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Estimation and group comparison of survival curves are two very common issues in survival analysis. In practice, the Kaplan-Meier estimates of survival functions may be biased due to unbalanced distribution of confounders. Here we develop an adjusted Kaplan-Meier estimator (AKME) to reduce confounding effects using inverse probability of treatment weighting(More)
Hierarchical models are widely used in medical research to structure complicated models and produce statistical inferences. In a hierarchical model, observations are sampled conditional on some parameters and these parameters are sampled from a common prior distribution. Bayes and empirical Bayes (EB) methods have been effectively applied in analyzing these(More)
Hierarchical models have a variety of applications, including multi-center clinical trials, local estimation of disease rates, longitudinal studies, risk assessment, and meta-analysis. In a hierarchical model, observations are sampled conditional on individual unit-specific parameters and these parameters are sampled from a mixing distribution. In(More)
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