Remarks on the design and analyses of longitudinal studies for cancer patients with anorexia and weight loss

Jennifer Le-Rademacher, Elizabeth M. Storrick, Aminah Jatoi

Research output: Contribution to journalReview article

Abstract

Longitudinal data serve an important role in understanding the cancer anorexia weight loss syndrome and in testing interventions to palliative and treat patients who develop this syndrome. The element of time and the interrelatedness of data points define longitudinal data and add to the richness of this type of data. However, longitudinal data can also give rise to non-random, missing data that can lead to flawed conclusions. This paper discusses these issues and suggests practical considerations for design and analysis of longitudinal cancer anorexia weight loss studies.

Original languageEnglish (US)
JournalJournal of Cachexia, Sarcopenia and Muscle
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Anorexia
Longitudinal Studies
Weight Loss
Neoplasms

Keywords

  • Cancer anorexia studies
  • Design considerations
  • Longitudinal data analysis
  • Missing data
  • Weight loss studies

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physiology (medical)

Cite this

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abstract = "Longitudinal data serve an important role in understanding the cancer anorexia weight loss syndrome and in testing interventions to palliative and treat patients who develop this syndrome. The element of time and the interrelatedness of data points define longitudinal data and add to the richness of this type of data. However, longitudinal data can also give rise to non-random, missing data that can lead to flawed conclusions. This paper discusses these issues and suggests practical considerations for design and analysis of longitudinal cancer anorexia weight loss studies.",
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AB - Longitudinal data serve an important role in understanding the cancer anorexia weight loss syndrome and in testing interventions to palliative and treat patients who develop this syndrome. The element of time and the interrelatedness of data points define longitudinal data and add to the richness of this type of data. However, longitudinal data can also give rise to non-random, missing data that can lead to flawed conclusions. This paper discusses these issues and suggests practical considerations for design and analysis of longitudinal cancer anorexia weight loss studies.

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