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Journal articles on the topic 'Prediction of survival; Probability; Time models'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Lan, Yu, and Daniel F. Heitjan. "Adaptive parametric prediction of event times in clinical trials." Clinical Trials 15, no. 2 (2018): 159–68. http://dx.doi.org/10.1177/1740774517750633.

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Background: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. Purpose: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric meth

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Gensheimer, Michael F., and Balasubramanian Narasimhan. "A scalable discrete-time survival model for neural networks." PeerJ 7 (January 25, 2019): e6257. http://dx.doi.org/10.7717/peerj.6257.

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There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD).

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Ren, Kan, Jiarui Qin, Lei Zheng, et al. "Deep Recurrent Survival Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4798–805. http://dx.doi.org/10.1609/aaai.v33i01.33014798.

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Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential p

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Li, Kan, and Sheng Luo. "Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease." Statistical Methods in Medical Research 28, no. 2 (2017): 327–42. http://dx.doi.org/10.1177/0962280217722177.

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In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and sur

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Alemazkoor, Negin, Conrad J. Ruppert, and Hadi Meidani. "Survival analysis at multiple scales for the modeling of track geometry deterioration." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 3 (2017): 842–50. http://dx.doi.org/10.1177/0954409717695650.

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Defects in track geometry have a notable impact on the safety of rail transportation. In order to make the optimal maintenance decisions to ensure the safety and efficiency of railroads, it is necessary to analyze the track geometry defects and develop reliable defect deterioration models. In general, standard deterioration models are typically developed for a segment of track. As a result, these coarse-scale deterioration models may fail to predict whether the isolated defects in a segment will exceed the safety limits after a given time period or not. In this paper, survival analysis is used

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Sun, Zhaohong, Wei Dong, Jinlong Shi, Kunlun He, and Zhengxing Huang. "Attention-Based Deep Recurrent Model for Survival Prediction." ACM Transactions on Computing for Healthcare 2, no. 4 (2021): 1–18. http://dx.doi.org/10.1145/3466782.

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Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attenti

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Tan, Ping, Lu Yang, Hang Xu, and Qiang Wei. "Novel perioperative parameters-based nomograms for survival outcomes in upper tract urothelial carcinoma after radical nephroureterectomy." Journal of Clinical Oncology 37, no. 7_suppl (2019): 414. http://dx.doi.org/10.1200/jco.2019.37.7_suppl.414.

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414 Background: Recently, several postoperative nomograms for cancer-specific survival (CSS) after radical nephroureterectomy (RNU) were proposed, while they did not incorporate the same variables; meanwhile, many preoperative blood-based parameters, which were recently reported to be related to survival, were not included in their models. In addition, no nomogram for overall survival (OS) was available to date. Methods: The full data of 716 patients were available. The whole cohort was randomly divided into two cohorts: the training cohort for developing the nomograms (n = 508) and the valida

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Liu, Xing-Rong, Yudi Pawitan, and Mark Clements. "Parametric and penalized generalized survival models." Statistical Methods in Medical Research 27, no. 5 (2016): 1531–46. http://dx.doi.org/10.1177/0962280216664760.

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We describe generalized survival models, where g( S( t| z)), for link function g, survival S, time t, and covariates z, is modeled by a linear predictor in terms of covariate effects and smooth time effects. These models include proportional hazards and proportional odds models, and extend the parametric Royston–Parmar models. Estimation is described for both fully parametric linear predictors and combinations of penalized smoothers and parametric effects. The penalized smoothing parameters can be selected automatically using several information criteria. The link function may be selected base

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Andrinopoulou, Eleni-Rosalina, D. Rizopoulos, Johanna JM Takkenberg, and E. Lesaffre. "Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data." Statistical Methods in Medical Research 26, no. 4 (2015): 1787–801. http://dx.doi.org/10.1177/0962280215588340.

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Nowadays there is an increased medical interest in personalized medicine and tailoring decision making to the needs of individual patients. Within this context our developments are motivated from a Dutch study at the Cardio-Thoracic Surgery Department of the Erasmus Medical Center, consisting of patients who received a human tissue valve in aortic position and who were thereafter monitored echocardiographically. Our aim is to utilize the available follow-up measurements of the current patients to produce dynamically updated predictions of both survival and freedom from re-intervention for futu

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Liu, Chuchu, Anja J. Rueten-Budde, Andreas Ranft, Uta Dirksen, Hans Gelderblom, and Marta Fiocco. "Dynamic prediction of overall survival: a retrospective analysis on 979 patients with Ewing sarcoma from the German registry." BMJ Open 10, no. 10 (2020): e036376. http://dx.doi.org/10.1136/bmjopen-2019-036376.

