Bias in longitudinal data analysis with missing data using t…

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  • DOI 10.1111/bmsp.12043
  • PMID https://pubmed.ncbi.nlm.nih.gov/25098455
  • OpenAlex ID W1977356080
  • Type Article
  • Language EN
  • Published 07 Aug 2014
  • Volume 68 (2)
  • Pages 246–267
  • Source British Journal of Mathematical and Stat…
  • Publisher Wiley
  • FWCI 0.70
  • Countries 1
  • Institutions 1
  • Added 24 Jun 2016
  • Updated 22 Oct 2026
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Bias in longitudinal data analysis with missing data using typical linear mixed‐effects modelling and pattern‐mixture approach: An analytical illustration

Article 2014 EN

We analytically derive the fixed-effects estimates in unconditional linear growth curve models by typical linear mixed-effects modelling (TLME) and by a pattern-mixture (PM) approach with random-slope-dependent two-missing-pattern missing not at random (MNAR) longitudinal data. Results showed that when the missingness mechanism is random-slope-dependent MNAR, TLME estimates of both the mean interc…

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3

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0.70

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3 authors
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1 Manshu Yang —
2 Lijuan Wang University of Notre Dame (US)
3 Scott E. Maxwell Corresponding University of Notre Dame (US)
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