BACKGROUND: Longitudinal designs are indispensable to the study of change in outcomes over time and have an important role in health, social, and behavioral sciences. However, these designs present statistical challenges particularly related to accounting for the variance and covariance of the repeated measurements on the same participants and to modeling outcomes that are not normally distributed. OBJECTIVES: The purpose of this study was to introduce a general methodology for longitudinal designs to address these statistical challenges and to present an example of an analysis conducted with data collected in a randomized clinical trial. In this example, the outcome of interest-monthly health-related out-of-pocket expenses incurred by breast cancer survivors-had a skewed distribution. METHODS: Common statistical approaches are for longitudinal analysis using linear and generalized linear mixed models are reviewed, and the discussed methods are applied to analyze monthly health-related out-of-pocket expenses. DISCUSSION: Although standard statistical software is available to conduct longitudinal analyses, training is necessary to understand and to take advantage of the various options available for model fitting. However, knowledge of the basics of the methodology allows assimilation and incorporation into practice of evidence from the numerous studies that use these designs.
BACKGROUND: Longitudinal designs are indispensable to the study of change in outcomes over time and have an important role in health, social, and behavioral sciences. However, these designs present statistical challenges particularly related to accounting for the variance and covariance of the repeated measurements on the same participants and to modeling outcomes that are not normally distributed. OBJECTIVES: The purpose of this study was to introduce a general methodology for longitudinal designs to address these statistical challenges and to present an example of an analysis conducted with data collected in a randomized clinical trial. In this example, the outcome of interest-monthly health-related out-of-pocket expenses incurred by breast cancer survivors-had a skewed distribution. METHODS: Common statistical approaches are for longitudinal analysis using linear and generalized linear mixed models are reviewed, and the discussed methods are applied to analyze monthly health-related out-of-pocket expenses. DISCUSSION: Although standard statistical software is available to conduct longitudinal analyses, training is necessary to understand and to take advantage of the various options available for model fitting. However, knowledge of the basics of the methodology allows assimilation and incorporation into practice of evidence from the numerous studies that use these designs.
Authors: Karen Dow Meneses; Patrick McNees; Victoria W Loerzel; Xiaogang Su; Ying Zhang; Lauren A Hassey Journal: Oncol Nurs Forum Date: 2007-09 Impact factor: 2.172
Authors: James L Devin; Michelle M Hill; Marina Mourtzakis; Joe Quadrilatero; David G Jenkins; Tina L Skinner Journal: J Physiol Date: 2019-03-20 Impact factor: 5.182
Authors: Rita A Jablonski; Ann M Kolanowski; Andres Azuero; Vicki Winstead; Corteza Jones-Townsend; Maria L Geisinger Journal: Gerodontology Date: 2018-07-13 Impact factor: 2.980
Authors: Samuel J Dicken; John Joseph Mitchell; Jessica Newberry Le Vay; Emma Beard; Dimitra Kale; Aleksandra Herbec; Lion Shahab Journal: Front Nutr Date: 2022-01-13
Authors: Samuel T Orange; Alastair R Jordan; Adam Odell; Owen Kavanagh; Kirsty M Hicks; Tristan Eaglen; Stephen Todryk; John M Saxton Journal: Int J Cancer Date: 2022-03-05 Impact factor: 7.316