redundant system. To ensure high reliability of the main components of the systems, standby units are used. The main component is replaced by the standby unit automatically, if it fails. The standby unit can be in warm, hot, or cold state. We give the procedure of one main and (n-1) standby units placed in hot state, and give the detailed analysis of one main and one standby unit using BS parametric family. We use Sedyakin's physical principal and the approach of accelerated failure time model for the analysis of redundant system. This approach is different from the traditional ones in the literature but difficulties in calculations. We calculate the reliability of the system in terms of distribution function (unreliability function) and asymptotic confidence interval
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Läuter, Henning. "Estimation in partly parametric additive Cox models." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5150/.

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The dependence between survival times and covariates is described e.g. by proportional hazard models. We consider partly parametric Cox models and discuss here the estimation of interesting parameters. We represent the ma- ximum likelihood approach and extend the results of Huang (1999) from linear to nonlinear parameters. Then we investigate the least squares esti- mation and formulate conditions for the a.s. boundedness and consistency of these estimators.
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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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Masiulaitytė, Inga. "Regression and degradation models in reliability theory and survival analysis." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100527_134956-15325.

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In doctoral thesis redundant systems and degradation models are considered. To ensure high reliability of important elements of the system, the stand-by units can be used. These units are commuted and operate instead of the main failed unit. The stand-by units can function in the different conditions: “hot”, “cold” or “warm” reserving. In the thesis systems with “warm” stand-by units are analyzed. Hypotheses of smooth commuting are formulated and goodness-of-fit tests for these hypotheses are constructed. Nonparametric and parametric point and interval estimation procedures are given. Modeling and statistical estimation of reliability of systems from failure time and degradation data are considered.
Daktaro disertacijos tyrimo objektai yra rezervuotos sistemos ir degradaciniai modeliai. Norint užtikrinti svarbių sistemos elementų aukštą patikimumą, naudojami jų rezerviniai elementai, kurie gali būti įjungiami sugedus šiems pagrindiniams elementams. Rezerviniai elementai gali funkcionuoti skirtinguose režimuose: „karštame“, „šaltame“ arba „šiltame“. Disertacijoje yra nagrinėjamos sistemos su „šiltai“ rezervuotais elementais. Darbe suformuluojama rezervinio elemento „sklandaus įjungimo“ hipotezė ir konstruojami statistiniai kriterijai šiai hipotezei tikrinti. Nagrinėjami neparametrinio ir parametrinio taškinio bei intervalinio vertinimo uždaviniai. Disertacijoje nagrinėjami pakankamai bendri degradacijos modeliai, kurie aprašo elementų gedimų intensyvumą kaip funkciją kiek naudojamų apkrovų, tiek ir degradacijos lygio, kuri savo ruožtu modeliuojama naudojant stochastinius procesus.
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Čabla, Adam. "Odhady v analýze přežívání." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-17134.

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This thesis introduces methods used in time-to-date analysis. It is written generally and so usable in dealing with any example. The thesis deals with problem of censoring, which means, that some observations occurred after the following, which is typical for the lifetime analysis. Methods mentioned in the thesis are nonparametric and parametric estimates of the survival function and their characteristics, and regression models, concretely Cox model and accelerated failure time model, which examine effect of the covariates on survival function. In the thesis is beside survival function presented hazard function, which express intensity of the analyzed event and cumulative hazard function, which is created as the name suggests by cumulative summation of the hazard function. Estimates of these functions are obtainable from survival function and for parametric estimate often exists formula resulting from parameters of used distribution. Empirical part of the thesis introduces influence of several different types and degrees of censoring on parametric and nonparametric estimates of the survival function, mean and median. The other empirical example is the usage of regression analysis on the data from the lungs cancer research made by Mayo Clinic.
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Islam, Sarwar. "Development of flexible parametric models for competing risks and tools to facilitate in the understanding and communication of cancer survival." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42864.

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In population-based cancer studies, researchers are often only interested in cancer-specific survival to determine variations in the impact of cancer in different population groups. In such cases, the net survival measure is usually reported. However, this is of little relevance for patients as it does not consider the probability of dying from other causes before dying from cancer, otherwise known as competing risks. Therefore, alternative measures that take this into account are required for a better representation of cancer survival in the real-world. Measures estimated from within this framework provide a more meaningful interpretation for patients which can be communicated to facilitate treatment-related decisions. Differences in interpretation between various cancer survival measures, and when they are appropriate, has led to some confusion amongst non-statisticians. This motivates the development of publicly available tools to improve understanding and communication. Thus, an aim of this thesis is to develop an interactive web-tool to aid interpretation of various important cancer survival measures that are commonly reported. Although not a new concept, many often fail to account for competing risks when it is necessary for a study. Even when accounted for, many apply the theory, or report analyses incorrectly. Recently, efforts have been made to make competing risks methods more accessible for researchers from within the increasingly popular flexible parametric modelling framework. However, much work is yet to be done, especially as cancer registry datasets are becoming larger with more detailed covariate information. This means that models are increasing in complexity and more computationally efficient methods are required. With this in mind, the primary aim of this PhD is to further develop competing risks methods from within the flexible parametric modelling framework. Particular focus is on obtaining predictions with less computational effort that facilitate communication of risk when interest is in prognosis.
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Hoglin, Phillip J. "Survival analysis and accession optimization of prior enlisted United States Marine Corps officers." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1673.

