Cheng K. Lee, Jenq-Daw Lee
A setting of a trivairate survival function using semi-competing risks concept is proposed. The Stanford Heart Transplant data is reanalyzed using a trivariate Weibull distribution model with the proposed survival function.
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Benchmarking Classical, Machine Learning, and Bayesian Survival Models for Clinical Prediction
本研究使用乳腺癌数据集基准测试了经典、机器学习与贝叶斯生存模型，发现正确实现的贝叶斯可解释模型在预测性能上可与现代黑盒模型相媲美，揭示了复杂性与可解释性间的权衡。
WTNN: Weibull-Tailored Neural Networks for survival analysis
WTNN是一种专为Weibull生存研究设计的神经网络框架，通过整合定性先验知识，提升生存预测的稳健性和可解释性。
To examine the effect of exponential, Weibull and Gompertz distributions on sample size determination for superiority trials (STs) or non-inferiority trials (NTs) with time-to-event data, we present two sample size formulas for STs or NTs based on Weibull and Gompertz distributions, respectively. The formulas are compared with the current exponential formula to examine their performance. The simulation results show that the sample size formula based on the Weibull distribution is the most robust among the three formulas in STs or NTs. We suggest that recognizing the appropriate distribution in…
This paper describes the use of survival analysis and simulation to model the lifetime of high voltage instrument transformers in the Dutch transmission sys-tem. To represent asset aging, the non-parametric Kaplan-Meier method is used to enable the fitting of Weibull distribution. Such an approach is implemented on three different voltage levels, namely 110kV, 150kV, and 220/380kV. Real failure and inspection data is used to achieve a realistic failure model of the instrument trans-formers. Failure and maintenance data occurring between 1989 and 2021 have been used for this study. In spite of …
A trivariate Weibull survival model using competing risks concept is applied on studying recidivism of committing 3 types of crimes - sex, violent and others. The assumption of independence of time to commit each type of crimes is relaxed so that the association of the time to recidivism between any two types of crimes can be evaluated. We found that the correlation of time to recidivism between sex crimes and violent crimes are more correlated than other pairs. Probability of experiencing a charged arrest of other crimes is greater than a charged arrest of violent crimes followed by a charged…
In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural network architecture and the associated loss functions that takes into account the right-censored data. We extend the approach to a finite mixture of two-parameter Weibull distributions. We first validate that our model is able to precisely estimate the right parameters of the conditional Weibull dis…
We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we have extolled the virtues of the "power generalized Weibull" (PGW) distribution as an attractive vehicle for univariate parametric survival analysis: it is a tractable, parsimonious, model which interpretably allows for a wide variety of hazard shapes and, when adapted (to give an adapted PGW, or APGW, distribution), covers a wide variety of important special/limiting cases. Here, we additionally observe a frailty relationship between a PGW distribution with one value of the parameter which control…
Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates
本研究提出贝叶斯非参数Weibull代理竞争（WDR）方法用于含竞争事件的生存分析，可处理左截断、时变协变量等复杂情况，通过合成与真实数据实验验证其优异性能与应用价值。
For many diseases it is reasonable to assume that the hazard rate is not constant across time, but also that it changes in different time intervals. To capture this, we work here with a piecewise survival model. One of the major problems in such piecewise models is to determine the time points of change of the hazard rate. From the practical point of view this can provide very important information as it may reflect changes in the progress of a disease. We present piecewise Weibull regression models with covariates. The time points where change occurs are assumed unknown and need to be estimat…
