Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data

Shu Xu and Shelley A. Blozis
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Shu Xu: Pennsylvania State University, State College
Shelley A. Blozis: University of California, Davis

Journal of Educational and Behavioral Statistics, 2011, vol. 36, issue 2, 237-256

Abstract: Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR), may be handled. While the mechanism giving rise to the missing data cannot be determined by the observations, the sensitivity of parameter estimates to missing data assumptions can be studied, for example, by fitting multiple models that make different assumptions about the missing data process. Sensitivity analysis of a mixed model that may include nonlinear parameters when some data are missing is discussed. An example is provided.

Keywords: data analysis; hierarchical modeling; longitudinal studies (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:36:y:2011:i:2:p:237-256

DOI: 10.3102/1076998610375836

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