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ObjectivesThis study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient’s prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.SettingIn the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.DesignA dynamic pre

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Garber, Sean M., John P. Brown, Duncan S. Wilson, Douglas A. Maguire, and Linda S. Heath. "Snag longevity under alternative silvicultural regimes in mixed-species forests of central Maine." Canadian Journal of Forest Research 35, no. 4 (2005): 787–96. http://dx.doi.org/10.1139/x05-021.

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Predictions of snag longevity, defined here as the probability of snag survival to a given age, are key to designing silvicultural regimes that ensure their availability for wildlife and form an important component of carbon flow models. Species, diameter at breast height, stand density, management regime, and agent of tree mortality were assessed for their effect on snag longevity in a long-term silvicultural study on the Penobscot Experimental Forest in central Maine. Snag recruitment and fall data from USDA Forest Service inventories between 1981 and 1997 were analyzed using parametric surv

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Chen, David, Gaurav Goyal, Ronald S. Go, Sameer A. Parikh, and Che G. Ngufor. "Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–11. http://dx.doi.org/10.1200/cci.18.00137.

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PURPOSE Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can readily learn complex nonlinear relationships, many methods are criticized as inadequate because of limited interpretability. We propose using unsupervised clustering of the continuous output of machine learning models to provide discrete risk stratification for predicting time to first treatment in a cohort of patients with CLL. PATIENTS AND METHOD

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Ma, Junsheng, Brian P. Hobbs, and Francesco C. Stingo. "Integrating genomic signatures for treatment selection with Bayesian predictive failure time models." Statistical Methods in Medical Research 27, no. 7 (2016): 2093–113. http://dx.doi.org/10.1177/0962280216675373.

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Over the past decade, a tremendous amount of resources have been dedicated to the pursuit of developing genomic signatures that effectively match patients with targeted therapies. Although dozens of therapies that target DNA mutations have been developed, the practice of studying single candidate genes has limited our understanding of cancer. Moreover, many studies of multiple-gene signatures have been conducted for the purpose of identifying prognostic risk cohorts, and thus are limited for selecting personalized treatments. Existing statistical methods for treatment selection often model tre

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Zheng, Panpan, Shuhan Yuan, and Xintao Wu. "SAFE: A Neural Survival Analysis Model for Fraud Early Detection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1278–85. http://dx.doi.org/10.1609/aaai.v33i01.33011278.

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Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based

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Shafipour, Gholamreza, and Abdolvahhab Fetanat. "Survival analysis in supply chains using statistical flowgraph models: Predicting time to supply chain disruption." Communications in Statistics - Theory and Methods 45, no. 21 (2016): 6183–208. http://dx.doi.org/10.1080/03610926.2014.957856.

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Yamaguchi, S., C. Lee, O. Karaer, S. Ban, A. Mine, and S. Imazato. "Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI." Journal of Dental Research 98, no. 11 (2019): 1234–38. http://dx.doi.org/10.1177/0022034519867641.

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A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division

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Agrawal, Smita, Vivek Vaidya, Prajwal Chandrashekaraiah, et al. "Development of an artificial intelligence model to predict survival at specific time intervals for lung cancer patients." Journal of Clinical Oncology 37, no. 15_suppl (2019): 6556. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.6556.

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6556 Background: Survival prediction models for lung cancer patients could help guide their care and therapy decisions. The objectives of this study were to predict probability of survival beyond 90, 180 and 360 days from any point in a lung cancer patient’s journey. Methods: We developed a Gradient Boosting model (XGBoost) using data from 55k lung cancer patients in the ASCO CancerLinQ database that used 3958 unique variables including Dx and Rx codes, biomarkers, surgeries and lab tests from ≤1 year prior to the prediction point, which was chosen at random for each patient. We used 40% data

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Stanojevic, Sanja, Jenna Sykes, Anne L. Stephenson, Shawn D. Aaron, and George A. Whitmore. "Development and external validation of 1- and 2-year mortality prediction models in cystic fibrosis." European Respiratory Journal 54, no. 3 (2019): 1900224. http://dx.doi.org/10.1183/13993003.00224-2019.