We introduce a general class of continuous univariate distributions with positive support obtained by transforming the class of two-piece distributions. We show that this class of distributions is very flexible, easy to implement, and contains members that can capture different tail behaviours and shapes, producing also a variety of hazard functions. The proposed distributions represent a flexible alternative to the classical choices such as the log-normal, Gamma, and Weibull distributions. We investigate empirically the inferential properties of the proposed models through an extensive simula…
The use of flat or weakly informative priors is popular due to the objective a priori belief in the absence of strong prior information. In the case of the Weibull model the improper uniform, equal parameter gamma and joint Jeffrey's priors for the shape parameter are popular choices. The effects and behaviors of these priors have yet to be established from a modeling viewpoint, especially their ability to reduce to the simpler exponential model. In this work we propose a new principled prior for the shape parameter of the Weibull model, originating from a prior on the distance function, and a…
Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model:Performance under Penalized Likelihood
本文提出兼容Weibull比例危险混合治愈模型的多重插补方法，通过模拟研究比较了不同插补模型在低事件率下的估计偏差和覆盖率。
The Weibull distribution, with shape parameter $k>0$ and scale parameter $λ>0$, is one of the most popular parametric distributions in survival analysis with complete or censored data. Although inference of the parameters of the Weibull distribution is commonly done through maximum likelihood, it is well established that the maximum likelihood estimate of the shape parameter is inadequate due to the associated large bias when the sample size is small or the proportion of censored data is large. This manuscript demonstrates how the Bayesian information-theoretic minimum message length principle…
We develop flexible multi-parameter regression survival models for interval censored survival data arising in longitudinal prospective studies and longitudinal randomised controlled clinical trials. A multi-parameter Weibull regression survival model, which is wholly parametric, and has non-proportional hazards, is the main focus of the paper. We describe the basic model, develop the interval-censored likelihood and extend the model to include gamma frailty and a dispersion model. We evaluate the models by means of a simulation study and a detailed re-analysis of data from the Signal Tandmobie…
Massive Open Online Course (MOOC) platforms incorporate large course catalogs from which individual students may register multiple courses. We performed a network-based analysis of student achievement, considering how course-course interactions may positively or negatively affect student success. Our dataset included 378,000 users and 1,000,000 unique registration events in France Universite Numerique (FUN), a national MOOC platform. We adapt reliability theory to model certificate completion rates with a Weibull survival function, following the intuition that students "survive" in a course fo…
Inference on Survival Reliability with Type-I Censored Weibull data
本文提出了一种新的精确推断方法，用于Weibull、对数正态和对数洛吉斯等分布的参数检验和置信区间计算，解决了截断数据和小样本下的可靠性推断问题。
merlin can do a lot of things. From simple stuff, like fitting a linear regression or a Weibull survival model, to a three-level logistic mixed effects model, or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with non-linear effects...the list is rather endless. merlin can do things I haven't even thought of yet. I'll take a single dataset, and attempt to show you the full range of capabilities of merlin, and discuss some future directions for the implementation in Stata.
A Real Data-Driven, Robust Survival Analysis on Patients who Underwent Deep Brain Stimulation for Parkinson's Disease by Utilizing Parametric, Non-Parametric, and Semi-Parametric Approaches
本研究利用参数化、非参数化和半参数化方法，分析帕金森病患者接受深部脑刺激后的生存情况，发现女性患者生存率较高，生存时间分布特征不同。
Smooth Transformation Models for Survival Analysis: A Tutorial Using R
本文介绍平滑变换模型框架在R中统一处理多种生存模型，包括非比例风险、相依删失等复杂场景，并利用直肠癌试验数据演示实现。
Survival data is encountered in a range of disciplines, most notably health and medical research. Although Bayesian approaches to the analysis of survival data can provide a number of benefits, they are less widely used than classical (e.g. likelihood-based) approaches. This may be in part due to a relative absence of user-friendly implementations of Bayesian survival models. In this article we describe how the rstanarm R package can be used to fit a wide range of Bayesian survival models. The rstanarm package facilitates Bayesian regression modelling by providing a user-friendly interface (us…
