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Journal articles on the topic 'Competing risks survival model'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

Lee, Jenq-Daw, and Cheng K. Lee. "SEMI-COMPETING RISKS ON A TRIVARIATE WEIBULL SURVIVAL MODEL." Pakistan Journal of Statistics and Operation Research 4, no. 2 (2008): 77. http://dx.doi.org/10.18187/pjsor.v4i2.51.

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2

Ha, Il Do, and Geon-Ho Cho. "A Joint Frailty Model for Competing Risks Survival Data." Korean Journal of Applied Statistics 28, no. 6 (2015): 1209–16. http://dx.doi.org/10.5351/kjas.2015.28.6.1209.

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3

Nishikawa, Masako. "Competing Risks Model in the Analysis of Survival Data." Japanese Journal of Biometrics 29, no. 2 (2008): 141–70. http://dx.doi.org/10.5691/jjb.29.141.

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4

Gorfine, Malka, and Li Hsu. "Frailty-Based Competing Risks Model for Multivariate Survival Data." Biometrics 67, no. 2 (2010): 415–26. http://dx.doi.org/10.1111/j.1541-0420.2010.01470.x.

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5

Hinchliffe, Sally R., and Paul C. Lambert. "Extending the Flexible Parametric Survival Model for Competing Risks." Stata Journal: Promoting communications on statistics and Stata 13, no. 2 (2013): 344–55. http://dx.doi.org/10.1177/1536867x1301300209.

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6

Edelmann, Dominic, Maral Saadati, Hein Putter, and Jelle Goeman. "A global test for competing risks survival analysis." Statistical Methods in Medical Research 29, no. 12 (2020): 3666–83. http://dx.doi.org/10.1177/0962280220938402.

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Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per j

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7

Lambert, Paul C. "The Estimation and Modeling of Cause-specific Cumulative Incidence Functions Using Time-dependent Weights." Stata Journal: Promoting communications on statistics and Stata 17, no. 1 (2017): 181–207. http://dx.doi.org/10.1177/1536867x1701700110.

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Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event's occurrence precludes another's. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal

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Laurent, Stéphane. "Estimating the survival functions in a censored semi-competing risks model." Sankhya A 75, no. 2 (2013): 231–52. http://dx.doi.org/10.1007/s13171-013-0023-2.

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9

Salazar, Adriana Marcela, and Jaime Huertas. "A Joint Model of Competing Risks in Discrete Time with Longitudinal Information." Revista Colombiana de Estadística 46, no. 2 (2023): 145–61. http://dx.doi.org/10.15446/rce.v46n2.98005.

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The survival competing risks model in discrete time based on multinomial logistic regression, proposed by Luo et al. (2016), models the non-linear and irregular shape of hazard functions by incorporating a time-dependent spline into the multinomial logistic regression. This model also directly includes longitudinal variables in the regression. Due to the issues arising from including both baseline and longitudinal covariates in the extended form as proposed, and considering that the latter may be subject to error, this article suggests an extension of the existing model. The proposed extension

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10

Kretowska, Malgorzata. "Tree-based models for survival data with competing risks." Computer Methods and Programs in Biomedicine 159 (June 2018): 185–98. http://dx.doi.org/10.1016/j.cmpb.2018.03.017.

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11

Alipour, Abbas, Abolghasem Shokri, Fatemeh Yasari, and Soheila Khodakarim. "Introduction to Competing Risk Model in the Epidemiological Research." International Journal of Epidemiologic Research 5, no. 3 (2018): 98–102. http://dx.doi.org/10.15171/ijer.2018.21.

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Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in surv

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Ren, Dianxu, Oscar Lopez, and Jennifer Lingler. "Analysis of Survival Data with Competing Risks in ADRD (Alzheimer's Disease and Related Dementias) Research." Innovation in Aging 5, Supplement_1 (2021): 642. http://dx.doi.org/10.1093/geroni/igab046.2436.

