Relevant bibliographies by topics / Survival regression model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Survival regression model'

Author: Grafiati
Published: 9 March 2023

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Journal articles on the topic "Survival regression model"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Survival regression model"

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Gandy, Axel. "Directed model checks for regression models from survival analysis." Berlin Logos-Ver, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2766731&prov=M&dok_var=1&dok_ext=htm.

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Gandy, Axel. "Directed model checks for regression models from survival analysis /." Berlin : Logos-Ver, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2766731&prov=M&dok_var=1&dok_ext=htm.

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Volinsky, Christopher T. "Bayesian model averaging for censored survival models /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8944.

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Sasieni, Peter D. "Beyond the Cox model : extensions of the model and alternative estimators /." Thesis, Connect to this title online; UW restricted, 1989. http://hdl.handle.net/1773/9556.

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Yuan, Xingchen. "Survival Model and Estimation for Lung Cancer Patients." Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etd/1002.

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Lung cancer is the most frequent fatal cancer in the United States. Following the notion in actuarial math analysis, we assume an exponential form for the baseline hazard function and combine Cox proportional hazard regression for the survival study of a group of lung cancer patients. The covariates in the hazard function are estimated by maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an explicit baseline hazard function, the baseline hazard function can be estimated by fitting the data with a non-linear least square technique. The survival model is then examined by a neural network simulation. The neural network learns the survival pattern from available hospital data and gives survival prediction for random covariate combinations. The simulation results support the covariate estimation in the survival model.
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Wang, Hongwei. "Effect of Risk and Prognosis Factors on Breast Cancer Survival: Study of a Large Dataset with a Long Term Follow-up." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_theses/116.

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The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for the risk and prognosis factors. The study results showed that some risk and prognosis factors, such as the demographic factors (race and age), social and family factor (marital status), biomedical factors (tumor size, disease stage, tumor markers and tumor cell differentiation level etc.) and type of treatment patients received had significant effects on survival of the female invasive breast cancer patients.
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Dawson, Amanda Caroline St Vincent???s Hospital Clinical School UNSW. "Evaluation of novel molecular markers from the WNT pathway : a stepwise regression model for pancreatic cancer survival." Awarded by:University of New South Wales. St Vincent???s Hospital Clinical School, 2007. http://handle.unsw.edu.au/1959.4/31528.

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Optimisation of the conventional tripartite of pancreatic cancer (PC) treatment have led to significant improvements in mortality, however further knowledge of the underlying molecular processes is still required. Transcript profiling of mRNA expression of over 44K genes with microarray technology demonstrated upregulation of secreted frizzled related protein 4 (sFRP4) and ??-catenin in PC compared to normal pancreata. Their pathway ??? Wnt signalling is integral to transcriptional regulation and aberrations in these molecules are critical in the development of many human malignancies. Immunohistochemistry protocols were evaluated by two independent blinded examiners for antigen expression differences associated with survival patterns in 140 patients with biopsy verified PC and a subset of 23 normal pancreata with substantial observer agreement (kappa value 0.6-0.8). A retrospective cohort was identified from 6 Sydney hospitals between 1972-2003 and archival formalin fixed tissue was collected together with clinicopathological data. Three manual stepwise regression models were fitted for overall, disease-specific and relapse-free survival to determine the value of significant prognostic variables in risk stratification. The models were fitted in a logical order using a careful strategy with step by step interpretation of the results. Immunohistochemistry demonstrated increased sFRP4 membranous expression (> 10%) in 49/95 PC specimens and this correlated with improved overall survival (HR:0.99;95%CI:0.97-6.40;LRchi2=134.75; 1df; ??< 0.001). Increased sFRP4 cytoplasmic staining (> 2/3) in 46/85 patients increased the disease-specific survival (HR:0.52;95%CI:0.31-0.89;LR test statistic =248.40;1df;??< 0.001). Increasing ??-catenin membranous expression (< _60%) in 26/116 patients was associated with an increased risk of overall death (HR:3.18;95%CI:1.14-8.89;LR test statistic =4.61;1df,??< 0.05). Increasing cytoplasmic expression in 65/114 patients was protective and was associated with prolonged survival on univariate, but not multivariate analysis (Disease specific survival HR:0.75;95%CI:0.56-1.00;logrank chi2=3.91;1df; ??=0.05). Increased nuclear ??-catenin expression in 65/114 patients was associated with prolonged survival (disease-specific HR:0.92;95%CI:0.83-1.02; LR test statistic= 49.72;1df;??< 0.001). At the conclusion, 12 patients (8.6%) remained alive, 122 died of their disease (68 males versus 54 females). They were followed for a median of 8.7 months (range 1.0-131.3) months. The median age was 66.5 years (range 34.4-96.0, standard deviation 10.9) years. Pancreatic resection was achieved in 79 patients with 46.8% achieving RO resection. The 30 day post-operative mortality was 2.1%. The overall 1 year survival rate was (33.7% ; 95%CI: 25.78-33.79) with a 5 year survival of (2.87%, 95%CI: 2.83-6.01) and a median survival of (8.90 months; 95%CI: 7.5-10.2). The median disease-specific survival was (9.40; 95%CI: 7.9-10.5 months) and the median time to relapse was 1.2 months (95%CI 1.0-1.2 months). A central tenet of contemporary cancer research is that an understanding of the genetic and molecular abnormalities that accompany the development and progression of cancer is critical to further advances in diagnosis, treatment and eventual prevention. High throughput tissue microarrays were used to study expression of two novel tumour markers in a cohort of pancreatic cancer patients and identified sFRP4 and ??-catenin as potential novel prognostic markers.
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Jinnah, Ali. "Inference for Cox's regression model via a new version of empirical likelihood." unrestricted, 2007. http://etd.gsu.edu/theses/available/etd-11272007-223933/.

