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Imputation‐based Strategies for Clinical Trial Longitudinal Data with Nonignorable Missing Values by Xiaowei Yang; Jinhui Li; Steven Shoptaw is a Mathematics article available to read on EtoBox.

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## Abstract Biomedical research is plagued with problems of missing data, especially in clinical trials of medical and behavioral therapies adopting longitudinal design. After a literature review on modeling incomplete longitudinal data based on full‐likelihood functions, this paper proposes a set of imputation‐based strategies for implementing selection, pattern‐mixture, and shared‐parameter models for handling intermittent missing values and dropouts that are potentially nonignorable according to various criteria. Within the framework of multiple partial imputation, intermittent missing values are first imputed several times; then, each partially imputed data set is analyzed to deal with dropouts with or without further imputation. Depending on the choice of imputation model or measurement model, there exist various strategies that can be jointly applied to the same set of data to study the effect of treatment or intervention from multi‐faceted perspectives. For illustration, the strategies were applied to a data set with continuous repeated measures from a smoking cessation clinical trial. Copyright © 2008 John Wiley & Sons, Ltd.

Who reads Imputation‐based Strategies for Clinical Trial Longitudinal Data with Nonignorable Missing Values?

Author

Xiaowei Yang; Jinhui Li; Steven Shoptaw

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2008

Language

EN

Field

Mathematics (Physical Sciences)