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Abstract Competing risk is an event that precludes the occurrence of the primary event of interest. For example, when studying risk factors associated with dementia, death before the onset of dementia serve as a competing event. A subject who dies is no longer at risk of dementia. This issue play more important role in ADRD research given the elderly population. Conventional methods for survival analysis assume independent censoring and ignore the competing events. However, there are some challenge issues using those conventional methods in the presence of competing risks. First, no one-to-one

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Basu, Sanjib, and Ram C. Tiwari. "Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 2 (2010): 307–29. http://dx.doi.org/10.1111/j.1467-985x.2009.00618.x.

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14

Jiang, Fei, and Sebastien Haneuse. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data." Scandinavian Journal of Statistics 44, no. 1 (2016): 112–29. http://dx.doi.org/10.1111/sjos.12244.

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15

Escarela, Gabriel, and Russell J. Bowater. "Fitting a Semi-Parametric Mixture Model for Competing Risks in Survival Data." Communications in Statistics - Theory and Methods 37, no. 2 (2008): 277–93. http://dx.doi.org/10.1080/03610920701649134.

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16

Zulkarnaev, A. B. "Features of survival analysis on patients on the «waiting list» for kidney transplantation." Bulletin of Siberian Medicine 18, no. 2 (2019): 215–22. http://dx.doi.org/10.20538/1682-0363-2019-2-215-222.

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Survival analysis is one of the most common methods of statistical analysis in medicine. The statistical analysis of the transplantation (or death) probability dependent on the waiting time on the "waiting list" is a rare case when the survival analysis is used to estimate the time before the event rather than to indirectly assess the risks. However, for an assessment to be adequate, the reason for censoringmust be independent of the outcome of interest. Patients on the waiting list are not only at risk of dying, they can be excluded from the waiting list due to deterioration of the comorbid b

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17

Molydah S, Molydah S., and Danardono Danardono. "AN ADDITIVE SUBDISTRIBUTION HAZARDS MODEL FOR COMPETING RISKS DATA." MEDIA STATISTIKA 16, no. 2 (2024): 194–205. http://dx.doi.org/10.14710/medstat.16.2.194-205.

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Competing risk failure time data occur frequently in medical a number of methods have been proposed for the analysis of these data. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In this paper, we consider a more flexible model for the subdistribution. It is a combination of the additive model and the Cox model and allows one to perform a mo

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18

Ko, Feng-shou. "An issue of identifying longitudinal biomarkers for competing risks data with masked causes of failure considering frailties model." Statistical Methods in Medical Research 29, no. 2 (2019): 603–16. http://dx.doi.org/10.1177/0962280219842352.

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In this paper, we consider joint modeling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint. Hence, we can fit a cause-specific hazards submodel to allow for competing risks, with a separate latent association between longitudinal measurements and each cause of failure. We also consider the possible masked causes of failure in joint modeling of repeated measurements and competing risks failure time data. We also derive a score test to identify longitudinal biomarkers or surrogates for a time-to-event outcome

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19

Ma, Yuan, and Wenhao Gui. "Competing Risks Step-Stress Model with Lagged Effect under Gompertz Distribution." Mathematics 9, no. 24 (2021): 3206. http://dx.doi.org/10.3390/math9243206.

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In many survival analysis studies, the failure of a product may be attributed to one of several competing risks. In addition, if survival time is long, researchers can adopt accelerated life tests, causing devices to fail more quickly. One popular type of accelerated life tests is the step-stress test, and in this test, the stress level changes at a predetermined point time. The manner that stress levels change abruptly and increase discontinuously has been studied extensively. This paper considers a more realistic situation where the effect of stress increases cannot be achieved all at once,

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20

Sami Ali Obed, Kurdistan I. Mawlood. "Modeling for Competing Risk Regression in Survival Analysis with Application in Breast Cancer Disease." Communications on Applied Nonlinear Analysis 32, no. 3 (2025): 803–18. https://doi.org/10.52783/cana.v32.4355.

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Competing risks regression is an essential component of survival analysis, particularly when there are several possible event types that prevent additional events from being seen. This paper investigates the modelling and analysis of competing risks in time-to-event data. The study was applied to a sample size of (4420) patients with Breast cancer. The data was obtained from Rizgari Hospital in the period from 1st of January 2019 to 31st of August 2024. In survival analysis, a competing risk occurs when an event (such as death from a cause other than breast cancer) precludes the occurrence of

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21

Emura, Takeshi, Jia-Han Shih, Il Do Ha, and Ralf A. Wilke. "Comparison of the marginal hazard model and the sub-distribution hazard model for competing risks under an assumed copula." Statistical Methods in Medical Research 29, no. 8 (2019): 2307–27. http://dx.doi.org/10.1177/0962280219892295.