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IntroductionWe aimed to develop a clinical tool for predicting 1- and 2-year risk of death for patients with cystic fibrosis (CF). The model considers patients' overall health status as well as risk of intermittent shock events in calculating the risk of death.MethodsCanadian CF Registry data from 1982 to 2015 were used to develop a predictive risk model using threshold regression. A 2-year risk of death estimated conditional probability of surviving the second year given survival for the first year. UK CF Registry data from 2007 to 2013 were used to externally validate the model.ResultsThe co

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Barbieri, Antoine, and Catherine Legrand. "Joint longitudinal and time-to-event cure models for the assessment of being cured." Statistical Methods in Medical Research 29, no. 4 (2019): 1256–70. http://dx.doi.org/10.1177/0962280219853599.

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Medical time-to-event studies frequently include two groups of patients: those who will not experience the event of interest and are said to be “cured” and those who will develop the event and are said to be “susceptible”. However, the cure status is unobserved in (right-)censored patients. While most of the work on cure models focuses on the time-to-event for the uncured patients (latency) or on the baseline probability of being cured or not (incidence), we focus in this research on the conditional probability of being cured after a medical intervention given survival until a certain time. As

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Joffe, Erel, Kevin R. Coombes, Yi Hua Qiu, et al. "Survival Prediction In High Dimensional Datasets – Comparative Evaluation Of Lasso Regularization and Random Survival Forests." Blood 122, no. 21 (2013): 1728. http://dx.doi.org/10.1182/blood.v122.21.1728.1728.

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Abstract Background High-dimensional data obtained using modern molecular technologies (e.g., gene expression, proteomics) and large clinical datasets is increasingly common. Risk stratification based on such high-dimensional data remains challenging. Traditional statistical models have a limited capability of handling large numbers of variables, non-linear effects, correlations and missing data. More importantly, as more variables are analyzed, models tend to over-fit (i.e., the model provides good predictions on the studied data but performs poorly on other data). Recently two methods have b

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Hong, Fangxin, Brad S. Kahl, and Robert Gray. "Incremental Value in Outcome Prediction with Molecular Signatures in Diffuse Large B-Cell Lymphoma,." Blood 118, no. 21 (2011): 3687. http://dx.doi.org/10.1182/blood.v118.21.3687.3687.

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Abstract Abstract 3687 INTRODUCTION: Multiple gene expression-based biomarkers have been identified in diffuse large B-cell lymphoma (DLBCL) that are predictive for survival outcomes. Most studies assess predictive significance based on p-value from multivariate Cox regression; few investigations have evaluated the incremental usefulness of these biomarkers in risk prediction. Using the recently developed concordance measures (e.g., C-statistics) on censored survival data, we assessed the usefulness of two published gene-based risk signatures and compared them to the known clinical prognostic

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Rizopoulos, Dimitris, Jeremy M. G. Taylor, Joost Van Rosmalen, Ewout W. Steyerberg, and Johanna J. M. Takkenberg. "Personalized screening intervals for biomarkers using joint models for longitudinal and survival data." Biostatistics 17, no. 1 (2015): 149–64. http://dx.doi.org/10.1093/biostatistics/kxv031.

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Abstract Screening and surveillance are routinely used in medicine for early detection of disease and close monitoring of progression. Motivated by a study of patients who received a human tissue valve in the aortic position, in this work we are interested in personalizing screening intervals for longitudinal biomarker measurements. Our aim in this paper is 2-fold: First, to appropriately select the model to use at the time point the patient was still event-free, and second, based on this model to select the optimal time point to plan the next measurement. To achieve these two goals, we combin

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Chen, Bo-Huan, Hsiao-Jung Tseng, Wei-Ting Chen, et al. "Comparing Eight Prognostic Scores in Predicting Mortality of Patients with Acute-On-Chronic Liver Failure Who Were Admitted to an ICU: A Single-Center Experience." Journal of Clinical Medicine 9, no. 5 (2020): 1540. http://dx.doi.org/10.3390/jcm9051540.

