Relevant bibliographies by topics / Cox-Snell residuals

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Academic literature on the topic 'Cox-Snell residuals'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Cox-Snell residuals"

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Rumyantseva, Ekaterina, and Kirill Furmanov. "Using out-of-sample Cox–Snell residuals in time-to-event forecasting." Business Informatics 15, no. 1 (2021): 7–18. http://dx.doi.org/10.17323/2587-814x.2021.1.7.18.

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The problem of assessing out-of-sample forecasting performance of event-history models is considered. Time-to-event data are usually incomplete because the event of interest can happen outside the period of observation or not happen at all. In this case, only the shortest possible time is observed and the data are right censored. Traditional accuracy measures like mean absolute or mean squared error cannot be applied directly to censored data, because forecasting errors also remain unobserved. Instead of mean error measures, researchers use rank correlation coefficients: concordance indices by
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Hashemian, A. H., M. Garshasbi, M. A. Pourhoseingholi, and S. Eskandari. "A Comparative Study of Cox Regression vs. Log-Logistic Regression (with and without its frailty) in Estimating Survival Time of Patients with Colorectal Cancer." Journal of Medical and Biomedical Sciences 6, no. 1 (2017): 35–43. http://dx.doi.org/10.4314/jmbs.v6i1.5.

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Colorectal cancer is common and lethal disease with different incidence rate in different parts of the world which is taken into account as the third cause of cancer-related deaths. In the present study, using non-parametric Cox model and parametric Log-logistic model, factors influencing survival of patients with colorectal cancer were evaluated and the models efficiency were compared to provide the best model. This study is conducted on medical records of 1,127 patients with colorectal cancer referred to Taleghani Medical and Training Center of Tehran between 2001 - 2007 and were definitely
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Nur Niswah Naslina, Azid Maarof, Arasan Jayanthi, Zulkafli Hani Syahida, and Adam Mohd Bakri. "Assessing the Goodness of Fit of the Gompertz Model in the Presence of Right and Interval Censored Data with Covariate." Austrian Journal of Statistics 49, no. 3 (2020): 57–71. http://dx.doi.org/10.17713/ajs.v49i3.1085.

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This research focuses on assessing the goodness of fit for the Gompertz model in the presence of right and interval censored data with covariate. The performance of the maximum likelihood estimates was evaluated via a simulation study at various censoring proportions and sample sizes. The conclusions were drawn based on the results of bias, standard error and root mean square error at different settings. Following that, another simulation study was carried out to compare the performance of the proposed modifications to the Cox-Snell residuals for both censored and uncensored observations at di
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Al-Khabori, Murtadha K., Gordon Guyatt, Mark D. Minden, et al. "A Prognostic Score of Overall Survival In Adults with Acute Myeloid Leukemia." Blood 116, no. 21 (2010): 2730. http://dx.doi.org/10.1182/blood.v116.21.2730.2730.

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Abstract Abstract 2730 Background: Acute myeloid leukemia (AML) is a malignant myeloid disorder with heterogeneous outcomes. A number of factors have been shown to be prognostic; age, white blood cell (WBC), prior malignancy, performance status (Eastern Cooperative Oncology Group; ECOG) and cytogenetics. Methods: We planned to develop and validate a prognostic score for the 5-year (y) overall survival (OS) of adults with AML receiving intensive induction chemotherapy. We used Cox model to estimate the regression coefficients and Kaplan-Meier to estimate the 5-y OS. We used Cox-Snell, Schoenfel
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GIOLO, Suely Ruiz, and Jaqueline Aparecida RAMINELLI. "SURVIVAL MODELS IN THE CONTEXT OF MULTIPLICATIVE AND ADDITIVE HAZARDS." REVISTA BRASILEIRA DE BIOMETRIA 37, no. 3 (2019): 306. http://dx.doi.org/10.28951/rbb.v37i3.391.

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In survival analysis, multiplicative and additive hazards models provide the two principal frameworks to study the association between the hazard and covariates. When these models are considered for analyzing a given survival dataset, it becomes relevant to evaluate the overall goodness-of-fit and how well each model can predict those subjects who subsequently will or will not experience the event. In this paper, this evaluation is based on a graphical representation of the Cox-Snell residuals and also on a time-dependent version of the area under the receiver operating characteristic (ROC) cu
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Scolas, Sylvie, Catherine Legrand, Abderrahim Oulhaj, and Anouar El Ghouch. "Diagnostic checks in mixture cure models with interval-censoring." Statistical Methods in Medical Research 27, no. 7 (2016): 2114–31. http://dx.doi.org/10.1177/0962280216676502.

