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Journal articles on the topic 'Survival regression model'

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Published: 9 March 2023

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1

Adams, Gerald J., and Micah Dial. "Teacher Survival: A Cox Regression Model." Education and Urban Society 26, no. 1 (November 1993): 90–99. http://dx.doi.org/10.1177/0013124593026001008.

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2

Zhang, Zhongheng. "Parametric regression model for survival data: Weibull regression model as an example." Annals of Translational Medicine 4, no. 24 (December 2016): 484. http://dx.doi.org/10.21037/atm.2016.08.45.

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3

Pires, Magda C., Enrico A. Colosimo, and Arlaine A. Silva. "Survival Weibull regression model for mismeasured outcomes." Communications in Statistics - Theory and Methods 47, no. 3 (September 14, 2017): 601–14. http://dx.doi.org/10.1080/03610926.2017.1309434.

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4

Christensen, Erik. "Multivariate survival analysis using Cox's regression model." Hepatology 7, no. 6 (November 1987): 1346–58. http://dx.doi.org/10.1002/hep.1840070628.

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Biglarian, Akbar, Enayatollah Bakhshi, Ahmad Reza Baghestani, Mahmood Reza Gohari, Mehdi Rahgozar, and Masoud Karimloo. "Nonlinear Survival Regression Using Artificial Neural Network." Journal of Probability and Statistics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/753930.

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Survival analysis methods deal with a type of data, which is waiting time till occurrence of an event. One common method to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that effect the appropriateness of the model. One strategy, which is used nowadays frequently, is artificial neural network (ANN) model which needs a minimal assumption. This study aimed to compare predictions of the ANN and Cox models by simulated data sets, which the average censoring rate were considered 20% to 80% in both simple and complex model. All simulations and comparisons were performed by R 2.14.1.
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Mohamed Ahmed Abdelaal, Medhat. "Modeling Survival Data by Using Cox Regression Model." American Journal of Theoretical and Applied Statistics 4, no. 6 (2015): 504. http://dx.doi.org/10.11648/j.ajtas.20150406.21.

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Wuryandari, Triastuti, Sri Haryatmi Kartiko, and Danardono Danardono. "ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF." Jurnal Gaussian 9, no. 4 (December 7, 2020): 402–10. http://dx.doi.org/10.14710/j.gauss.v9i4.29259.

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Survival data is the length of time until an event occurs. If the survival time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of the additive hazard models is the semiparametric additive hazard model that introduced by Lin Ying in 1994. The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration
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8

Peng, Defen, Gilbert MacKenzie, and Kevin Burke. "A multiparameter regression model for interval‐censored survival data." Statistics in Medicine 39, no. 14 (April 24, 2020): 1903–18. http://dx.doi.org/10.1002/sim.8508.

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9

Pandey, Arvind, David D. Hanagal, and Shikhar Tyagi. "Generalised Lindley shared additive frailty regression model for bivariate survival data." Statistics in Transition New Series 23, no. 4 (December 1, 2022): 161–76. http://dx.doi.org/10.2478/stattrans-2022-0048.

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Abstract Frailty models are the possible choice to counter the problem of the unobserved heterogeneity in individual risks of disease and death. Based on earlier studies, shared frailty models can be utilised in the analysis of bivariate data related to survival times (e.g. matched pairs experiments, twin or family data). In this article, we assume that frailty acts additively to the hazard rate. A new class of shared frailty models based on generalised Lindley distribution is established. By assuming generalised Weibull and generalised log-logistic baseline distributions, we propose a new class of shared frailty models based on the additive hazard rate. We estimate the parameters in these frailty models and use the Bayesian paradigm of the Markov Chain Monte Carlo (MCMC) technique. Model selection criteria have been applied for the comparison of models. We analyse kidney infection data and suggest the best model.
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Kusumawardhani, Gatri Eka, Vera Maya Santi, and Suyono Suyono. "Analisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik." Jurnal Statistika dan Aplikasinya 2, no. 2 (December 30, 2018): 28–35. http://dx.doi.org/10.21009/jsa.02204.