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Limited data is available on long-term outcome predictions for patients with acute-on-chronic liver failure (ACLF) in an intensive care unit (ICU) setting. Assessing the reliability and accuracy of several mortality prediction models for these patients is helpful. Two hundred forty-nine consecutive patients with ACLF and admittance to the liver ICU in a single center in northern Taiwan between December 2012 and March 2015 were enrolled in the study and were tracked until February 2017. Ninety-one patients had chronic hepatitis B-related cirrhosis. Clinical features and laboratory data were col

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Torkey, Hanaa, Mostafa Atlam, Nawal El-Fishawy, and Hanaa Salem. "A novel deep autoencoder based survival analysis approach for microarray dataset." PeerJ Computer Science 7 (April 21, 2021): e492. http://dx.doi.org/10.7717/peerj-cs.492.

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Background Breast cancer is one of the major causes of mortality globally. Therefore, different Machine Learning (ML) techniques were deployed for computing survival and diagnosis. Survival analysis methods are used to compute survival probability and the most important factors affecting that probability. Most survival analysis methods are used to deal with clinical features (up to hundreds), hence applying survival analysis methods like cox regression on RNAseq microarray data with many features (up to thousands) is considered a major challenge. Methods In this paper, a novel approach applyin

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Claret, L., J. Lu, Y. Sun, D. Stepan, and R. Bruno. "A modeling framework to simulate motesanib efficacy in thyroid cancer." Journal of Clinical Oncology 27, no. 15_suppl (2009): e14553-e14553. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e14553.

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e14553 Background: Motesanib is a highly selective, oral inhibitor of VEGF receptors 1, 2, and 3; PDGFR, and Kit with antiangiogenic and direct antitumor activity. A modeling framework that simulates clinical endpoints, including objective response rate (ORR; per RECIST) and progression-free survival (PFS), was developed to support clinical development of motesanib. This study evaluated the framework using results from a trial of motesanib in thyroid cancer (TC). Methods: Models for tumor growth inhibition (J Clin Oncol 24[18S]:abstract 6025, 2006) with drug effect driven by area under the cur

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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 (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

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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

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Choi, Jiin, Stewart J. Anderson, Thomas J. Richards, and Wesley K. Thompson. "Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data." Journal of Applied Statistics 41, no. 10 (2014): 2192–205. http://dx.doi.org/10.1080/02664763.2014.909784.

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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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Seibold, Heidi, Achim Zeileis, and Torsten Hothorn. "Individual treatment effect prediction for amyotrophic lateral sclerosis patients." Statistical Methods in Medical Research 27, no. 10 (2017): 3104–25. http://dx.doi.org/10.1177/0962280217693034.

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A treatment for a complicated disease might be helpful for some but not all patients, which makes predicting the treatment effect for new patients important yet challenging. Here we develop a method for predicting the treatment effect based on patient characteristics and use it for predicting the effect of the only drug (Riluzole) approved for treating amyotrophic lateral sclerosis. Our proposed method of model-based random forests detects similarities in the treatment effect among patients and on this basis computes personalised models for new patients. The entire procedure focuses on a base

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Yang, Ming, Sheng Luo, and Stacia DeSantis. "Bayesian quantile regression joint models: Inference and dynamic predictions." Statistical Methods in Medical Research 28, no. 8 (2018): 2524–37. http://dx.doi.org/10.1177/0962280218784757.

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In the traditional joint models of a longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the longitudinal process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the longitudinal process. To this end, quantile regression provides

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Korepanova, Natalia, Heidi Seibold, Verena Steffen, and Torsten Hothorn. "Survival forests under test: Impact of the proportional hazards assumption on prognostic and predictive forests for amyotrophic lateral sclerosis survival." Statistical Methods in Medical Research 29, no. 5 (2019): 1403–19. http://dx.doi.org/10.1177/0962280219862586.

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We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis. We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, including random forests for survival, conditional inference forests, Ranger, and survival forests with L1 splitting, with two novel variants, namely distributional and transformation survival forests. Theoretical considerations explain the low power of log-rank-based splitting in detecting

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Rui Lam, Amanda Yun, Min Min Chan, David Carmody, et al. "Predicting Major Adverse Cardiovascular Events in Asian Type 2 Diabetes Patients With Lasso-Cox Regression." Journal of the Endocrine Society 5, Supplement_1 (2021): A417—A418. http://dx.doi.org/10.1210/jendso/bvab048.852.

