Modeling Incomplete Longitudinal Data

Modeling Incomplete Longitudinal 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...

Modeling Correlation in Incomplete Longitudinal Data: The Case of Fruit Fly Mortality Data

Longitudinal studies are prevalent in clinical trials, biological and social sciences where subjects are measured repeatedly over time. Modeling the correlations of repeated measurements on the same subject and handling missing data are challenging problems in the statistical analysis of such data. The situation is exacerbated knowing that the presence of missing data can hamper modeling of dep...

Analyzing incomplete longitudinal clinical trial data.

Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based ...

Image Reconstruction from Incomplete Data