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Survival analysis is an analysis used to determine the length of time required by an object in order to survive. That time is sometimes influenced by several factors called independent variables. One way to know relationship is through a regression model. The dependent variable in this regression model is a survival time which is log-logistic distributed. The data used in this study were right censored survival data. Log-logistic regression models for survival data can be expressed by transformation Y=lnT= θ0+θ1xi1+...+θixij+σԑ. The parameter of the log-logistic regression models for right censored survival data are estimated with the maximum likelihood method. In this study, the application of log-logistic regression model for survival data is in data of lung cancer patients. Based on the data already performed, best log-logistic regression model is obtained yi=1.92458+0.0242393 xi1+0.639037ԑi.
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11

Veerkamp, R. F., S. Brotherstone, B. Engel, and T. H. E. Meuwissen. "Analysis of censored survival data using random regression models." Animal Science 72, no. 1 (February 2001): 1–10. http://dx.doi.org/10.1017/s1357729800055491.

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AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.
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12

Quantin, Catherine, Thierry Moreau, Bernard Asselain, Jean Maccario, and Joseph Lellouch. "A Regression Survival Model for Testing the Proportional Hazards Hypothesis." Biometrics 52, no. 3 (September 1996): 874. http://dx.doi.org/10.2307/2533049.

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13

Hashimoto, Elizabeth M., Edwin M. M. Ortega, Gauss M. Cordeiro, and Mauricio L. Barreto. "The Log-Burr XII Regression Model for Grouped Survival Data." Journal of Biopharmaceutical Statistics 22, no. 1 (December 28, 2011): 141–59. http://dx.doi.org/10.1080/10543406.2010.509527.

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14

Ahmad, Yuhaniz, Zakiyah Zain, and Nazrina Aziz. "Multistage Logistic Regression Model for Analyzing Survival from Colorectal Cancer." International Journal of Technology 9, no. 8 (December 30, 2018): 1618. http://dx.doi.org/10.14716/ijtech.v9i8.2764.

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15

Zucker, David M., Sarit Agami, and Donna Spiegelman. "Testing for a Changepoint in the Cox Survival Regression Model." Journal of Statistical Theory and Practice 7, no. 2 (January 2013): 360–80. http://dx.doi.org/10.1080/15598608.2013.772030.

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16

Jiang, Dan, Hongwei Wang, Jiahan Li, Yang Wu, Ming Fang, and Runqing Yang. "Cox regression model for dissecting genetic architecture of survival time." Genomics 104, no. 6 (December 2014): 472–76. http://dx.doi.org/10.1016/j.ygeno.2014.10.002.

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17

YING, Z., L. J. WEI, and J. S. LIN. "Prediction of survival probability based on a linear regression model." Biometrika 79, no. 1 (1992): 205–9. http://dx.doi.org/10.1093/biomet/79.1.205.

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18

Lai, Tze Leung, and Zheng Su. "Confidence intervals for survival quantiles in the Cox regression model." Lifetime Data Analysis 12, no. 4 (October 20, 2006): 407–19. http://dx.doi.org/10.1007/s10985-006-9024-y.

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19

Ramires, Thiago G., Gauss M. Cordeiro, Michael W. Kattan, Niel Hens, and Edwin MM Ortega. "Predicting the cure rate of breast cancer using a new regression model with four regression structures." Statistical Methods in Medical Research 27, no. 11 (February 23, 2017): 3207–23. http://dx.doi.org/10.1177/0962280217695344.

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Cure fraction models are useful to model lifetime data with long-term survivors. We propose a flexible four-parameter cure rate survival model called the log-sinh Cauchy promotion time model for predicting breast carcinoma survival in women who underwent mastectomy. The model can estimate simultaneously the effects of the explanatory variables on the timing acceleration/deceleration of a given event, the surviving fraction, the heterogeneity, and the possible existence of bimodality in the data. In order to examine the performance of the proposed model, simulations are presented to verify the robust aspects of this flexible class against outlying and influential observations. Furthermore, we determine some diagnostic measures and the one-step approximations of the estimates in the case-deletion model. The new model was implemented in the generalized additive model for location, scale and shape package of the R software, which is presented throughout the paper by way of a brief tutorial on its use. The potential of the new regression model to accurately predict breast carcinoma mortality is illustrated using a real data set.
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Silva, Carolina, Cira Otiniano, and Eduardo Nakano. "Modelo de regressao Weibull discreto com fracao de cura em dados de sobrevivencia." Selecciones Matemáticas 6, no. 1 (June 30, 2019): 84–97. http://dx.doi.org/10.17268/sel.mat.2019.01.11.