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Abstract Background: South-East Asia has seen a dramatic increase in type 2 diabetes (T2D). Risk prediction models for Major adverse cardiovascular events (MACE) identify patients who may benefit most from intensive prevention strategies. Existing risk prediction models for T2D were developed mainly in Caucasian populations, limiting their generalizability to Asian populations. We developed a Lasso-Cox regression model to predict the 5-year risk of incident MACE in Asian patients with T2DM using data from the largest diabetes registry in Singapore. Methodology: The diabetes registry contained

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Fang, Hong-Bin, Tong Tong Wu, Aaron P. Rapoport, and Ming Tan. "Survival analysis with functional covariates for partial follow-up studies." Statistical Methods in Medical Research 25, no. 6 (2016): 2405–19. http://dx.doi.org/10.1177/0962280214523586.

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Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received

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Durand, J., S. Pourchet, and F. Goldwasser. "Prediction of short-term outcome in terminally ill cancer patients." Journal of Clinical Oncology 25, no. 18_suppl (2007): 19572. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.19572.

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19572 Background: Oncologists tend to overestimate survival of advanced cancer patients (pts) emphasizing the need for objective prognostic models to prevent futility. Methods: From January 2004 to May 2006, a prospective unicenter study was done in terminally ill cancer pts referred to a palliative care unit. Evaluation at admission included physical examination and the following routine blood tests : total blood count, hemostasis (Prothrombin Time (PT), activated Partial Thromboplastin Time (aPTT)), inflammatory and nutritional proteins (fibrinogen, ferritin, PINI = [alpha-1 acid glycoprotei

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Wu, Lunpo, Hongjuan Zheng, Jianfei Fu, Jinlin Du, Shu Zheng, and Liangjing Wang. "The “effect” of T classification on colorectal liver metastasis." Journal of Clinical Oncology 37, no. 15_suppl (2019): e15049-e15049. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e15049.

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e15049 Background: T classification is considered as a detail and credible category of the depth of tumor invasion. Generally, with the increasing T category, the risk of metastases should be continuously rising. However, there is a group of metastatic patients with early T classification, who were supposed to have a low metastatic probability. Our study aims to present the T classification on metastatic liver colorectal cancer (CRLM) in both clinical and biological aspects, and explore preoperative predictions to develop a convenient individual assessment model for clinicians to speculate whe

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Morin, Amy A., Alisha Albert-Green, Douglas G. Woolford, and David L. Martell. "The use of survival analysis methods to model the control time of forest fires in Ontario, Canada." International Journal of Wildland Fire 24, no. 7 (2015): 964. http://dx.doi.org/10.1071/wf14158.

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This paper presents the results from employing survival analysis methods to model the probability distribution of the control time of forest fires. The Kaplan–Meier estimator, log–location–scale models, accelerated failure time models, and Cox proportional hazards (PH) models are described. Historical lightning and people-caused forest fire data from the Province of Ontario, Canada from 1989 through 2004 are employed to illustrate the use of the Cox PH model. We demonstrate how this methodology can be used to examine the association between the control time of a suppressed forest fire and loca

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Wijenayake, Pavithra Rangani, and Takuya Hiroshima. "Prediction of Tree Age Distribution Based on Survival Analysis in Natural Forests: A Case Study of Preserved Permanent Plots in the University of Tokyo Hokkaido Forest, Northern Japan." Environmental Sciences Proceedings 3, no. 1 (2020): 50. http://dx.doi.org/10.3390/iecf2020-08077.

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In forests, tree mortality is strongly determined by complex interactions between multiple biotic and abiotic factors, and the analysis of tree mortality is widely implemented in forest management. However, age-based tree mortality remains poorly evaluated quantitatively at the stand scale for unevenly aged forests. The objective of this study was to predict the age distributions of living and dead trees based on survival analyses. We used a combination of tree-ring and census data from the two preserved permanent plots in the University of Tokyo Hokkaido Forest in pan-mixed and sub-boreal nat

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Wierda, William G., S. OBrien, X. Wang, et al. "Weighted Prognostic Models for Survival in Untreated and Previously Treated Patients with CLL." Blood 106, no. 11 (2005): 5012. http://dx.doi.org/10.1182/blood.v106.11.5012.5012.