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Approved for public release, distribution is unlimited
The purpose of this thesis is to firstly analyze the determinants on the survival of United States Marine Corps Officers, and secondly, to develop the methodology to optimize the accessions of prior and non-prior enlisted officers. Using data from the Marine Corps Officer Accession Career file (MCCOAC), the Cox Proportional Hazards Model is used to estimate the effects of officer characteristics on their survival as a commissioned officer in the USMC. A Markov model for career transition is combined with fiscal data to determine the optimum number of prior and non-prior enlisted officers under the constraints of force structure and budget. The findings indicate that prior enlisted officers have a better survival rate than their non-prior enlisted counterparts. Additionally, officers who are married, commissioned through MECEP, graduate in the top third of their TBS class, and are assigned to a combat support MOS have a better survival rate than officers who are unmarried, commissioned through USNA, graduate in the middle third of their TBS class, and are assigned to either combat or combat service support MOS. The findings also indicate that the optimum number of prior enlisted officer accessions may be considerably lower than recent trends and may differ across MOS. Based on the findings; it is recommended that prior enlisted officer accession figures be reviewed.
Major, Australian Army
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Books on the topic "Parametric survival models"

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Hemming, Karla. Parametric dynamic survival models. [s.l.]: typescript, 2000.

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Royston, Patrick. Flexible parametric survival analysis using Stata: Beyond the Cox model. College Station, TX: Stata Press, 2011.

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C, Lambert Paul, ed. Flexible parametric survival analysis using Stata: Beyond the Cox model. College Station, TX: Stata Press, 2011.

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Balakrishnan, N., M. S. Nikulin, M. Mesbah, and N. Limnios, eds. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4.

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Golub, Jonathan. Survival Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0023.

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This article provides a discussion of survival analysis that presents another way to incorporate temporal information into analysis in ways that give advantages similar to those from using time series. It describes the main choices researchers face when conducting survival analysis and offers a set of methodological steps that should become standard practice. After introducing the basic terminology, it shows that there is little to lose and much to gain by employing Cox models instead of parametric models. Cox models are superior to parametric models in three main respects: they provide more reliable treatment of the baseline hazard and superior handling of the proportional hazards assumption, and they are the best for handling tied data. Moreover, the illusory benefits of parametric models are presented. The greater use of Cox models enables researchers to elicit more useful information from their data, and allows for more reliable substantive inferences about important political processes.
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Nikulin, M. S., N. Balakrishnan, and Mounir Mesbah. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Springer, 2011.

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Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Birkhäuser, 2012.

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Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation. It is known that maximum likelihood estimation can still be used provided the range of τ‎ is restricted. It is shown that the alternative maximum product of spacings method is consistent without restriction on τ‎. Censored observations which commonly occur in survival-time data can be accounted for using Kaplan-Meier estimation. A real data numerical example is given.
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(Editor), Mikhail S. Nikulin, N. Balakrishnan (Editor), M. Mesbah (Editor), and Nikolaos Limnios (Editor), eds. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life (Statistics for Industry and Technology). Birkhäuser Boston, 2004.

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Book chapters on the topic "Parametric survival models"

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Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. "Parametric Models." In Bayesian Survival Analysis, 30–46. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8_2.

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Nag, Avishek. "Parametric Models." In Survival Analysis with Python, 21–36. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003255499-3.

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Harrell, Frank E. "Parametric Survival Models." In Regression Modeling Strategies, 423–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_18.

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Harrell, Frank E. "Parametric Survival Models." In Regression Modeling Strategies, 413–42. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_17.

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Kleinbaum, David G., and Mitchel Klein. "Parametric Survival Models." In Statistics for Biology and Health, 289–361. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6646-9_7.

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Nag, Avishek. "Non-Parametric Models." In Survival Analysis with Python, 37–54. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003255499-4.

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Hanagal, David D. "Some Parametric Models." In Modeling Survival Data Using Frailty Models, 19–47. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1181-3_2.

