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Joint Occurrences of Competing Risks and Multivariate Longitudinal Data: A Prediction Investigation for the HIV. long Data
Ocurrencias conjuntas de riesgos competitivos y datos longitudinales multivariados: una investigación de predicción para los datos de HIV. long
DOI:
https://doi.org/10.15446/rce.v47n2.110557Keywords:
Competing or semi-competing risks data, Covariate prognosis, Cumulative incidence functions, Multivariate longitudinal data, Order statistics. (en)Riesgos competitivos o semi-competitivos, Pronostico de covariables, Funciones de incidencia acumulativa, Datos longitudinales multivariados, Estadisticas de orden. (es)
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In this article, some prediction strategies are introduced for event times, where multivariate data with competing or semi-competing risks are simultaneously collected. Without loss of generality, the proposed methods can be used to analyze multivariate longitudinal data with competing or semi competing risks, often encountered in social sciences and sports activities. Regarding the situations mentioned earlier, we can provide the prediction values of: I. Time of occurrences of any cause for specific individuals II. Time of subsequent events for some cause in other individuals III. The covariate values on predicted time of I and II. Accordingly, doctor assistants or nurses can schedule good visiting times based on I and II. Item III can provide the missing values of all covariates that are utilized for better modeling. The corresponding statistical background is extensively discussed. Finally, an actual data set has been analyzed, the prediction values are provided, and their performances are assessed.
En este artículo, se presentan algunas estrategias de predicción para los tiempos de eventos, donde se recopilan datos multivariados con riesgos competitivos o semi-competitivos de manera simultánea. Sin pérdida de generalidad, los métodos propuestos se pueden utilizar para analizar datos longitudinales multivariados con riesgos competitivos o semi-competitivos, que a menudo se encuentran en las ciencias sociales y actividades deportivas. En relación con las situaciones mencionadas anteriormente, podemos proporcionar los valores de predicción de: I. Tiempo de ocurrencia de cualquier causa para individuos específicos. II. Tiempo de eventos subsiguientes para alguna causa en otros individuos. III. Los valores de covariables en el tiempo predicho de I y II. En consecuencia, los asistentes médicos o enfermeras pueden programar buenos momentos de visita en función de I y II. El ítem III puede proporcionar los valores faltantes de todas las covariables que se utilizan para un mejor modelado. El fondo estadístico correspondiente se discute ampliamente. Finalmente, se ha analizado un conjunto de datos reales, se proporcionan los valores de predicción y se evalúan sus rendimientos.
References
Aalen, O. O. & Johansen, S. (1978), `An empirical transition matrix for nonhomogeneous markov chains based on censored observations', Scandinavian Journal of Statistics pp. 141-150.
Bakoyannis, G., Yu, M. & Yiannoutsos, C. T. (2017), `Semiparametric regression on cumulative incidence function with interval-censored competing risks data', Statistics in medicine 36(23), 3683-3707.
Balakrishnan, N. & Cramer, E. (2008), `Progressive censoring from heterogeneous distributions with applications to robustness', Annals of the Institute of Statistical Mathematics 60(1), 151-171.
Barrett, J. K., Siannis, F. & Farewell, V. T. (2011), `A semi-competing risks model for data with interval-censoring and informative observation: An application to the mrc cognitive function and ageing study', Statistics in medicine 30(1), 1-10.
Berry, S. D., Ngo, L., Samelson, E. J. & Kiel, D. P. (2010), `Competing risk of death: an important consideration in studies of older adults', Journal of the American Geriatrics Society 58(4), 783-787.
Bhattacharjee, A. (2019), `A joint longitudinal and survival model for dynamic treatment regimes in presence of competing risk analysis', Clinical Epidemiology and Global Health 7(3), 337-341.
Collett, D. (2015), Modelling survival data in medical research, Chapman and Hall/CRC.
Curnow, E., Hughes, R. A., Birnie, K., Crowther, M. J., May, M. T. & Tilling, K. (2021), `Multiple imputation strategies for a bounded outcome variable in a competing risks analysis', Statistics in Medicine 40(8), 1917-1929.
David, H. A. & Nagaraja, H. N. (2004), `Order statistics', Encyclopedia of Statistical Sciences.
Eleuteri, A., Rola, A. C., Kalirai, H., Hussain, R., Sacco, J., Damato, B. E. Heimann, H., Coupland, S. E. & Taktak, A. F. (2021), `Cost-utility analysis of a decade of liver screening for metastases using the liverpool uveal melanoma prognosticator online (lumpo)', Computers in Biology and Medicine 130, 104221.
Emura, T., Shih, J.-H., Ha, I. D. & Wilke, R. A. (2020), `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(8), 2307-2327.
Fine, J. P. (2001), `Regression modeling of competing crude failure probabilities', Biostatistics 2(1), 85-97.
Fine, J. P. & Gray, R. J. (1999), `A proportional hazards model for the sub distribution of a competing risk', Journal of the American Statistical Association 94(446), 496-509.