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Abstract The clinical course for patients with chronic lymphocytic leukemia (CLL) is remarkably variable. Patient characteristics have been correlated with meaningful clinical endpoints such as time to treatment, response to treatment, progression-free survival, and overall survival (OS) for patients with CLL. Identification of such characteristics enables informed discussion about timing of treatment, treatment options, and can provide insight into the basic biology of the disease. The Rai staging system identifies risk groups for survival based on characteristics in untreated patients. Howev

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Dueñas-Jurado, J. M., P. A. Gutiérrez, A. Casado-Adam, et al. "New models for donor-recipient matching in lung transplantations." PLOS ONE 16, no. 6 (2021): e0252148. http://dx.doi.org/10.1371/journal.pone.0252148.

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Objective One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process. Methods This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtaine

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Petros, Firas G., Aradhana M. Venkatesan, Diana Kaya, et al. "Conditional survival and landmark analysis for patients with small renal masses undergoing active surveillance at a tertiary care center." Journal of Clinical Oncology 36, no. 6_suppl (2018): 609. http://dx.doi.org/10.1200/jco.2018.36.6_suppl.609.

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609 Background: Conditional survival can provide guidance for patients once they have survived a period of time after diagnosis of their disease. We determine conditional survival for patients with small renal masses (SRM) undergoing active surveillance (AS). Methods: Patients were enrolled in a prospective AS registry at our institution between May 2005 and January 2016. Patients with localized SRM ≤4cm were included, with serial radiologic imaging available in-house for re-review. Overall survival (OS) was estimated using the Kaplan-Meier method and modeled via Cox proportional hazards model

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Ambrose, Paul G., Alan Forrest, William A. Craig, et al. "Pharmacokinetics-Pharmacodynamics of Gatifloxacin in a Lethal Murine Bacillus anthracis Inhalation Infection Model." Antimicrobial Agents and Chemotherapy 51, no. 12 (2007): 4351–55. http://dx.doi.org/10.1128/aac.00251-07.

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ABSTRACT We determined the pharmacokinetic-pharmacodynamic (PK-PD) measure most predictive of gatifloxacin efficacy and the magnitude of this measure necessary for survival in a murine Bacillus anthracis inhalation infection model. We then used population pharmacokinetic models for gatifloxacin and simulation to identify dosing regimens with high probabilities of attaining exposures likely to be efficacious in adults and children. In this work, 6- to 8-week-old nonneutropenic female BALB/c mice received aerosol challenges of 50 to 75 50% lethal doses of B. anthracis (Ames strain, for which the

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Kovtun, N. V., I. M. Motuziuk, and R. O. Ganzha. "Using Cox Regression to Forecast of Survival of Women with Multiple Malignant Neoplasms." Statistics of Ukraine 83, no. 4 (2018): 65–71. http://dx.doi.org/10.31767/su.4(83)2018.04.08.

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Recently, an increase in the incidence of multiple primary malignant neoplasms has been observed, specifically, when two or more unrelated tumors originate from different organs and appear in the body simultaneously or sequentially, one after another. During past few years, the interval between the first and second reproductive cancer diagnosis has decreased in 6 times – from 11 to just 2 years while probability of surviving the next 3 years after 8.5 years past initial diagnosis has decreased from 0.995 to 0.562. Using performed analysis, this paper provides details of survival modelling for

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Schluchter, Mark D., and Annalisa V. Piccorelli. "Shared parameter models for joint analysis of longitudinal and survival data with left truncation due to delayed entry – Applications to cystic fibrosis." Statistical Methods in Medical Research 28, no. 5 (2018): 1489–507. http://dx.doi.org/10.1177/0962280218764193.

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Many longitudinal studies observe time to occurrence of a clinical event such as death, while also collecting serial measurements of one or more biomarkers that are predictive of the event, or are surrogate outcomes of interest. Joint modeling can be used to examine the relationship between the biomarker and the event, and also as a way of adjusting analyses of the biomarker for non-ignorable dropout. In settings such as registry studies, an additional complexity is caused when follow-up of subjects is delayed, referred to as left-truncation of follow-up in the survival analysis setting. If no

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Avila, Olga B., and Harold E. Burkhart. "Modeling survival of loblolly pine trees in thinned and unthinned plantations." Canadian Journal of Forest Research 22, no. 12 (1992): 1878–82. http://dx.doi.org/10.1139/x92-245.