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Thesis (M.S.)--Georgia State University, 2007.
Title from file title page. Yichuan Zhao, committee chair; Yu-Sheng Hsu , Xu Zhang, Yuanhui Xiao , committee members. Electronic text (54 p.) : digital, PDF file. Description based on contents viewed Feb. 25, 2008. Includes bibliographical references (p. 30-32).
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Erich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.

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Race, Jonathan Andrew. "Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077.

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Books on the topic "Survival regression model"

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Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer, 2001.

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Eric, Vittinghoff, ed. Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. New York: Springer, 2005.

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Lemeshow, Stanley. Applied Survival Analysis: Regression Modeling of Time to Event Data. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2008.

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Stanley, Lemeshow, ed. Applied survival analysis: Regression modeling of time to event data. New York: Wiley, 1999.

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Hosmer, David W. Applied survival analysis: Regression modeling of time-to-event data. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2008.

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Hosmer, David W. Applied survival analysis: Regression modeling of time-to-event data. 2nd ed. Hoboken, N.J: John Wiley & Sons, 2008.

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P, Harrington David, ed. Counting processes and survival analysis. New York: Wiley, 1991.

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Life time data: Statistical models and methods. Singapore: World Scientific, 2006.

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Dynamic Regression Models for Survival Data. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/0-387-33960-4.

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Martinussen, Torben, and Thomas H. Scheike. Dynamic Regression Models for Survival Data. Springer New York, 2010.

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Book chapters on the topic "Survival regression model"

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O’Quigley, John. "Model construction guided by regression effect process." In Survival Analysis, 261–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-33439-0_10.

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Harrell, Frank E. "Logistic Model Case Study 2: Survival of Titanic Passengers." In Regression Modeling Strategies, 291–310. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_12.

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Harrell, Frank E. "Case Study in Parametric Survival Modeling and Model Approximation." In Regression Modeling Strategies, 453–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_19.

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Harrell, Frank E. "Logistic Model Case Study 2: Survival of Titanic Passengers." In Regression Modeling Strategies, 299–330. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_12.

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Harrell, Frank E. "Case Study in Parametric Survival Modeling and Model Approximation." In Regression Modeling Strategies, 443–64. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_18.

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Wu, Jianrong. "Survival Trial Design under the Cox Regression Model." In Statistical Methods for Survival Trial Design, 99–107. Boca Raton : Taylor & Francis, 2018.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429470172-6.

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Lin, Jianchang, Debajyoti Sinha, Stuart Lipsitz, and Adriano Polpo. "Semiparametric Analysis of Interval-Censored Survival Data with Median Regression Model." In Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics, 149–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42568-9_13.

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Brunel, Elodie, and Fabienne Comte. "Model Selection for Additive Regression in the Presence of Right-Censoring." In Mathematical Methods in Survival Analysis, Reliability and Quality of Life, 15–32. London, UK: ISTE, 2010. http://dx.doi.org/10.1002/9780470610985.ch1.

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Freitas, Ana Cristina Moreira. "Asymptotic Distribution of the Maximum for a Chaotic Economic Model." In Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications, 193–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34904-1_20.

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Mota, Pedro P. "On a Continuous-Time Stock Price Model with Two Mean Reverting Regimes." In Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications, 297–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34904-1_31.

