Statistical Methods on Challenging Issues of Longitudinal and Lifetime Data

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As statistical research advances, additional challenges are emerging from applications, bringing new issues that make existing methods break down. Standard statistical methods have been developed under the assumption that the collected data are "perfect". This assumption is, however, often violated in practice. In particular, measurement error and missing observations occur commonly. Measurement error has been a long standing concern in data analysis, and it should be avoided whenever possible. Missingness is ubiquitously present in applications, and it creates tremendous challenges to standard inference methods. There have been considerable advances in the past few decades on methodology for dealing with measurement error and missing data, where much of the work has focused on issues arising from longitudinal and lifetime studies. The growing literature and emerging lexicon have helped raise awareness of the impact that measurement error or missing data can have on inferences regarding treatment and other covariate effects.