標(biāo)題: Titlebook: Emerging Topics in Modeling Interval-Censored Survival Data; Jianguo Sun,Ding-Geng Chen Book 2022 The Editor(s) (if applicable) and The Au [打印本頁(yè)] 作者: 大口水罐 時(shí)間: 2025-3-21 16:05
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書目名稱Emerging Topics in Modeling Interval-Censored Survival Data讀者反饋學(xué)科排名
作者: Misgiving 時(shí)間: 2025-3-21 23:47 作者: Cholesterol 時(shí)間: 2025-3-22 02:14 作者: SCORE 時(shí)間: 2025-3-22 06:05
Non-linear algebraic equations,rt of this chapter, we introduce a general goodness-of-fit test procedure for copula-based interval-censored data using the information ratio (IR). It can be applied to any copula family with a parametric form, such as the frequently used Archimedean and Gaussian families. Finally, we present an R p作者: 租約 時(shí)間: 2025-3-22 09:31 作者: SKIFF 時(shí)間: 2025-3-22 15:39 作者: SKIFF 時(shí)間: 2025-3-22 18:34
Linear Integral Equations in One Variable,ced number of unknown parameters. In this project, we will illustrate the key characteristics of the sieve nonparametric maximum likelihood estimation with emphasis on numerical computation. We will develop an R-based software to facilitate the public use for computing the spline-based sieve nonpara作者: DEFER 時(shí)間: 2025-3-22 21:15
Ordinary Differential Equations,mitations of the methods and equipment. Up to this date, there is no consensus as to how data under the limit of quantitation or even detection should be treated..In this chapter, we treat the concentration of these compounds as interval-censored random variables with lower and upper limits given by作者: 凹室 時(shí)間: 2025-3-23 02:27 作者: 越自我 時(shí)間: 2025-3-23 05:38 作者: Antioxidant 時(shí)間: 2025-3-23 11:19 作者: 隼鷹 時(shí)間: 2025-3-23 14:10 作者: Fierce 時(shí)間: 2025-3-23 18:49 作者: PLE 時(shí)間: 2025-3-24 02:01
Regression Analysis with Interval-Censored Covariates. Application to Liquid Chromatographymitations of the methods and equipment. Up to this date, there is no consensus as to how data under the limit of quantitation or even detection should be treated..In this chapter, we treat the concentration of these compounds as interval-censored random variables with lower and upper limits given by作者: Monotonous 時(shí)間: 2025-3-24 04:55
Emerging Topics in Modeling Interval-Censored Survival Data作者: 稀釋前 時(shí)間: 2025-3-24 08:21 作者: Derogate 時(shí)間: 2025-3-24 13:49 作者: BARGE 時(shí)間: 2025-3-24 16:46 作者: 嘲弄 時(shí)間: 2025-3-24 19:37 作者: 安裝 時(shí)間: 2025-3-25 02:49
Numbers, Information and Complexitycrimination measure. Also, Brier score is dealt to evaluate overall performance for the prediction model. We aim to provide clarity of each method and identify software tools to carry out analysis in practice. We illustrate the methods using a dementia dataset.作者: Noisome 時(shí)間: 2025-3-25 07:18
https://doi.org/10.1007/978-981-19-9263-6 time-dependent covariates. Simulation results demonstrate the proposed estimator is approximately unbiased and the standard errors are well estimated from the sandwich estimators. The methods are applied to an observational study which examined the association between hormonal contraceptive use and risk of HIV acquisition.作者: 惡臭 時(shí)間: 2025-3-25 10:07 作者: MARS 時(shí)間: 2025-3-25 12:05
Book 2022deling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censored survival data can easily be mistaken for typical right-censored survival data, which can result in erroneous statistical inference due to the complexity of作者: 胖人手藝好 時(shí)間: 2025-3-25 18:42
2199-0980 analyze interval-censored data.Details R/SAS implementations.This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biome作者: 最后一個(gè) 時(shí)間: 2025-3-25 21:54
Example II: The Neumann Probleman model and as such INLA can be used for near real-time Bayesian inference. We provide a brief summary of the INLA methodology and illustrate the approach on real data examples with interval censoring, including a joint model. The analysis is done using the . package . and all code is available for reproducibility.作者: Entropion 時(shí)間: 2025-3-26 04:05 作者: 缺乏 時(shí)間: 2025-3-26 07:36
Structured Eigenvalue Perturbation Theory obtained using the sieve maximum likelihood method, and the resulting estimators are shown to be consistent and asymptotically normal under mild regularity conditions. The finite sample performance of these models is examined through simulation studies and their practical applications are illustrated by real data examples.作者: pancreas 時(shí)間: 2025-3-26 08:30
https://doi.org/10.1007/978-1-4842-5064-8l-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.作者: 整潔 時(shí)間: 2025-3-26 13:04 作者: Thymus 時(shí)間: 2025-3-26 17:42 作者: Daily-Value 時(shí)間: 2025-3-26 23:32
Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated l-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.作者: 南極 時(shí)間: 2025-3-27 03:54
Overview of Historical Developments in Modeling Interval-Censored Survival Datadesigns. However, the well-developed methods that are used for right-censored survival data analysis could not be applied. This chapter follows on the methodological development of this area, starting in 1970 from a broad historical perspective.作者: 樣式 時(shí)間: 2025-3-27 06:31
Predictive Accuracy of Prediction Model for Interval-Censored Datacrimination measure. Also, Brier score is dealt to evaluate overall performance for the prediction model. We aim to provide clarity of each method and identify software tools to carry out analysis in practice. We illustrate the methods using a dementia dataset.作者: 取之不竭 時(shí)間: 2025-3-27 10:30 作者: temperate 時(shí)間: 2025-3-27 16:27