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Conference papers on the topic "Survival regression model"

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Ezzeddine, Wajih, Jeremie Schutz, and Nidhal Rezg. "Cox regression model applied to Pitot tube survival data." In 2015 International Conference on Industrial Engineering and Systems Management (IESM). IEEE, 2015. http://dx.doi.org/10.1109/iesm.2015.7380153.

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Tak Hyung Lee, Ju Hyung Lee, Sang Won Chung, Hyung Wook Noh, Young Woo Shim, and Deok Won Kim. "A survival prediction model of hemorrhagic shock in rats using a logistic regression equation." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5334251.

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S. Pahl, Eric, W. Nick Street, Hans J. Johnson, and Alan I. Reed. "A Predictive Model for Kidney Transplant Graft Survival using Machine Learning." In 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101609.

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Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.
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Lu, Jo-Yang, Y. Chuang Eric, K. Hsiao Chuhsing, Mong-Hsun Tsai, Liang-Chuan Lai, and Pei-Chun Chen. "Utilizing Cox regression model to assess the relations between predefined gene sets and the survival outcome of lung adenocarcinoma." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706566.

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Mori, Masamichi, Sadao Kuromitsu, Yoko Ueno, Ruriko Tanaka, Itsuro Shimada, Yutaka Kondoh, Satoshi Konagai, et al. "Abstract 866: ASP3026, a selective ALK inhibitor, induces tumor regression in a crizotinib-refractory model and prolongs survival in an intrapleurally xenograft model." In Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/1538-7445.am2012-866.

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Yang, Guolei, Ying Cai, and Chandan K. Reddy. "Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/413.

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We introduce a novel check-in time prediction problem. The goal is to predict the time a user will check-in to a given location. We formulate check-in prediction as a survival analysis problem and propose a Recurrent-Censored Regression (RCR) model. We address the key challenge of check-in data scarcity, which is due to the uneven distribution of check-ins among users/locations. Our idea is to enrich the check-in data with potential visitors, i.e., users who have not visited the location before but are likely to do so. RCR uses recurrent neural network to learn latent representations from historical check-ins of both actual and potential visitors, which is then incorporated with censored regression to make predictions. Experiments show RCR outperforms state-of-the-art event time prediction techniques on real-world datasets.
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Farhangdoost, Khalil, and Mehran Siahpoosh. "On the Fatigue Life Prediction of Die-Marked Drillpipes." In ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/pvp2006-icpvt-11-93181.

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Drillpipe fatigue damage occurs under cyclic loading conditions due to, for instance, rotation in a dogleg region. Usually, failure mechanisms develop in the transition region of the tool joints and the die-marks due to gripping systems intensify it. In this paper two approaches are presented to evaluate damage in drillpipes; FEM and Cox Regression Model. First, Finite Element Method is used to evaluate cumulative effects of fatigue damage in a number of drilling events with respect to rotating cyclic bending and constant tension and internal pressure in a G-105 drillpipe. The results show that how die-marks or other surface crushes can reduce the fatigue life of the pipe. The presented graphs can be easily used to determine the allowable length of a G-105 drillpipe below dogleg that consumes the fatigue life of the pipe section. In the second approach, as a case study, the Cox Regression Model, a broadly applicable and the most widely used method of survival analysis is used to evaluate the distribution of survival times for the failure data of the southern oilfields of Iran. The resultant cumulative survival and hazard functions can reliably predict the time of failure and assist the engineers to evaluate cumulative damage.
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Cirqueira, Magno Belém, Carolina Rodrigues Mendonça, Leonardo Ribeiro Soares, Maria Auxiliadora de Paula Carneiro Cysneiros, Régis Resende Paulinelli, Marise Amaral Rebouças Moreira, and Ruffo de Freitas-Junior. "PROGNOSTIC SIGNIFICANCE OF PD-L1 EXPRESSION IN BREAST CANCER." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2036.

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Objective: To investigate the immunohistochemical expression of programmed cell death ligand 1 (PD-L1) in female invasive mammary carcinoma and to analyze the association of PD-L1 expression with clinicopathological characteristics, overall survival, and disease-free survival. Methodology: The expression of PD-L1 and its association with the main clinicopathological parameters have been evaluated in 232 cases. The Cox regression model was used to assess the possible association of PD-L1 expression with overall survival and disease-free survival. Results: A total of 58 cases (28.7%) were positive for PD-L1 expression. There is an association between PD-L1 expression with tumor size, negative hormone receptors, and triple-negative molecular subtype. Negative estrogen receptor and nodal status (≥10 positive lymph nodes) were associated with a reduction in overall survival, and the latter was associated with a lower disease-free survival. Luminal A tumor phenotype demonstrated a greater overall survival (p=0.042). Despite the significant association with unfavorable clinical and pathological characteristics in univariate and multivariate analyses, no significant correlation was observed between the expression of PD-L1 and overall or disease-free survival. Conclusions: Our data indicate that PD-L1 expression was associated with unfavorable clinical-pathological variables, such as greater tumor size, negative hormone receptors, and a greater number of metastatic nodes. No prognostic value was observed for the expression of PD-L1 in relation to overall survival or disease-free survival.
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"Interpretation of the features of a linear regression model for predicting the survival time of the amyotrophic lateral sclerosis patients with mutated SOD1." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-373.