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Broniatowski, Michel, and Virgile Caron. "Conditional Inference in Parametric Models." In Statistical Models and Methods for Reliability and Survival Analysis, 125–43. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118826805.ch9.

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Collett, D. "Some other parametric models for survival data." In Modelling Survival Data in Medical Research, 199–222. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3115-3_6.

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Hougaard, Philip. "Multivariate Interval-Censored Survival Data: Parametric, Semi-parametric and Non-parametric Models." In Contributions to Statistics, 9–21. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04579-5_2.

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Conference papers on the topic "Parametric survival models"

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Nguyễn, Thông T., and Siu Cheung Hui. "Privacy Protection for Flexible Parametric Survival Models." In CIKM '18: The 27th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3269206.3271716.

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Asnaashari, Ahmad, Isam Shahrour, Bahram Gharabaghi, and Edward McBean. "Parametric and Non-Parametric Survival Models for “Time to Failure” of Water Pipelines: Case Study." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64071.

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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.
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Seungyeoun Lee, Jinseok Oh, Min-Seok Kwon, and Taesung Park. "Gene-gene interaction analysis for the survival phenotype based on the standardized residuals from parametric regression models." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112460.

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Kadooka, Kevin, James Fitzpatrick, and Nicholas Klymyshyn. "Large-Scale Parametric Modeling of Spent Nuclear Fuel Dynamics in the 30 cm Package Drop Scenario." In ASME 2022 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/pvp2022-84747.

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Abstract Packages used to transport spent nuclear fuel (SNF) are required by the U.S. Code of Federal Regulations 10 CFR 71.71 to demonstrate satisfactory performance during a drop scenario. While the CFR is meant to ensure safe package function, it does not evaluate survival of the SNF within. The U.S. Department of Energy Spent Fuel and Waste Science and Technology program is working on closing the knowledge gap related to the response of SNF to external mechanical loads, including the 30 cm package drop scenario in the CFR. In support of this effort, LS-DYNA finite element simulations were developed by Pacific Northwest National Laboratory (PNNL) to model generic drop scenarios at both the package and fuel assembly level. The models were validated against one-third scale package and full scale fuel assembly drop test data and were exercised to predict fuel cladding strains in a narrow range of model configurations. This work describes a large-scale parametric study conducted by PNNL using the previously developed and validated PWR finite element model, with the addition of a new generic BWR assembly model. The motivation for the parametric study was to characterize the broad range of SNF responses in the 30 cm package drop scenario. This was accomplished by varying the drop orientation, fuel assembly type (17 × 17 PWR and 10 × 10 BWR), burnup, cladding temperature, spacer grid buckling load, package mass, impact limiter stiffness, and mechanical gap conditions within the basket. A MATLAB framework was developed to automate LS-DYNA model generation and execution on PNNL institutional computing resources. In total, over 2000 simulations were performed. For each simulation, the SNF response was quantified in terms of permanent grid deformation, fuel rod contact pressure, and strains within the fuel rods, guide tubes, and water rods. The results provide valuable insight into the range of responses that could be reasonably expected from SNF in the 30 cm drop scenario, as well as the sensitivity to each input parameter. The results of this parametric study are a key component of the testing and modeling strategy the Spent Fuel and Waste Science and Technology program is using to close the external loads knowledge gap.
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Ahn, Kwang-Won, Yoonik Kim, Chang-Hyun Chung, and Kil-Yoo Kim. "Development of Reliability Function and Failure Rate Considering Information of Censored Data." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22248.

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Generally, there are two methods to estimate reliability of system or component failure in engineering, or survival function of patient death in medicine: 1) parametric and 2) non-parametric methods. The parametric method has been used widely since there is few data on severe accidents in nuclear power plants. However, if we have a lot of data on system failure or patient death, the non-parametric method could be used to estimate reliability or survival function. In this paper we focus on the latter case in which one problem arises in treating the data. The problem is how to reflects efficiently the censoring information into reliability or survival function. In this study, two works have been done: 1) develop the model to reflect censoring information and 2) show how efficient the model is using the Monte-Carlo Simulation. In the WASH-1400 (NUREG-75/014), Failure to Operate of Motor Operated Valves from Nuclear Experience is suggested 1×10−3/d. The new developed method can estimate closer to the failure rate 1×10−3/d than the other existing methods. Also the failure rate using the new method satisfies the conservative limit.
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Xiao, Daiquan, Xuecai Xu, Quan Yuan, and Changxi Ma. "Preventing Pedestrian Injury Severity at Signalized Intersections: A Bayesian Multilevel Parametric Survival Model." In 2021 6th International Conference on Transportation Information and Safety (ICTIS). IEEE, 2021. http://dx.doi.org/10.1109/ictis54573.2021.9798411.