Fine, J. P., Jiang, H. & Chappell, R. (2001), `On semi-competing risks data', Biometrika 88(4), 907-919.
Groeneboom, P., Maathuis, M. H. & Wellner, J. A. (2008), `Current status data with competing risks: Limiting distribution of the mle', Annals of Statistics 36(3), 1064.
Haneuse, S. & Lee, K. H. (2016), `Semi-competing risks data analysis: accounting for death as a competing risk when the outcome of interest is nonterminal', Circulation: Cardiovascular Quality and Outcomes 9(3), 322-331.
Hickey, G. L., Philipson, P., Jorgensen, A. & Kolamunnage-Dona, R. (2018), `A comparison of joint models for longitudinal and competing risks data, with application to an epilepsy drug randomized controlled trial', Journal of the Royal Statistical Society: Series A (Statistics in Society) 181(4), 1105-1123.
Hogg, R. V. & Craig, A. T. (1978),`Introduction to mathematical statistics'.
Hudgens, M. G., Li, C. & Fine, J. P. (2014), `Parametric likelihood inference for interval censored competing risks data', Biometrics 70(1), 1-9.
Hudgens, M. G., Satten, G. A. & Longini Jr, I. M. (2001), `Nonparametric maximum likelihood estimation for competing risks survival data subject to interval censoring and truncation', Biometrics 57(1), 74-80.
Jeong, J.-H. & Fine, J. (2006a), `Direct parametric inference for the cumulative incidence function', Journal of the Royal Statistical Society: Series C (Applied Statistics) 55(2), 187-200.
Jeong, J.-H. & Fine, J. P. (2006b), `Parametric regression on cumulative incidence function', Biostatistics 8(2), 184-196.
Jeong, J.-H. & Oakes, D. (2003), `On the asymptotic relative efficiency of estimates from cox's model', Sankhy a: The Indian Journal of Statistics pp. 422-439.
Jewell, N. P., van der Laan, M. & Henneman, T. (2003), `Nonparametric estimation from current status data with competing risks', Biometrika 90(1), 183-197.
Joe, H. (2014), Dependence modeling with copulas, Chapman and Hall/CRC.
Kazempoor, J., Habibirad, A. & Okhli, K. (2022), `Bounds for cdfs of order statistics arising from inid random variables', Journal of the Iranian Statistical Society 19(1), 39-57.
Kim, J., Kim, J. & Kim, S. W. (2019), `Additive-multiplicative hazards regression models for interval-censored semi-competing risks data with missing intermediate events', BMC medical research methodology 19(1), 49.
Kim, J., Kim, J. & Kim, S. W. (2020), `Regression analysis of the illness-death model with a shared frailty when all transition times are interval censored', Communications in Statistics-Simulation and Computation 52(1), 1-16.
Li, C. (2016), `The -ne-gray model under interval censored competing risks data', Journal of Multivariate Analysis 143, 327-344.
Mao, L. & Lin, D. (2017), `Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks', Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79(2), 573-587.
Meng, X.-L. (1994), `Multiple-imputation inferences with uncongenial sources of input', Statistical Science pp. 538-558.
Mitra, D., Das, U. & Das, K. (2020), `Analysis of interval-censored competing risks data under missing causes', Journal of Applied Statistics 47(3), 439-459.
Moharib Alsarray, R. M., Kazempoor, J. & Ahmadi Nadi, A. (2023), `Monitoring the weibull shape parameter under progressive censoring in presence of independent competing risks', Journal of Applied Statistics 50(4), 945-962.
Park, J., Bakoyannis, G. & Yiannoutsos, C. T. (2019), `Semiparametric competing risks regression under interval censoring using the r package intccr', Computer Methods and Programs in Biomedicine 173, 167-176.
Park, J., Bakoyannis, G., Zhang, Y. & Yiannoutsos, C. T. (2021), `Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types', Biostatistics.
Prentice, R. L., Kalbfleisch, J. D., Peterson Jr, A. V., Flournoy, N., Farewell,V. T. & Breslow, N. E. (1978), `The analysis of failure times in the presence of competing risks', Biometrics pp. 541-554.
Putter, H., Fiocco, M. & Geskus, R. B. (2007), `Tutorial in biostatistics: competing risks and multi-state models', Statistics in medicine 26(11), 2389-2430.
Rouanet, A., Joly, P., Dartigues, J.-F., Proust-Lima, C. & Jacqmin-Gadda, H. (2015), `Joint latent class model for longitudinal data and interval-censored semi competing events: Application to dementia', arXiv preprint arXiv:1506.07415.
Williamson, P., Kolamunnage-Dona, R., Philipson, P. & Marson, A. (2008), `Joint modelling of longitudinal and competing risks data', Statistics in Medicine 27(30), 6426-6438.
Zhang, H. & Huang, Y. (2020), `Quantile regression-based bayesian joint modeling analysis of longitudinal-survival data, with application to an aids cohort study', Lifetime Data Analysis 26(2), 339-368.
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