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For the analysis of competing risks data, three different types of hazard functions have been considered in the literature, namely the cause-specific hazard, the sub-distribution hazard, and the marginal hazard function. Accordingly, medical researchers can fit three different types of the Cox model to estimate the effect of covariates on each of the hazard function. While the relationship between the cause-specific hazard and the sub-distribution hazard has been extensively studied, the relationship to the marginal hazard function has not yet been analyzed due to the difficulties related to n

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22

Zhang, Zhongheng, Giuliana Cortese, Christophe Combescure, et al. "Overview of model validation for survival regression model with competing risks using melanoma study data." Annals of Translational Medicine 6, no. 16 (2018): 325. http://dx.doi.org/10.21037/atm.2018.07.38.

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23

Bandyopadhyay, Dipankar, and M. Amalia Jácome. "Comparing conditional survival functions with missing population marks in a competing risks model." Computational Statistics & Data Analysis 95 (March 2016): 150–60. http://dx.doi.org/10.1016/j.csda.2015.10.001.

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24

Bhattacharjee, Atanu, and Rajashree Dey. "Bayesian modelling for semi-competing risks data in the presence of censoring." Statistics in Transition new series 24, no. 3 (2023): 201–11. http://dx.doi.org/10.59170/stattrans-2023-044.

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In biomedical research, challenges to working with multiple events are often observed while dealing with time-to-event data. Studies on prolonged survival duration are prone to having numerous possibilities. In studies on prolonged survival, patients might die of other causes. Sometimes in the survival studies, patients experienced some events (e.g. cancer relapse) before dying within the study period. In this context, the semi-competing risks framework was found useful. Similarly, the prolonged duration of follow-up studies is also affected by censored observation, especially interval censori

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25

Jakaitienė, Audronė, and Danas Zuokas. "The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions." Lietuvos matematikos rinkinys 42 (December 20, 2002): 518–22. http://dx.doi.org/10.15388/lmr.2002.32987.

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In this paper the trivariate survival regression model for FGM family of distributions is constructed with marginal left-truncated logistic distributions. Two methods (using survival and hazard functions in the first case, and distributional density and ` `conditional'' survival function in the second case) are used when constructing likelihood function for model parameter estimation. Constructed survival model was run with the data of the ` `KRIS'' (The Kaunas Rotterdam Intervention Study), which lasted for 22 years from 1972. The results show, that using second case for likehhood function

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26

Ambrogi, Federico, and Thomas H. Scheike. "Penalized estimation for competing risks regression with applications to high-dimensional covariates." Biostatistics 17, no. 4 (2016): 708–21. http://dx.doi.org/10.1093/biostatistics/kxw017.

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High-dimensional regression has become an increasingly important topic for many research fields. For example, biomedical research generates an increasing amount of data to characterize patients' bio-profiles (e.g. from a genomic high-throughput assay). The increasing complexity in the characterization of patients' bio-profiles is added to the complexity related to the prolonged follow-up of patients with the registration of the occurrence of possible adverse events. This information may offer useful insight into disease dynamics and in identifying subset of patients with worse prognosis and be

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27

Tabassum, Tasnuva, and Wasimul Bari. "Competing risks analysis of under-five child mortality in Bangladesh." Bangladesh Journal of Scientific Research 27, no. 1 (2016): 27–38. http://dx.doi.org/10.3329/bjsr.v27i1.26222.

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This study implements an analysis of under five mortality to the Bangladesh Demographic and Health Survey, 2011 data from a competing risks perspective. Kaplan - Meier overall survival curves along with log-rank test p-values are employed to determine the prospective covariates for the Cox proportional hazard (PH) model. Later the typical Cox PH model is used to model the causespecific hazard for the two competing causes, namely disease and non-disease on the selected covariates. It is revealed that mothers primary and secondary education, birth order of the index child in case of disease mod

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28

Ge, Miaomiao, and Ming-Hui Chen. "Bayesian inference of the fully specified subdistribution model for survival data with competing risks." Lifetime Data Analysis 18, no. 3 (2012): 339–63. http://dx.doi.org/10.1007/s10985-012-9221-9.

