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Modeling Change in the Presence of Non-Randomly Missing Data: Evaluating A Shared Parameter Mixture Model.

ABSTRACT: In longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed random coefficient-dependent missingness, is statistically non-ignorable and can bias parameter estimates obtained from conventional growth models that assume missing data are missing at random. This paper describes a shared-parameter mixture model (SPMM) for testing the sensitivity of growth model parameter estimates to a random coefficient-dependent missingness mechanism. Simulations show that the SPMM recovers trajectory estimates as well as or better than a standard growth model across a range of missing data conditions. The paper concludes with practical advice for longitudinal data analysts.

SUBMITTER: Gottfredson NC

PROVIDER: S-EPMC4084916 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Modeling Change in the Presence of Non-Randomly Missing Data: Evaluating A Shared Parameter Mixture Model.

Gottfredson Nisha C NC Bauer Daniel J DJ Baldwin Scott A SA

Structural equation modeling : a multidisciplinary journal 20140101 2

In longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed <i>random coefficient-dependent missingness</i>, is statistically non-ignorable and can bias parameter estimates obtained from conventional growth models that assume missing data are missing at random. This paper describes a shared-paramete ...[more]

PMID: 25013354

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