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21

Li, Kexuan. "Variable Selection for Nonlinear Cox Regression Model via Deep Learning." International Journal of Statistics and Probability 12, no. 1 (December 24, 2022): 21. http://dx.doi.org/10.5539/ijsp.v12n1p21.

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Variable selection problem for the nonlinear Cox regression model is considered. In survival analysis, one main objective is to identify the covariates that are associated with the risk of experiencing the event of interest. The Cox proportional hazard model is being used extensively in survival analysis in studying the relationship between survival times and covariates, where the model assumes that the covariate has a log-linear effect on the hazard function. However, this linearity assumption may not be satisfied in practice. In order to extract a representative subset of features, various variable selection approaches have been proposed for survival data under the linear Cox model. However, there exists little literature on variable selection for the nonlinear Cox model. To break this gap, we extend the recently developed deep learning-based variable selection model LassoNet to survival data. Simulations are provided to demonstrate the validity and effectiveness of the proposed method. Finally, we apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
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22

Lai, Yeuntyng, Morihiro Hayashida, and Tatsuya Akutsu. "Survival Analysis by Penalized Regression and Matrix Factorization." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/632030.

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Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We triedL1- (lasso),L2- (ridge), andL1-L2combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found thatL1-L2combined method predicts survival best with the smallest logrankPvalue. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrankPvalues. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.
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23

N, SUNDARAM. "Parametric regression model for response time in clinical trials – a bayesian approach." Journal of Management and Science 7, no. 1 (June 30, 2017): 1–7. http://dx.doi.org/10.26524/jms.2017.1.

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In this paper an attempt has been made to model the censored survival data using Bayesian regressions with Markov Chain Monte Carlo (MCMC) methods. Bayesian LogNormal (LN) regression model are found to be providing better fit than the other Bayesian regression models namely Exponential (E), Generalized Exponential (GE), Webull (W), LogLogistic (LL) and Gamma (G).
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Badmus, Nofiu Idowu, Mary Idowu Akinyemi, and Josephine Nneamaka Onyeka-Ubaka. "A Log-Beta Rayleigh Lomax Regression Model." Afrika Statistika 16, no. 4 (October 1, 2021): 2993–3007. http://dx.doi.org/10.16929/as/2021.2993.192.

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For the first time, a location-scale regression model based on the logarithm of an extended Raleigh Lomax distribution which has the ability to deal and model of any survival data than classical regression model is introduced. We obtain the estimate for the model parameters using the method of maximum likelihood by considering breast cancer data. In addition, normal probability plot of the residual is used to detect the outliers and evaluate model assumptions. We use a real data set to illustrate the performance of the new model, some of its submodels and classical models consider in the study. Also, we perform the statistics AIC, BIC and CAIC to select the most appropriate model among those regression models considered in the study.
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Mulayath Variyath, Asokan, and P. G. Sankaran. "Parametric Regression Models Using Reversed Hazard Rates." Journal of Probability and Statistics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/645719.

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Proportional hazard regression models are widely used in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we develop a parametric proportional hazard rates model using an inverted Weibull distribution. The estimation and construction of confidence intervals for the parameters are discussed. We assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. We illustrate the proposed method using a real case example.
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Chandra, Novita Eka, and Siti Alfiatur Rohmaniah. "ANALISIS SURVIVAL MODEL REGRESI SEMIPARAMETRIK PADA LAMA STUDI MAHASISWA." Jurnal Ilmiah Teknosains 5, no. 2 (February 3, 2020): 94. http://dx.doi.org/10.26877/jitek.v5i2.4256.