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29

Rehman, Habbiburr, Navin Chandra, Takeshi Emura, and Manju Pandey. "Estimation of the Modified Weibull Additive Hazards Regression Model under Competing Risks." Symmetry 15, no. 2 (2023): 485. http://dx.doi.org/10.3390/sym15020485.

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The additive hazard regression model plays an important role when the excess risk is the quantity of interest compared to the relative risks, where the proportional hazard model is better. This paper discusses parametric regression analysis of survival data using the additive hazards model with competing risks in the presence of independent right censoring. In this paper, the baseline hazard function is parameterized using a modified Weibull distribution as a lifetime model. The model parameters are estimated using maximum likelihood and Bayesian estimation methods. We also derive the asymptot

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30

Vásquez, Alejandro Román, Gabriel Escarela, Hortensia Josefina Reyes-Cervantes, and Gabriel Núñez-Antonio. "Gaussian Copula Regression Modeling for Marker Classification Metrics with Competing Risk Outcomes." International Journal of Mathematics and Mathematical Sciences 2024 (January 19, 2024): 1–13. http://dx.doi.org/10.1155/2024/1671254.

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Decisions regarding competing risks are usually based on a continuous-valued marker toward predicting a cause-specific outcome. The classification power of a marker can be summarized using the time-dependent receiver operating characteristic curve and the corresponding area under the curve (AUC). This paper proposes a Gaussian copula-based model to represent the joint distribution of the continuous-valued marker, the overall survival time, and the cause-specific outcome. Then, it is used to characterize the time-varying ROC curve in the context of competing risks. Covariate effects are incorpo

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Keramati, Amin, Pan Lu, Xiaoyi Zhou, and Denver Tolliver. "A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method." Journal of Advanced Transportation 2020 (August 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8878911.

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This paper proposes a mathematical model, the competing risks method, to investigate highway-rail grade crossing (HRGC) crash frequency and crash severity simultaneously over a 30-year period. The proposed competing risks model is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest; in this case, the competing multiple outcomes are crash severities, while event of interest is crash occurrence. Knowledge-gain-based benefits to be discovered through the application of this model and 30-year dataset are as follows: (1) a str

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32

Sparapani, Rodney, Brent R. Logan, Robert E. McCulloch, and Purushottam W. Laud. "Nonparametric competing risks analysis using Bayesian Additive Regression Trees." Statistical Methods in Medical Research 29, no. 1 (2019): 57–77. http://dx.doi.org/10.1177/0962280218822140.

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Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible predictio

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Nuel, Gregory, Alexandra Lefebvre, and Olivier Bouaziz. "Computing Individual Risks Based on Family History in Genetic Disease in the Presence of Competing Risks." Computational and Mathematical Methods in Medicine 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/9193630.

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When considering a genetic disease with variable age at onset (e.g., familial amyloid neuropathy, cancers), computing the individual risk of the disease based on family history (FH) is of critical interest for both clinicians and patients. Such a risk is very challenging to compute because 1 the genotype X of the individual of interest is in general unknown, 2 the posterior distribution PX∣FH,T>t changes with t (T is the age at disease onset for the targeted individual), and 3 the competing risk of death is not negligible. In this work, we present modeling of this problem using a Bayesian n

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Tolba, Ahlam, Ehab Almetwally, Neveen Sayed-Ahmed, Taghreed Jawa, Nagla Yehia, and Dina Ramadan. "Bayesian and non-Bayesian estimation methods to independent competing risks models with type II half logistic weibull sub-distributions with application to an automatic life test." Thermal Science 26, Spec. issue 1 (2022): 285–302. http://dx.doi.org/10.2298/tsci22s1285t.