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Azevedo, Wylson, Eduardo Augusto Schutz, Mayara Menezes Attuy, Thamara Graziela Flores, and Melissa Agostini Lampert. "Prediction model to delirium in hospitalized elderly people." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.478.

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Introduction: Delirium has a high prevalence in hospitalized elderly patients. This is due to low hospital detection and the absence of a screening instrument. Objective: evaluate predictive variables in the development of delirium in na in-hospital environment. Methods: Cross-sectional study. Data collection was carried out between 2015-2016, with a sample of 493 elderly people. The variables used were age, sex, the reason for hospitalization, Identification of Elderly at Risk (ISAR), delirium during hospitalization using the Confusion Assessment Method, frailty using the Edmonton Scale, the impact of comorbidities by the Charlson Index and hospital immobility. Predictive variables were identified through logistic regression. Results: 469 elderly people were taken. The presence of delirium during hospitalization was mostly observed between 80 and 89 years old (n = 12), female (n = 16), with the most common reasons for hospitalization due to fractures (n = 6) and accident brain vascular (n = 11), 79% chance of surviving in one year using the Charlson Index (n = 11) and with ISAR> 2 (n = 26). There are important associations for the development of delirium for patients who have a 98% chance of surviving in one year (p = 0.05) and with ISAR <2 (p = 0.027), with a 34% increased chance and 38%, respectively. Conclusion: It is observed that, by the results, the predictive variables of inhospital delirium are patients with a 98% chance of survival and with ISAR <2.
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Reports on the topic "Survival regression model"

1

Koenker, Roger, and Naveen Narisetty. Censored quantile regression survival models with a cure proportion. The IFS, October 2019. http://dx.doi.org/10.1920/wp.cem.2019.5619.

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2

Tummala, Rohan, Andrew de Jesus, Natasha Tillett, Jeffrey Nelson, and Christine Lamey. Clinical and Socioeconomic Predictors of Palliative Care Utilization. University of Tennessee Health Science Center, January 2021. http://dx.doi.org/10.21007/com.lsp.2020.0006.

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INTRODUCTION: Palliative care continues to gain recognition among primary care providers, as patients suffering from chronic conditions may benefit from use of this growing service. OBJECTIVES: This single-institution quality improvement study investigates the clinical characteristics and socioeconomic status (SES) of palliative care patients and identifies predictors of palliative care utilization. METHODS: Retrospective chart review was used to compare clinical and SES parameters for three groups of patients: (1) palliative care patients who attended at least one visit since the inception of the University Clinical Health Palliative Care Clinic in Memphis, TN in October 2018 (n = 61), (2) palliative care patients who did not attend any appointments (n = 19), and (3) a randomized group of age-matched primary care patients seen by one provider from May 2018 to May 2019 (n = 36). A Poisson regression model with backward conditional variable selection was used to determine predictors of palliative care utilization. RESULTS: Patients across the three care groups did not differ in demographic parameters. Compared to palliative care-referred non-users and primary care patients, palliative care patients tended to have lower health risk (p < 0.001). Palliative care patients did not differ from primary care patients in socioeconomic status but did differ in comorbidity distribution, having a higher prevalence of cancer (𝜒2 = 14.648, df = 7, p = 0.041). Chance of 10-year survival did not differ across risk categories for palliative care patients but was significantly lower for very high-risk compared to moderate-risk primary care patients (30% vs. 78%, p = 0.019). Significant predictors of palliative care use and their corresponding incidence rate ratios (IRR) were hospital referral (IRR = 1.471; p = 0.039), higher number of prescribed medications (IRR = 1.045; p = 0.003), lower Charlson Comorbidity Index (IRR = 0.907; p = 0.003), and lower systolic blood pressure (IRR = 0.989; p = 0.004). CONCLUSIONS: Patients who are expected to benefit from and of being high utilizers of palliative care may experience greater clinical benefit from earlier referral to this service.
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