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In survival analysis to determine the relationship between variables is used a regression model, one of which uses the semiparametric regression model. The semiparametric regression model is a model that does not require assumptions or information on survival data distribution. That way, this model is more flexible in its use. In this study, the semiparametric regression model used the Cox Proportional Hazard (Cox PH) regression model. Estimation of Cox PH regression parameters can be done without determining the function baseline hazard. The purpose of this study is to determine the factors that influence the duration of student studies. If there are many students whose studies have not been on time, it shows that there is a lack of professionalism in the academic field of the educator. Thus, the community will assess the low quality of the university, resulting in a decrease in the number of students who want to study at the university. The samples in this study were students of class 2014 Universitas Islam Darul Ulum Lamongan. The variables have used the length of study for students, gender, GPA, school origin, organization, and work. Based on the results of the assumption Proportional Hazard (PH) conducted, all independent variables have fulfilled these assumptions, so that these variables can be used in Cox PH regression. After parameter estimation by Cox PH regression, the GPA and organizational factors significantly influence the duration of student study. Students with high GPA and participating in organizations more quickly complete their studies.
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27

Zhang, Zhongheng. "Semi-parametric regression model for survival data: graphical visualization with R." Annals of Translational Medicine 4, no. 23 (December 2016): 461. http://dx.doi.org/10.21037/atm.2016.08.61.

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28

Ma, Steven. "Principal Component Analysis in Linear Regression Survival Model with Microarray Data." Journal of Data Science 5, no. 2 (July 12, 2021): 183–98. http://dx.doi.org/10.6339/jds.2007.05(2).326.

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29

Qing Chen, Ying. "Accelerated Hazards Regression Model and Its Adequacy for Censored Survival Data." Biometrics 57, no. 3 (September 2001): 853–60. http://dx.doi.org/10.1111/j.0006-341x.2001.00853.x.

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30

DiRienzo, A. Gregory. "Flexible Regression Model Selection for Survival Probabilities: With Application to AIDS." Biometrics 65, no. 4 (January 23, 2009): 1194–202. http://dx.doi.org/10.1111/j.1541-0420.2008.01178.x.

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31

Stare, Janez, and Harald Heinzl. "07 On the choice of regression model for censored Survival data." Controlled Clinical Trials 18, no. 3 (June 1997): S45. http://dx.doi.org/10.1016/s0197-2456(97)90992-2.

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32

Satoh, Kenichi, Tetsuji Tonda, and Shizue Izumi. "Logistic Regression Model for Survival Time Analysis Using Time-Varying Coefficients." American Journal of Mathematical and Management Sciences 35, no. 4 (August 31, 2016): 353–60. http://dx.doi.org/10.1080/01966324.2016.1215945.

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33

Yoo, Jae Keun, and Keunbaik Lee. "Model-free predictor tests in survival regression through sufficient dimension reduction." Lifetime Data Analysis 17, no. 3 (November 4, 2010): 433–44. http://dx.doi.org/10.1007/s10985-010-9187-4.

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34

Maul, A. "A discrete time logistic regression model for analyzing censored survival data." Environmetrics 5, no. 2 (June 1994): 145–57. http://dx.doi.org/10.1002/env.3170050205.

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35

Malec, Karel, Socrates Kraido Majune, Elena Kuzmenko, Joseph Phiri, Rahab Liz Masese Nyamoita, Seth Nana Kwame Appiah-Kubi, Mansoor Maitah, et al. "Energy Logistic Regression and Survival Model: Case Study of Russian Exports." International Journal of Environmental Research and Public Health 20, no. 1 (January 3, 2023): 885. http://dx.doi.org/10.3390/ijerph20010885.

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The importance of environmental sustainability is becoming more and more obvious, so the rationale behind long-term usage of solely non-renewable energy sources appeared questionable. This study aims to identify, using Kaplan-Meier survival analysis and logistic regressions, the main determinants that affect the duration of Russian non-renewable energy exports to different regions of the world. Data were retrieved from the databanks of the World Development Indicators (WDI), World Integrated Trade Solution (WITS), and the French Centre for Prospective studies and International Information (CEPII). The obtained results point to the fact that approximately 52% of energy products survive beyond their first year of trading, nearly 38% survive beyond the second year, and almost 18% survive to the twelfth year. The survival of Russian non-renewable energy exports differs depending on the region, and the affecting factors are of different importance. The duration of Russian non-renewable energy exports is significantly linked to Russia’s GDP, Total export, and Initial export values. A decline in Russia’s GDP by 1% is associated with an increasing probability of a spell ending by 2.9% on average, in turn growing Total export and Initial export values positively linked with the duration of non-renewable energy exports from Russia. These findings may have practical relevance for strategic actions aimed at approaching both energy security and environmental sustainability.
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Zwick, Rebecca, and Jeffrey C. Sklar. "A Note on Standard Errors for Survival Curves in Discrete-Time Survival Analysis." Journal of Educational and Behavioral Statistics 30, no. 1 (March 2005): 75–92. http://dx.doi.org/10.3102/10769986030001075.