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In the survival data analysis, competing risks are commonly overlooked, and conventional statistical methods are used to analyze the event of interest. There may be more than one cause of death or failure in many experimental investigations of survival analysis. A competing risks model will be derived statistically applying Type-II half logistic weibull sub-distributions. Type-II half logistic weibull life?times failure model with independent causes. It is possible to estimate parameters and parametric functions using Bayesian and classical methods. A Bayes estimation is obtained by the Markov

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Mondal, Prosanta, Hyun J. Lim, and OHTN Cohort Study Team. "The Effect of MSM and CD4+ Count on the Development of Cancer AIDS (AIDS-defining Cancer) and Non-cancer AIDS in the HAART Era." Current HIV Research 16, no. 4 (2019): 288–96. http://dx.doi.org/10.2174/1570162x17666181205130532.

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Background: The HIV epidemic is increasing among Men who have Sex with Men (MSM) and the risk for AIDS defining cancer (ADC) is higher among them. Objective: To examine the effect of MSM and CD4+ count on time to cancer AIDS (ADC) and noncancer AIDS in competing risks setting in the HAART era. Method: Using Ontario HIV Treatment Network Cohort Study data, HIV-positive adults diagnosed between January 1997 and October 2012 having baseline CD4+ counts ≤ 500 cells/mm3 were evaluated. Two survival outcomes, cancer AIDS and non-cancer AIDS, were treated as competing risks. Kaplan-Meier analysis, Co

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36

Crowther, Michael J. "Simulating time-to-event data from parametric distributions, custom distributions, competing-risks models, and general multistate models." Stata Journal: Promoting communications on statistics and Stata 22, no. 1 (2022): 3–24. http://dx.doi.org/10.1177/1536867x221083853.

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In this article, I describe some substantial extensions to the survsim command for simulating survival data. survsim can now simulate survival data from a parametric distribution, a custom or user-defined distribution, a fitted merlin model, a specified cause-specific hazards competing-risks model, or a specified general multistate model (with multiple timescales). Left-truncation (delayed entry) is now also available for all settings. I illustrate the survsim command with some examples, demonstrating the huge flexibility that can be used to better evaluate statistical methods.

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37

Scheike, Thomas H., and Klaus Kähler Holst. "A Practical Guide to Family Studies with Lifetime Data." Annual Review of Statistics and Its Application 9, no. 1 (2022): 47–69. http://dx.doi.org/10.1146/annurev-statistics-040120-024253.

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Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefo

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Mozumder, Sarwar Islam, Mark J. Rutherford, and Paul C. Lambert. "A Flexible Parametric Competing-risks Model Using a Direct Likelihood Approach for the Cause-specific Cumulative Incidence Function." Stata Journal: Promoting communications on statistics and Stata 17, no. 2 (2017): 462–89. http://dx.doi.org/10.1177/1536867x1701700212.

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In competing-risks analysis, the cause-specific cumulative incidence function (CIF) is usually obtained in a modeling framework by either 1) transforming on all cause-specific hazards or 2) transforming by using a direct relationship with the subdistribution hazard function. We expand on current competing-risks methodology from within the flexible parametric survival modeling framework and focus on the second approach. This approach models all cause-specific CIFs simultaneously and is more useful for answering prognostic-related questions. We propose the direct flexible parametric survival mod

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Wood, Richard, and David Powell. "Addressing probationary period within a competing risks survival model for retail mortgage loss given default." Journal of Credit Risk 13, no. 3 (2017): 47–66. http://dx.doi.org/10.21314/jcr.2017.228.

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Ng, S. K., and G. J. McLachlan. "On modifications to the long-term survival mixture model in the presence of competing risks." Journal of Statistical Computation and Simulation 61, no. 1-2 (1998): 77–96. http://dx.doi.org/10.1080/00949659808811903.

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Wycinka, Ewa, and Tomasz Jurkiewicz. "SURVIVAL REGRESSION MODELS FOR SINGLE EVENTS AND COMPETING RISKS BASED ON PSEUDOOBSERVATIONS." Statistics in Transition New Series 20, no. 1 (2019): 171–88. http://dx.doi.org/10.21307/stattrans-2019-010.

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42

Huang, Ying, and Mei-Cheng Wang. "Estimating the Occurrence Rate for Prevalent Survival Data in Competing Risks Models." Journal of the American Statistical Association 90, no. 432 (1995): 1406–15. http://dx.doi.org/10.1080/01621459.1995.10476646.

