Survival analysis with incomplete genetic data

Marginal Analysis of A Population-Based Genetic Association Study of Quantitative Traits with Incomplete Longitudinal Data

A common study to investigate gene-environment interaction is designed to be longitudinal and population-based. Data arising from longitudinal association studies often contain missing responses. Naive analysis without taking missingness into account may produce invalid inference, especially when the missing data mechanism depends on the response process. To address this issue in the ana...

Survival Analysis of Gastric Cancer Patients with Incomplete Data

PURPOSE Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. MATERIALS AND METHODS Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carl...

Bayesian Analysis of Survival Data with Spatial Correlation

Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study‎. ‎One of the most important issues in the analysis of survival data with spatial dependence‎, ‎is estimation of the parameters and prediction of the unknown values in known sites based on observations vector‎. ‎In this paper to analyze this type of survival‎, ‎Cox...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data