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Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that includes one parameter per time period. This work has been expanded to show how logistic regression output can also be used to estimate the standard errors of the survival functions. This is particularly simple under the model described by Singer and Willett, when there are no predictors other than time.
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Giorgi, Roch, Michal Abrahamowicz, Catherine Quantin, Philippe Bolard, Jacques Esteve, Joanny Gouvernet, and Jean Faivre. "A relative survival regression model using B-spline functions to model non-proportional hazards." Statistics in Medicine 22, no. 17 (2003): 2767–84. http://dx.doi.org/10.1002/sim.1484.

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Khinanti, Aprilia Sekar, Sudarno Sudarno, and Triastuti Wuryandari. "MODEL REGRESI COX PROPORTIONAL HAZARD PADA DATA KETAHANAN HIDUP PASIEN HEMODIALISA." Jurnal Gaussian 10, no. 2 (May 31, 2021): 303–14. http://dx.doi.org/10.14710/j.gauss.v10i2.30958.

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Cox regression is a type of survival analysis that can be implemented with proportional hazard models or duration models. In the survival analysis data, there is a possibility that the data has ties, so it is necessary to use several approaches in estimating the parameters, namely the breslow, efron, and exact approaches. In this study, the Cox proportional hazard regression was used as a method of analysis for knowing the factors that influence the survival time on chronic kidney patients undergoing hemodialysis therapy. Based on the analysis that has been done, the best model is obtained with an exact approach and the factors that influence the survival time of hemodialysis patients are systolic blood pressure, hemoglobin level, and dialysis time. Hemodialysis patients who have high systolic blood pressure have a chance of failing to survive 12,950 times than normal systolic blood pressure.While the hemodialysis patient hemoglobin level increases, the hemodialysis patients chances of failing to survive is 0,6681 times less. Hemodialysis patients who received dialysis therapy with a dialysis time of more than four hours had 0.237 times the chance of failing to survive than patients with a dialysis time of less than or equal to 4 hours.Keywords: Cox Regression ,Survival, Ties, Hemodialysis.
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Kharazmi, Omid, Morad Alizadeh, Javier E. Contreras-Reyes, and Hossein Haghbin. "Arctan-Based Family of Distributions: Properties, Survival Regression, Bayesian Analysis and Applications." Axioms 11, no. 8 (August 12, 2022): 399. http://dx.doi.org/10.3390/axioms11080399.

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In this paper, a new class of the continuous distributions is established via compounding the arctangent function with a generalized log-logistic class of distributions. Some structural properties of the suggested model such as distribution function, hazard function, quantile function, asymptotics and a useful expansion for the new class are given in a general setting. Two special cases of this new class are considered by employing Weibull and normal distributions as the parent distribution. Further, we derive a survival regression model based on a sub-model with Weibull parent distribution and then estimate the parameters of the proposed regression model making use of Bayesian and frequentist approaches. We consider seven loss functions, namely the squared error, modified squared error, weighted squared error, K-loss, linear exponential, general entropy, and precautionary loss functions for Bayesian discussion. Bayesian numerical results include a Bayes estimator, associated posterior risk, credible and highest posterior density intervals are provided. In order to explore the consistency property of the maximum likelihood estimators, a simulation study is presented via Monte Carlo procedure. The parameters of two sub-models are estimated with maximum likelihood and the usefulness of these sub-models and a proposed survival regression model is examined by means of three real datasets.
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Utkin, Lev, and Andrei Konstantinov. "Random Survival Forests Incorporated by the Nadaraya-Watson Regression." Informatics and Automation 21, no. 5 (September 28, 2022): 851–80. http://dx.doi.org/10.15622/ia.21.5.1.

