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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Dove
2020
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| Materias: | |
| 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 |