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43

Alberge, J., V. Maladière, O. Grisel, J. Abécassis, and G. Varoquaux. "P52 - Survival models: Proper scoring rule and stochastic optimization with competing risks." Journal of Epidemiology and Population Health 73 (May 2025): 203083. https://doi.org/10.1016/j.jeph.2025.203083.

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44

Urbańczyk, Dominika M. "Competing Risks Models for an Enterprises Duration on the Market." Folia Oeconomica Stetinensia 20, no. 1 (2020): 456–73. http://dx.doi.org/10.2478/foli-2020-0027.

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AbstractResearch background: Enterprises are an important element of the economy, which explains that the analysis of their duration on the market is an important and willingly undertaken research topic. In the case of complex problems like this, considering only one type of event, which ends the duration, is often insufficient for full understanding.Purpose: In this paper there is an analysis of the duration of enterprises on the market, taking into account various reasons for the termination of their business activity as well as their characteristics.Research methodology: A survival analysis

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45

Acquah, Joseph, Senyefia Bosson-Amedenu, and Eric Adubuah. "On the Implications of Ignoring Competing Risk in Survival Analysis: The Case of the Product-Limit Estimator." Journal of Mathematics Research 15, no. 5 (2023): 1. http://dx.doi.org/10.5539/jmr.v15n5p1.

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Although the Kaplan-Meier (KM) is a single event model, it is frequently used in literature with datasets that are assumed to be cause-specific without any proper verification. It is crucial to evaluate the implication of this on the probability estimates. This study compares the estimates of the cumulative incidence functions to the complement of the product-limit estimator (1-KM). The KM was found to inflate probability estimates when the dataset is unverified for competing risk. Estimates with lower standard errors and a larger area under the Receiver Operation Characteristic (ROC) curve we

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Huang, Xin, Gang Li, Robert M. Elashoff, and Jianxin Pan. "A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects." Lifetime Data Analysis 17, no. 1 (2010): 80–100. http://dx.doi.org/10.1007/s10985-010-9169-6.

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47

Wang, Jintao, Zhongshang Yuan, Yi Liu, and Fuzhong Xue. "A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach." International Journal of Environmental Research and Public Health 16, no. 18 (2019): 3435. http://dx.doi.org/10.3390/ijerph16183435.

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In the competing risks frame, the cause-specific hazard model (CSHM) can be used to test the effects of some covariates on one particular cause of failure. Sometimes, however, the observed covariates cannot explain the large proportion of variation in the time-to-event data coming from different areas such as in a multi-center clinical trial or a multi-center cohort study. In this study, a multi-center competing risks model (MCCRM) is proposed to deal with multi-center survival data, then this model is compared with the CSHM by simulation. A center parameter is set in the MCCRM to solve the sp

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48

Haque, Mohammad Anamul, and Giuliana Cortese. "Cumulative Incidence Functions for Competing Risks Survival Data from Subjects with COVID-19." Mathematics 11, no. 17 (2023): 3772. http://dx.doi.org/10.3390/math11173772.

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Abstract:

Competing risks survival analysis is used to answer questions about the time to occurrence of events with the extension of multiple causes of failure. Studies that investigate how clinical features and risk factors of COVID-19 are associated with the survival of patients in the presence of competing risks (CRs) are limited. The main objective of this paper is, under a CRs setting, to estimate the Cumulative Incidence Function (CIF) of COVID-19 death, the CIF of other-causes death, and the probability of being cured in subjects with COVID-19, who have been under observation from the date of sym

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49

Rossman, Hagai, Ayya Keshet, and Malka Gorfine. "PyMSM: Python package for Competing Risks and Multi-State models for Survival Data." Journal of Open Source Software 7, no. 78 (2022): 4566. http://dx.doi.org/10.21105/joss.04566.

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50

Huebner, Marianne, Martin Wolkewitz, Maurice Enriquez-Sarano, and Martin Schumacher. "Competing risks need to be considered in survival analysis models for cardiovascular outcomes." Journal of Thoracic and Cardiovascular Surgery 153, no. 6 (2017): 1427–31. http://dx.doi.org/10.1016/j.jtcvs.2016.12.039.

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