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An attention-based random survival forest (Att-RSF) is presented in the paper. The first main idea behind this model is to adapt the Nadaraya-Watson kernel regression to the random survival forest so that the regression weights or kernels can be regarded as trainable attention weights under important condition that predictions of the random survival forest are represented in the form of functions, for example, the survival function and the cumulative hazard function. Each trainable weight assigned to a tree and a training or testing example is defined by two factors: by the ability of corresponding tree to predict and by the peculiarity of an example which falls into a leaf of the tree. The second main idea behind Att-RSF is to apply the Huber's contamination model to represent the attention weights as the linear function of the trainable attention parameters. The Harrell's C-index (concordance index) measuring the prediction quality of the random survival forest is used to form the loss function for training the attention weights. The C-index jointly with the contamination model lead to the standard quadratic optimization problem for computing the weights, which has many simple algorithms for its solution. Numerical experiments with real datasets containing survival data illustrate Att-RSF.
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Muse, Abdisalam Hassan, Samuel Mwalili, Oscar Ngesa, Christophe Chesneau, Huda M. Alshanbari, and Abdal-Aziz H. El-Bagoury. "Amoud Class for Hazard-Based and Odds-Based Regression Models: Application to Oncology Studies." Axioms 11, no. 11 (November 1, 2022): 606. http://dx.doi.org/10.3390/axioms11110606.

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The purpose of this study is to propose a novel, general, tractable, fully parametric class for hazard-based and odds-based models of survival regression for the analysis of censored lifetime data, named as the “Amoud class (AM)” of models. This generality was attained using a structure resembling the general class of hazard-based regression models, with the addition that the baseline odds function is multiplied by a link function. The class is broad enough to cover a number of widely used models, including the proportional hazard model, the general hazard model, the proportional odds model, the general odds model, the accelerated hazards model, the accelerated odds model, and the accelerated failure time model, as well as combinations of these. The proposed class incorporates the analysis of crossing survival curves. Based on a versatile parametric distribution (generalized log-logistic) for the baseline hazard, we introduced a technique for applying these various hazard-based and odds-based regression models. This distribution allows us to cover the most common hazard rate shapes in practice (decreasing, constant, increasing, unimodal, and reversible unimodal), and various common survival distributions (Weibull, Burr-XII, log-logistic, exponential) are its special cases. The proposed model has good inferential features, and it performs well when different information criteria and likelihood ratio tests are used to select hazard-based and odds-based regression models. The proposed model’s utility is demonstrated by an application to a right-censored lifetime dataset with crossing survival curves.
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42

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 (June 13, 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 diagnosed with cancer, pathologically. Semi-parametric Cox model and parametric log-logistic model were fitted. Akaike’s criterion of Cox Snell graph was used to compare the models. To take into account non-measured individual characteristics, frailty was added to Cox and log-logistic models. All calculations were carried out using STATA software version 12 and SPSS version 20.0, at the 0.05 level of significance. From a total of 1,127 patients studied in this research, there were 690 men and 437 women. According to non-parametric Kaplan-Meier method, chances of surviving for 1, 3, 5 and 7 years were 91.16, 73.20, 61.00, and 54.94, respectively. Addition of frailty parameter did not change the model outcome. The results of fitting classified Cox and log-logistic models showed that body mass index (BMI), tumor grade, tumor size, and spread to lymph nodes, were the factors affecting survival time. Based on comparisons, and according to Cox Snell residuals, Cox and log-logistic models had almost identical results; however, because of the benefits of parametric models, in surveying survival time of patients with colorectal cancer, log-logistic can be replaced, as a parametric model, with Cox model.Journal of Medical and Biomedical Sciences (2017) 6(1), 35-43Keywords: Colorectal cancer, Cox regression, Log-logistic model, Cox Snell residual
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43

JAMEE, AHSAN RAHMAN, and WASIMUL BARI. "On truncated Poisson exponential proportional hazard model." Journal of Statistical Research 53, no. 2 (March 1, 2020): 129–46. http://dx.doi.org/10.47302/jsr.2019530203.

