Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 84 for “"time-to-event"”.
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Joint Modeling of Tumor Size and Time-to-Event
<p>In clinical trials, time-to-event data (survival component) and longitudinal data (longitudinal component) are often collected. In order to model these two components simultaneously, the joint modeling approach which can reduce potential biases and improve the efficiency in estimating treatment …
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Probabilistic Time-to-Event Modeling Approaches for Risk Profiling
… providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. Models for predicting the time of a future event are …
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Modelling multivariate longitudinal outcomes and time-to-event data
It is common in clinical or observational studies to record information repeatedly over time while observing a time-to-event outcome among subjects. Joint models for longitudinal and survival data simultaneously analyse repetitively measured outcomes and associated event times. They offer valuable …
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Evaluating Alpha Spending Functions Applied to Observational Time-to-Event Analysis
… setting of randomized controlled trials (RCTs) to observational studies with time to event analyses. In these less structured environments, key design parameters such as the total number of events are often unknown, posing challenges for the standard implementation of sequential analysis …
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Methods for two-sample comparisons from censored time-to-event data
… survival data, it is frequently of interest to determine the efficacy of a treatment or new method over a control or existing method. For this purpose, one may report estimates of the two survival functions or, more specifically, their difference, accompanied by simultaneous confidence bands …
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Design and Monitoring of Clinical Trials with Clustered Time-to-Event Endpoint
… from subunits within each cluster tend to be positively correlated due to shared characteristics. Therefore, analysis of such data needs to account for the dependency between subunits. For clustered time-to-event endpoints, there are only few methods proposed for sample size calculation, …
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A Predictive Time-to-Event Modeling Approach with Longitudinal Measurements and Missing Data
… in the survival analysis is predicting the time to a future event such as the death or failure of a subject. It is of great importance for the medical decision making to investigate how the predictor variables including repeated measurements of the same subjects are affecting the future …
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Federated Survival Analysis: Ensemble and Neural Methods for Distributed Time-to-Event Data
L'abstract è presente nell'allegato / the abstract is in the attachment
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Bayesian models for unmeasured confounder in the analysis of time-to-event data.
… studies that omit confounders are subject to bias. In this dissertation we consider the specific case of time-to-event data. We also provide both the Bayesian parametric and the semi-parametric "twin regression" approaches with distributional assumptions of an unmeasured confounding …
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Advances in Time-to-Event Analysis: Big Data Applications in Cancer Risk Prediction
… of health care provides new opportunities to study dis- ease and gain unprecedented insights into the underlying biology. With the wealth of data generated, new statistical challenges arise. This thesis will address some of them, with a particular focus on Time-to-Event analysis. The Cox …
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Time-to-Event Prediction Using Deep Learning Models: Application to GPU Failure Data
… gain significant popularity in recent years due to their ability to identify complex patterns. In the field of reliability research, efforts are made to develop neural network models for predictive reliability. However, research focused on utilizing neural networks to forecast graphics processing …
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Statistical inference for residual time quantiles in regression models for censored time-to-event data
In this dissertation, we set out to develop new methods for the analysis of time-to-event data. In particular, we are concerned with residual time, or the time remaining to an event after a certain amount of time has passed since time zero. We develop methods to estimate quantiles of residual time …
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Time-to-Event Modeling with Bayesian Perspectives and Applications in Reliability of Artificial Intelligence Systems
… (AI) technology, the reliability of AI needs to be investigated for confidently using AI products in our daily lives. This dissertation includes three projects introducing the statistical models and model estimation methods that can be used in the reliability analysis of AI systems. The first …
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Size and Power of Tests of Hypotheses on Parameters When Modeling Time-to-Event Data with the Lindley Distribution
… on subjects are collected in addition to times-to-event in time-to-event studies. Such data are often analyzed by choosing a model that allows the covariate information to be utilized in the analyses. The analysis proceeds by estimating parameters in the model and testing hypotheses …
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Joint models for longitudinal and survival data
… These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus …
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Bayesian approaches for survival data in pharmaceutical research.
… research, we consider Bayesian methodologies to address problems in biopharmaceutical research, most of which are motivated by real-world problems in network meta-analysis, prior elicitation, and adaptive designs. Network meta-analysis is a hierarchical model used to combine the results of …
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Approaches for Handling Time-Varying Covariates in Survival Models
Survival models are used in analysing time-to-event data. This type of data is very common in medical research. The Cox proportional hazard model is commonly used in analysing time-to-event data. However, this model is based on the proportional hazard (PH) assumption. Violation of this assumption …
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"Sequential analysis of duration data with application to ""reemployment bonus"" experiments"
"Suitable methodology and an asymptotic theory for the sequential analysis of time-to-event (duration) data is developed and its application in ""Reemployment Bonus"" experiments is studied."
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