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Noshi, Christine Ikram, and Mahmood Amani. "Casing String Fatigue: No More." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31970-ms.

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Abstract Casing strings are an indispensable component in the design of any well and serve numerous purposes in oil and gas wells, constituting 20-30% of the total well cost. An alarming rate of failures, up to 50%, have been observed yet hushed under the rug, due to reputation, company profile, and privacy. In this work, The designed workflow follows 6 acceptance criteria, namely, (1) identification of potential risk factors amongst different exposures, (2) evaluation of the type and magnitude of the impact for each risk factor, (3) identification of the levels within each potential risk factor that impose the highest risk on casing failure, (4) acknowledgement of the depths most susceptible to casing failure, (5) prediction of the overall probability of casing failure given the information for pre-defined risk factors, and finally (6) have a scheme for mitigating casing failure. Case-control study design was adopted to test the association between the different exposures and the occurrence of casing failure. Impact type and magnitude of identified risk factors were determined through various statistical association measurements. Non-parametric survival analysis techniques were used for identification of the levels within each potential risk factor that impose the highest risk on casing failure and the depths most susceptible to casing failure. The scheme provided quantifiable numeric percentage increase/decrease for significant risk factors at lower, intermediate, and higher depth of casing. One importance of such conclusions is that although the conclusions coincide with already proven theories, unlike physics-based models, we did not need to acquire any domain knowledge to reach to those outputs. Moreover, in addition to exploring the significance of subcategories with respect to imposed risk, we managed to quantify those impacts which would be of great value when calibrating malpractices later using the proposed "correction-prediction" procedure.
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Noshi, Christine Ikram, and Mahmood Amani. "Casing String Fatigue: No More." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31970-ms.

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Abstract Casing strings are an indispensable component in the design of any well and serve numerous purposes in oil and gas wells, constituting 20-30% of the total well cost. An alarming rate of failures, up to 50%, have been observed yet hushed under the rug, due to reputation, company profile, and privacy. In this work, The designed workflow follows 6 acceptance criteria, namely, (1) identification of potential risk factors amongst different exposures, (2) evaluation of the type and magnitude of the impact for each risk factor, (3) identification of the levels within each potential risk factor that impose the highest risk on casing failure, (4) acknowledgement of the depths most susceptible to casing failure, (5) prediction of the overall probability of casing failure given the information for pre-defined risk factors, and finally (6) have a scheme for mitigating casing failure. Case-control study design was adopted to test the association between the different exposures and the occurrence of casing failure. Impact type and magnitude of identified risk factors were determined through various statistical association measurements. Non-parametric survival analysis techniques were used for identification of the levels within each potential risk factor that impose the highest risk on casing failure and the depths most susceptible to casing failure. The scheme provided quantifiable numeric percentage increase/decrease for significant risk factors at lower, intermediate, and higher depth of casing. One importance of such conclusions is that although the conclusions coincide with already proven theories, unlike physics-based models, we did not need to acquire any domain knowledge to reach to those outputs. Moreover, in addition to exploring the significance of subcategories with respect to imposed risk, we managed to quantify those impacts which would be of great value when calibrating malpractices later using the proposed "correction-prediction" procedure.
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Mohammed, Yusuf Abbakar, Bidin Yatim, and Suzilah Ismail. "A parametric mixture model of three different distributions: An approach to analyse heterogeneous survival data." In PROCEEDINGS OF THE 21ST NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM21): Germination of Mathematical Sciences Education and Research towards Global Sustainability. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4887734.

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Mao, L., H. S. Udaykumar, and J. O. M. Karlsson. "Direct Numerical Simulation of Micro-Scale Interaction Between Ice and Biological Cells." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32679.

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The freezing of biological material, such as cells and tissue, duing cryopreservation involves the interaction of ice in the extracellular medium with living cells. This cell-ice interaction critically determines the success of the cryo-preservation protocol, as measured by cell survival and viability after the freeze-thaw process. This paper presents numerical simulations of the response of a cell to freezing. The phase change of the aqueous salt solution outside the cell is computed using a sharp-interface technique. The cell is modeled as a salt solution enclosed by a semi-permeable membrane. We compute the concentration and temperature fields around a single cell in the presence of extracellular ice formation. Parametric variations in the factors affecting the cell-ice interaction are performed to describe the physics of thermo-solutal transport of the interaction. Cell water loss is quantified. The external ice front is computed for both stable and unstable (cellular/dendritic) growth modes. The results show that water egress from the cell is dependent on several controlling parameters in complex ways.
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