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Classical survival regression models may provide misleading results when event of interest occurs due to more than one causes. In this paper, taking all possible causes for the occurrence of event into account, a Truncated Poisson Exponential survival proportional hazard model has been proposed. An extensive simulation study has been conducted to examine the performance of the proposed survival model in the absence and presence of covariates under different percentages of censoring. The simulation results reveal that estimators of the regression parameters are consistent and efficient. To illustrate the model, under–five child survival data extracted from Bangladesh Demographic and Health Survey 2014 have been used.
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Liu, Qingqing, Jie Ren, and Haoyu Feng. "Nomograms for predicting long‐term overall survival and cancer‐specific survival in chordoma: a population‐based study." Future Oncology 18, no. 24 (August 2022): 2687–99. http://dx.doi.org/10.2217/fon-2022-0158.

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Background: The aim of this study was to develop two predictive models to predict overall survival (OS) and cancer-specific survival (CSS) in chordoma patients. Methods: We searched for independent prognostic factors by using univariate and multivariate Cox regression analyses. The prediction model of OS and CSS of chordoma patients was constructed by using the screened factors. Results: The study enrolled 362 chordoma patients. Cox regression analysis showed that disease stage, age, surgery, marital status and tumor size are independent influencing factors of OS and CSS in chordoma patients. After testing, the prediction model constructed in this study has good performance. Conclusion: Two predictive models were successfully constructed and validated for chordoma patients’ OS and CSS.
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Chandra, Novita Eka, and Siti Alfiatur Rohmaniah. "Analisis Survival Model Regresi Parametrik Lama Studi Mahasiswa." Jurnal Matematika 9, no. 1 (June 30, 2019): 01. http://dx.doi.org/10.24843/jmat.2019.v09.i01.p106.

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Timely graduation of students can be used as an indicator to show the quality of a university. Students are said to graduate on time if they have a short study period of 4 years. The duration of the study of students varies because it is influenced by several factors. The purpose of this study is to determine the factors that have a significant effect on the duration of student studies. The factors studied included gender, GPA, school origin, joining the organization and working in college. The method used in this study is survival analysis. Survival analysis in this study used Log-normal and Weibull, parametric regression models. From the two models, it was found that the GPA and organizational factors significantly influence the duration of student studies. Next, to determine the best model is determined based on the minimum AIC value. Based on the comparison of the two models, the parametric Weibull model has a minimum AIC value, so this model is the best model. Based on HR values ??obtained by students who have a higher GPA and are more active in graduating faster or can be said to have fewer studies. Keywords: survival, regression, parametric, time of study.
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Qiu, Zhiping, Huijuan Ma, Jianwei Chen, and Gregg E. Dinse. "Quantile regression models for survival data with missing censoring indicators." Statistical Methods in Medical Research 30, no. 5 (April 7, 2021): 1320–31. http://dx.doi.org/10.1177/0962280221995986.

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The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application.
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Zhang, Zhongheng, Giuliana Cortese, Christophe Combescure, Roger Marshall, Minjung Lee, Hyun Lim, and Bernhard Haller. "Overview of model validation for survival regression model with competing risks using melanoma study data." Annals of Translational Medicine 6, no. 16 (August 2018): 325. http://dx.doi.org/10.21037/atm.2018.07.38.

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48

Ayuputri, Ikacipta Mega, Nur Iriawan, and Pratnya Paramitha Oktaviana. "Frequency Model of Credit Payment using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression." MATEMATIKA 34, no. 3 (December 31, 2018): 103–13. http://dx.doi.org/10.11113/matematika.v34.n3.1143.

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In distributing funds to customers as credit, multi-finance companies have two necessary risks, i.e. prepayment risk, and default risk. The default risk can be minimized by determining the factors that affect the survival of customers to make credit payment, in terms of frequency of credit payments by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture Geometric Regression. Modelling using Bayesian Geometric Regression show the significant variables are marital status, down payment, installment length, length of stay, and insurance.
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Wang, Lu, and Lianming Wang. "Regression analysis of arbitrarily censored survival data under the proportional odds model." Statistics in Medicine 40, no. 16 (April 21, 2021): 3724–39. http://dx.doi.org/10.1002/sim.8994.

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50

Hanagal, David D., and Richa Sharma. "Modeling heterogeneity for bivariate survival data by shared gamma frailty regression model." Model Assisted Statistics and Applications 8, no. 2 (May 15, 2013): 85–102. http://dx.doi.org/10.3233/mas-130259.

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