Empirical Simulation of Survival and Mixed-Type Data for Clinical Trial Design
本研究提出基于经验Copula的R包EmpiricalSim，用于模拟含多类型变量的多元生存数据，可匹配目标生存分布并保留变量间相关性，在肿瘤临床试验数据重建中表现良好。
Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to users, they constantly face decisions on what to send, when and how. A lack of research and methodology commonly leads to heuristic decision making. Many notifications arrive at an inappropriate moment or introduce too many interruptions, failing to provide value to users and spurring users' complaints. In this paper we explore unique features of interactions between mobile notifications and user engagement. We propo…
Estimating the duration of user behavior is a central concern for most internet companies. Survival analysis is a promising method for analyzing the expected duration of events and usually assumes the same survival function for all subjects and the event will occur in the long run. However, such assumptions are inappropriate when the users behave differently or some events never occur for some users, i.e., the conversion period on web services of the light users with no intention of behaving actively on the service. Especially, if the proportion of inactive users is high, this assumption can l…
Interval-Censored Survival Analysis of Grapevine Phenology: Thermal Controls on Flowering and Fruit Ripening
本研究开发了基于区间删失生存模型的可重现工作流程，结合 USA-NPN 物候观测与 NASA POWER 天气数据，利用 AFT 模型分析葡萄藤开花和成熟，发现季前温暖条件显著促进早熟，为从公民科学物候数据中提取气候信号提供了有效方法。
SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
本文提出SDPM，一种用于连续时间生存分析的生成模型，通过去噪扩散模型建模生存结果的条件分布，避免了对事件时间分布的参数假设，并在变换的目标空间中使用标准化对数时间和连续高斯混合表示来表示删失指示符，从而在多个真实生存数据集上取得了竞争力的预测性能。
We introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard function (constant, increasing, decreasing, up-then-down, down-then-up), various common survival distributions (log-logistic, Burr type XII, Weibull, Gompertz), and includes defective distributions (i.e., cure models). This generality is achieved using four basic distributional parameters: two scale-type parameters and two shape parameters. Generalising to covariate dependence, the scale-type regression components correspond to accelerated failure time (AFT) and proportional hazards…
The Cox regression, a semi-parametric method of survival analysis, is extremely popular in biomedical applications. The proportional hazards assumption is a key requirement in the Cox model. To accommodate non-proportional hazards, we propose to parameterise the shape parameter of the baseline hazard function using the additional, separate Cox-regression term which depends on the vector of the covariates. We call this model the double-Cox model. The R programs for fitting the double-Cox model are available on Github. We formally introduce the double-Cox model with shared frailty and investigat…
In this paper, we make an experimental comparison of semi-parametric (Cox proportional hazards model, Aalen's additive regression model), parametric (Weibull AFT model), and machine learning models (Random Survival Forest, Gradient Boosting with Cox Proportional Hazards Loss, DeepSurv) through the concordance index on two different datasets (PBC and GBCSG2). We present two comparisons: one with the default hyper-parameters of these models and one with the best hyper-parameters found by randomized search.
Normalised Local Hazard Plots
本文提出了一种用于生存数据分析模型验证的标准化局部危险图，通过比较非参数和参数危险率估计值构建危险比较函数，并讨论了检测力和时间离散模型的扩展。
A Weibull Mixture Cure Frailty Model for High-dimensional Covariates
本文提出一种融合脆弱成分与高维协变量的 Weibull 混合治愈模型，结合自适应弹性网惩罚和 EM 算法进行变量选择与推断，并在模拟和 TCGA 乳腺癌 RNAseq 数据中验证其预后性能。
Boosting Distributional Copula Regression for Bivariate Right-Censored Time-to-Event Data
提出一种用于右删失双变量事件时间数据的分布copula回归模型，通过梯度boosting实现自动变量选择，适用于高维数据。
Individualized Treatment Effects in Advanced Prostate Cancer: A Causal-Survival Modeling Approach to Risk-Guided Therapy
本文基于475例晚期前列腺癌随机试验数据，采用带交互项的Weibull AFT模型估计个体化治疗效应，揭示高剂量DES的疗效随年龄和肿瘤大小显著变化，并验证了估计的可靠性。
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