Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An Optimization Randomized Clinical Trial to Identify an Effective, Efficient Smoking Cessation Intervention in the Context of Lung Cancer Screening: Cessation and Screening to Save Lives (CASTL).

Chest·2026
Same author

End "reusable" waste dumping in Africa.

Science (New York, N.Y.)·2026
Same author

Synthesis and Preclinical Evaluation of Structurally Optimized <sup>68</sup>Ga-Labeled CXCR4 Radiotracers for PET Imaging of Atherosclerotic Plaque Inflammation.

Journal of medicinal chemistry·2026
Same author

Feasibility study on developing alkali-activated cementitious materials via calcination-induced structural reconstruction of carbide slag and illite.

Environmental research·2026
Same author

Interactive effects of forest succession and seasonal hydrology on heavy metal accumulation and ecological risk in subtropical forest soils.

Environmental geochemistry and health·2026
Same author

Integrating mind-body exercises for stress in schizophrenia: A systematic review of randomized controlled trials.

Archives of psychiatric nursing·2026

Related Experiment Video

Updated: May 29, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Data mining for response shift patterns in multiple sclerosis patients using recursive partitioning tree analysis.

Yuelin Li1, Carolyn E Schwartz

  • 1Behavioral Science, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|September 13, 2011
PubMed
Summary
This summary is machine-generated.

Response shift significantly impacts quality of life (QOL) scores in multiple sclerosis (MS) patients. These shifts, affecting physical and mental component scores, vary across disease trajectories, complicating true score comparisons.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: May 29, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Neurology
  • Psychometrics
  • Health Outcomes Research

Background:

  • Quality of life (QOL) assessment in multiple sclerosis (MS) is crucial for monitoring disease progression and treatment efficacy.
  • Response shift, a change in the meaning of one's self-evaluation, can alter QOL scores over time.
  • Understanding response shift is vital for accurate interpretation of QOL data in chronic conditions like MS.

Purpose of the Study:

  • To investigate the presence and characteristics of QOL response shift in patients with multiple sclerosis.
  • To utilize recursive partitioning tree analysis (RPART) to identify patterns of response shift across different MS disease trajectories.

Main Methods:

  • Employed RPART to analyze longitudinal QOL data (SF-12v2 Physical Component Scores [PCS] and Mental Component Scores [MCS]) from MS patients in the NARCOMS registry.
  • Categorized patients into three disease trajectory groups: relapsing, stable, and progressive.
  • Interpreted RPART trees by identifying terminal nodes with significant, unexpected changes in PCS and MCS scores, indicating response shift.

Main Results:

  • Quantitatively, 20% of MS patients exhibited response shift, with higher prevalence in the progressive (10%) and relapsing (8%) cohorts compared to the stable (2%) cohort.
  • Qualitative analysis of RPART trees revealed differences in response shift indicators (recalibration, reconceptualization, reprioritization) across disease groups.
  • Disability subscales were more influential than symptom management in distinguishing homogenous patient groups regarding response shift.

Conclusions:

  • Response shift significantly obscures the interpretation of changes in physical and mental component scores for MS patients.
  • The true scores for PCS changes are not directly comparable across different MS disease trajectory groups due to response shift.
  • Future research should account for response shift to enable more accurate QOL assessments in MS.