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Joint modeling of longitudinal and survival outcomes using generalized estimating equationsZheng, Mengjie 07 May 2018 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / Joint models for longitudinal and time-to-event data has been introduced to study the
association between repeatedly measured exposures and the risk of an event. The use
of joint models allows a survival outcome to depend on some characteristic functions
from the longitudinal measures. Current estimation methods include a two-stage
approach, Bayesian and maximum likelihood estimation (MLEs) methods. The twostage
method is computationally straightforward but often yields biased estimates.
Bayesian and MLE methods rely on the joint likelihood of longitudinal and survival
outcomes and can be computationally intensive.
In this work, we propose a joint generalized estimating equation framework
using an inverse intensity weighting approach for parameter estimation from joint
models. The proposed method can be used to longitudinal outcomes from the exponential
family of distributions and is computationally e cient. The performance of
the proposed method is evaluated in simulation studies. The proposed method is used
in an aging cohort to determine the relationship between longitudinal biomarkers and
the risk of coronary artery disease.
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Bayesian predictive model averaging approach to joint longitudinal-survival modeling: Application to an immuno-oncology clinical trial / ベイズ予測モデル平均化法を用いた経時測定データと生存時間データの同時解析: 癌免疫臨床試験データへの適用Yao, Zixuan 25 March 2024 (has links)
京都大学 / 新制・課程博士 / 博士(医科学) / 甲第25204号 / 医科博第160号 / 京都大学大学院医学研究科医科学専攻 / (主査)教授 佐藤 俊哉, 教授 古川 壽亮, 教授 武藤 学 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM
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