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Models for interval-censored survival data presenting a fraction of “cure” or “immune” patients have recently been proposed in the literature, particularly extending the mixture cure model to interval-censored data. However, little is known about the goodness-of-fit of such models. In a mixture cure model, the survival distribution of the entire population is improper and expressed in terms of the survival distribution of uncured individuals, i.e. the latency part of the model, and the probability to experience the event of interest, i.e. the incidence part. To validate a mixture cure model, a
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MEKKI, Reem Yousif, Mohamed Hassan MUDAWI, Manahil Saidahmed MUSTAFA, Altaiyb Omer Ahmed MOHMMED, Ahmed Bakheet Abd ALLA, and Abdel Rahman AHMED. "Parametric Survival Models of Hemodialysis Patients in Relation with Patient-Related Factors." Medicina Moderna - Modern Medicine 27, no. 4 (2020): 295–304. http://dx.doi.org/10.31689/rmm.2020.27.4.295.

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Background: Survival analysis refers to analyzing of statistical data for which the outcome variable of interest is time until an event occurs. This research aimed at comparing different models of parametric Proportional Hazards (PH) models (Weibull, exponential, Gompertz) in patients with hemodialysis to determine the best model for assessing the survival of patient. Study consists of 325 hemodialysis patients who referred to public hospitals in Khartoum state in the period from December 2005 to December 2015. Data was used to estimate the survival function with view to identify risk factors
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Nikpour, Abolfazl, Jamshid Yazdani Charati, Iraj Maleki, Hosien Ranjbaran, and Alireza Khalilian. "Parametric Model Evaluation in Examining the Survival of Gastric Cancer Patients Andits Influencing Factors." Global Journal of Health Science 9, no. 3 (2016): 260. http://dx.doi.org/10.5539/gjhs.v9n3p260.

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<p><strong>BACKGROUND:</strong> Cox proportional hazard model is the most common technique to analysis the variables effect on survival time, but under certain circumstances, parametric models may offer advantages over Cox’s model. In this study we use cox regression and alternative parametric models such as Weibull, exponential, log-normal, logistics and gamma model to evaluate factors affecting survival of patients with gastric cancer. Comparisons were made to find the best model.</p><p><strong>METHOD</strong><strong>:</strong> In this st
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Khanal, Shankar Prasad, V. Sreenivas, and Subrat K. Acharya. "Comparison of Cox Proportional Hazards Model and Lognormal Accelerated Failure Time Model: Application in Time to Event Analysis of Acute Liver Failure Patients in India." Nepalese Journal of Statistics 3 (September 16, 2019): 21–40. http://dx.doi.org/10.3126/njs.v3i0.25576.

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Background: Different survival analysis techniques such as nonparametric, semi-parametric, parametric Accelerated Failure Time (AFT) models have been generally applied to analyze time to event data. In order to identify the prognostic factors for survival of Acute Liver Failure (ALF) patients, previous studies applied Cox Proportional hazards (CPH) model, Lognormal AFT and Log-Logistic AFT model satisfying respective model’s assumptions and goodness of fit of each model. However, comparison of CPH model and AFT model has not been reported so far for ALF data with short follow up time.
 Ob
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Gibson, Steven J., Jennifer A. Thornton, and Christin B. DeStefano. "Multiple Myeloma in Adolescent and Young Adults: A SEER and CIBMTR Analysis." Blood 136, Supplement 1 (2020): 12–13. http://dx.doi.org/10.1182/blood-2020-136399.

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Background Multiple myeloma (MM) is a disease of the elderly, with less than 3% of cases diagnosed in adolescents and young adults (AYA). Data on demographics, use of autologous stem cell transplant (ASCT), second primary malignancies (SPMs) and survival are scant to non-existent in the AYA MM population. To our knowledge, this study is the first to better understand characteristics and survival trends of this unique population. Methods The Surveillance, Epidemiology, and End Results (SEER)-18 and Center for International Blood and Marrow Transplant Research (CIBMTR) datasets were utilized. In
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Dissertations / Theses on the topic "Cox-Snell residuals"

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Ansin, Elin. "An evaluation of the Cox-Snell residuals." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256665.

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It is common practice to use Cox-Snell residuals to check for overall goodness of tin survival models. We evaluate the presumed relation of unit exponentially dis-tributed residuals for a good model t and evaluate under some violations of themodel. This is done graphically with the usual graphs of Cox-Snell residual andformally using Kolmogorov-Smirnov goodness of t test. It is observed that residu-als from a correctly tted model follow unit exponential distribution. However, theCox-Snell residuals do not seem to be sensitive to the violations of the model.
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Book chapters on the topic "Cox-Snell residuals"

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"Survival Function of Cox−Snell Residuals." In Joint Models for Longitudinal and Time-to-Event Data. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b12208-17.

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