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The probability of survival for an individual tree was modeled. A variable screening algorithm, screen, was used to find the best set of predictor variables. Stepwise procedures in SAS and BMDP were also used, and results were compared with those obtained from the SCREEN algorithm. The logistic model, with independent variables that were found to be significant through the SCREEN algorithm, was fitted to the data. The fitted models were validated by splitting the data and applying equations fitted to the estimation set to the data in the testing set. Two methods of data splitting were applied:

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Hummel, Manuela, Thomas Hielscher, Hans Jürgen Salwender, et al. "Quantitative Integrative Prediction of Survival Probability in Multiple Myeloma Using Molecular and Clinical Prognostic Factors in 657 Patients Treated with Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation." Blood 132, Supplement 1 (2018): 403. http://dx.doi.org/10.1182/blood-2018-99-113307.

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Abstract Background Survival in multiple myeloma ranges from months to decades and the majority of patients remain incurable with current treatment approaches. Given this high variability, it would be clinically very useful to quantitatively predict survival on a continuous scale. Current risk prediction models attribute patients to 2-3 groups, i.e. high, intermediate, and low risk. Group size and survival rates largely vary between different systems. Rarely, molecular prognostic factors beyond iFISH are used. Widely accepted standard is the revised ISS score (rISS) including serum B2M, albumi

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Simoes, J. P. C., P. Schoning, and M. Butine. "Prognosis of Canine Mast Cell Tumors: A Comparison of Three Methods." Veterinary Pathology 31, no. 6 (1994): 637–47. http://dx.doi.org/10.1177/030098589403100602.

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In this study, age, sex, recurrence, metastasis, death rate, and histologic patterns were in agreement with those of previous reports on canine mast cell tumors. Histologic grading, mitotic index, chromosome nucleolar organizer regions stained with silver (AgNORs), and anti-proliferating cell nuclear antigen (PCNA) were evaluated as indicators of prognosis. Histologic grading, AgNORs estimated in 100 cells, and PCNA-labeled fraction estimated in five high power fields (HPFs) were significantly different between recurring and nonrecurring tumors. Those prognostic factors were also significantly

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Elsensohn, MH, E. Dantony, J. Iwaz, E. Villar, C. Couchoud, and R. Ecochard. "Improving survival in end-stage renal disease: A case study." Statistical Methods in Medical Research 28, no. 12 (2018): 3579–90. http://dx.doi.org/10.1177/0962280218811357.

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Background: With the increase of life expectancy, *On behalf of the REIN registry. end-stage renal disease (ESRD) is affecting a growing number of people. Simultaneously, renal replacement therapies (RRTs) have considerably improved patient survival. We investigated the way current RRT practices would affect patients' survival. Methods: We used a multi-state model to represent the transitions between RRTs and the transition to death. The concept of “crude probability of death” combined with this model allowed estimating the proportions of ESRD-related and ESRD-unrelated deaths. Estimating the

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Shouval, Roni, Joshua A. Fein, Myriam Labopin, et al. "Prediction of Leukemia-Free Survival Following Haploidentical Stem Cell Transplantation in Acute Myeloid Leukemia: A Study from the Acute Leukemia Working Party of the EBMT." Blood 132, Supplement 1 (2018): 485. http://dx.doi.org/10.1182/blood-2018-99-111822.

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Abstract Background: Haploidentical (Haplo) stem cell transplantation (SCT) provide a curative option for nearly all Acute Myeloid Leukemia (AML) patients lacking an HLA matched donor. However, outcomes following Haplo-SCT vary and are dependent on a number of individual features. Integrative prognostic models for decision support towards a Haplo-SCT are lacking. We sought to develop a prediction model of Leukemia-Free Survival (LFS) for AML patients undergoing a Haplo-SCT. Methods: A total of 1,804 de-novo (80%) and secondary (20%) AML patients who received a non-T-cell depleted Haplo-SCT bet

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Kawakami, Takeshi, Yukiya Narita, Isao Oze, et al. "Establishment and validation of prognostic nomograms including HER2 status in metastatic gastric cancer." Journal of Clinical Oncology 34, no. 4_suppl (2016): 24. http://dx.doi.org/10.1200/jco.2016.34.4_suppl.24.

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24 Background: It remains unclear whether human epidermal growth factor receptor 2 (HER2) status is an outcome-associated biomarker independent of known prognostic factors for metastatic gastric cancer (MGC). There are few reports on nomograms in MGC, while several studies have been published on nomograms for other cancer types. This retrospective study aimed to develop nomograms that combine HER2 status and other prognostic factors for predicting survival outcome of individual patients with MGC starting first-line treatment. Methods: We used a training set of 838 consecutive patients with MGC

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