Joint Modeling for Longitudinal and Interval-Censored Survival Data: Application to IMPI Multi-Centeling framework has mainly been focused on right-censored data in the survival outcome for the last decade. This chapter is then aimed to extend the classical joint modeling framework to interval-censored data using a cardiology multi-center clinical trial. We illustrate our approach using . statistical software.作者: Legion 時(shí)間: 2025-3-27 20:58
Overview of Historical Developments in Modeling Interval-Censored Survival Datadescription of how the interval-censored data arise from the studies where the subjects are followed periodically, and the time to the event of interest cannot be observed exactly. From a historical perspective, such data become more common with the emergence of new clinical and epidemiologic study 作者: Cougar 時(shí)間: 2025-3-28 01:43
Overview of Recent Advances on the Analysis of Interval-Censored Failure Time Datacur in many areas, including demographical studies, epidemiological studies, medical or public health research and social science. In contrast to the historic review of Chap. 1, this chapter will provide a brief review of some recent advances on several topics concerning the analysis of interval-cen作者: Microgram 時(shí)間: 2025-3-28 03:49
Predictive Accuracy of Prediction Model for Interval-Censored Datatcomes. In this chapter, our purpose is to review several methods to evaluate prediction models and to compare their performance in a context of interval censored data. This chapter provides conceptual and practical explanation of statistical methods for the time-dependent ROC and C-index as the dis作者: 松馳 時(shí)間: 2025-3-28 10:05
A Practical Guide to Exact Confidence Intervals for a Distribution of Current Status Data Using the were based on asymptotic distributions, but our new approach is based on binomial properties. This binomial approach can be applied with continuous and discrete assessment distributions. We discuss confidence interval (CI) versions using the binomial approach, a valid (i.e., exact) CI and an ABA (A作者: languor 時(shí)間: 2025-3-28 13:45
Accelerated Hazards Model and Its Extensions for Interval-Censored Datanal odds and accelerated failure time models. There are cases in practice that such conventional model assumptions may be inappropriate for modeling survival outcomes of interest. In this chapter, we introduce an alternative, the accelerated hazards model, for the analysis of interval-censored data 作者: etidronate 時(shí)間: 2025-3-28 15:33
Maximum Likelihood Estimation of Semiparametric Regression Models with Interval-Censored Dataobserved at an exact time point but is rather known to occur within a time interval induced by periodic examinations. We formulate the effects of potentially time-dependent covariates on the failure time through the semiparametric Cox proportional hazards model. We study nonparametric maximum likeli作者: Dappled 時(shí)間: 2025-3-28 19:37 作者: 不發(fā)音 時(shí)間: 2025-3-29 01:15
Copula Models and Diagnostics for Multivariate Interval-Censored Data from the non-fatal events are sometimes unobservable due to “interval-censoring” since the event status can only be determined at intermittent assessment times. In this chapter, we introduce a class of copula models to analyze multivariate interval-censored outcomes. It is a joint approach that dir作者: 主講人 時(shí)間: 2025-3-29 05:55 作者: Sputum 時(shí)間: 2025-3-29 08:41 作者: 男生戴手銬 時(shí)間: 2025-3-29 12:13 作者: Daily-Value 時(shí)間: 2025-3-29 16:15
The ,: An R Package for Nonparametric Inference of Bivariate Interval-Censored Datainterval censoring that gives rise to bivariate interval-censored data. Nonparametric inference of bivariate interval-censored data focuses on estimation of the joint distribution function of event times or the joint survival function. The conventional nonparametric maximum likelihood estimator suff作者: 狗舍 時(shí)間: 2025-3-29 23:12
Joint Modeling for Longitudinal and Interval-Censored Survival Data: Application to IMPI Multi-Centewith the associated event times. These models are useful in two practical applications; firstly focusing on survival outcome whilst accounting for time-varying covariates measured with error and secondly focusing on the longitudinal outcome while controlling for informative censoring. The joint mode作者: Incorporate 時(shí)間: 2025-3-30 01:17 作者: 細(xì)胞膜 時(shí)間: 2025-3-30 05:53 作者: anagen 時(shí)間: 2025-3-30 10:59 作者: FIR 時(shí)間: 2025-3-30 15:42
Manufacturing Industry of Bangladesh,cur in many areas, including demographical studies, epidemiological studies, medical or public health research and social science. In contrast to the historic review of Chap. 1, this chapter will provide a brief review of some recent advances on several topics concerning the analysis of interval-cen作者: figment 時(shí)間: 2025-3-30 19:24
Numbers, Information and Complexitytcomes. In this chapter, our purpose is to review several methods to evaluate prediction models and to compare their performance in a context of interval censored data. This chapter provides conceptual and practical explanation of statistical methods for the time-dependent ROC and C-index as the dis作者: Postmenopause 時(shí)間: 2025-3-30 22:07 作者: 小蟲 時(shí)間: 2025-3-31 02:44
Structured Eigenvalue Perturbation Theorynal odds and accelerated failure time models. There are cases in practice that such conventional model assumptions may be inappropriate for modeling survival outcomes of interest. In this chapter, we introduce an alternative, the accelerated hazards model, for the analysis of interval-censored data 作者: 學(xué)術(shù)討論會(huì) 時(shí)間: 2025-3-31 07:50
