Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data

BACKGROUND: In the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such ‘aggregate-level’ analysis is biased when data are missing at random (MAR) and p...

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Detalles Bibliográficos
Autores principales: Bazo-Alvarez, Juan Carlos, Morris, Tim P, Pham, Tra My, Carpenter, James R, Petersen, Irene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549500/
https://www.ncbi.nlm.nih.gov/pubmed/33116899
http://dx.doi.org/10.2147/CLEP.S266428

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549500/
https://www.ncbi.nlm.nih.gov/pubmed/33116899
http://dx.doi.org/10.2147/CLEP.S266428

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