A dynamic approach for reconstructing missing longitudinal data using the linear increments model

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A dynamic approach for reconstructing missing longitudinal data using the linear increments model

Missing observations are commonplace in longitudinal data. We discuss how to model and analyze such data in a dynamic framework, that is, taking into consideration the time structure of the process and the influence of the past on the present and future responses. An autoregressive model is used as a special case of the linear increments model defined by Farewell (2006. Linear models for censor...

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ژورنال

عنوان ژورنال: Biostatistics

سال: 2010

ISSN: 1468-4357,1465-4644

DOI: 10.1093/biostatistics/kxq014