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

Aalen, Odd O ; Gunnes, Nina

Biostatistics (Oxford, England), 2010-07, Vol.11 (3), p.453-472 [Periódico revisado por pares]

England: Oxford Publishing Limited (England)

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Título:
A dynamic approach for reconstructing missing longitudinal data using the linear increments model
Autor: Aalen, Odd O ; Gunnes, Nina
Assuntos: Biostatistics ; Carcinoma, Non-Small-Cell Lung - drug therapy ; Carcinoma, Non-Small-Cell Lung - radiotherapy ; Clinical Trials, Phase III as Topic ; Computer Simulation ; Data analysis ; Estimating techniques ; Humans ; Linear Models ; Longitudinal Studies ; Lung Neoplasms - drug therapy ; Lung Neoplasms - radiotherapy ; Markov analysis ; Markov Chains ; Patient Dropouts ; Quality of Life ; Regression analysis ; Simulation
É parte de: Biostatistics (Oxford, England), 2010-07, Vol.11 (3), p.453-472
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: 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 censored data, [PhD Thesis]. Lancaster University) and Diggle and others (2007. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal. Journal of the Royal Statistical Society, Series C (Applied Statistics, 56, 499-550). We wish to reconstruct responses for missing data and discuss the required assumptions needed for both monotone and nonmonotone missingness. The computational procedures suggested are very simple and easily applicable. They can also be used to estimate causal effects in the presence of time-dependent confounding. There are also connections to methods from survival analysis: The Aalen-Johansen estimator for the transition matrix of a Markov chain turns out to be a special case. Analysis of quality of life data from a cancer clinical trial is analyzed and presented. Some simulations are given in the supplementary material available at Biostatistics online.
Editor: England: Oxford Publishing Limited (England)
Idioma: Inglês

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

Aalen, Odd O ; Gunnes, Nina

England: Oxford Publishing Limited (England)

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