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 Experiment Videos

Exploring individual change

M S Krause1, K I Howard, W Lutz

  • 1Department of Psychology, Northwestern University, Evanston, Illinois 60208-2710, USA.

Journal of Consulting and Clinical Psychology
|November 6, 1998
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Survey data on voluntary nature conservation commitments of German businesses and their perceptions towards conservation credits.

Data in brief·2020
Same author

Selective deletion of Pten in theca-interstitial cells leads to androgen excess and ovarian dysfunction in mice.

Molecular and cellular endocrinology·2017
Same author

CODIMENSIONALITY WITHOUT HIGH CORRELATION.

Multivariate behavioral research·2016
Same author

Multivariate Analysis Of Gang Delinquency: I. Ecologic Influences.

Multivariate behavioral research·2016
Same author

Ordinal Scale Construction For Convergent Validity, Object Discrimination, And Resolving Power.

Multivariate behavioral research·2016
Same author

Multivariate Analysis Of Gang Delinquency: Iv. Personality Factors In Gangs And Clubs.

Multivariate behavioral research·2016
Same journal

(Computation of) the standardized mean difference in clinical trials should be standardized.

Journal of consulting and clinical psychology·2026
Same journal

Efficacy of a modified digital unified protocol targeting both positive and negative emotion regulation: A randomized clinical trial.

Journal of consulting and clinical psychology·2026
Same journal

Perceived changes in drinking are a mechanism of spillover effects from a brief alcohol intervention conducted in a college social network.

Journal of consulting and clinical psychology·2026
Same journal

Online versus telehealth delivery of a parenting program for postdeployed military families: Child internalizing and externalizing symptoms as moderators of response.

Journal of consulting and clinical psychology·2026
Same journal

Testing self-help interventions for adults with clinical perfectionism: A randomized controlled trial comparing online skills modules to a book.

Journal of consulting and clinical psychology·2026
Same journal

Testing child STEPS in school-based mental health care: Cluster-randomized controlled effectiveness trial in five school districts.

Journal of consulting and clinical psychology·2026
See all related articles

Evaluating clinical interventions requires focusing on individual patient change scores, not just post-treatment scores. Analyzing response curves over time offers a more accurate and clinically relevant assessment of treatment impact.

Area of Science:

  • Clinical research methodology
  • Biostatistics
  • Health outcomes research

Background:

  • Traditional clinical intervention analysis relies on post-treatment scores, assuming pretreatment scores are controlled.
  • This approach may not accurately reflect treatment effectiveness or patient response.
  • Existing methods often overlook individual patient variability in treatment outcomes.

Purpose of the Study:

  • To challenge the conventional use of post-treatment scores for evaluating clinical interventions.
  • To advocate for the use of individual change scores and response curves in treatment research.
  • To emphasize the importance of disaggregated data for clinical relevance.

Main Methods:

  • Comparative analysis of post-treatment scores versus change scores.

Related Experiment Videos

  • Exploration of data-analytic methods for modeling individual patient change over time.
  • Discussion of statistical techniques for analyzing response curves.

    • Main Results:

    • Post-treatment scores are not consistently more reliable or equivalent to change scores, even when controlling for baseline data.
    • Advanced data-analytic methods can reveal individual patient response trajectories over time.
    • Focusing solely on group averages can obscure meaningful individual treatment effects.

    Conclusions:

    • Clinical intervention research should prioritize individual change scores and response curves for accurate outcome assessment.
    • Reporting results at the individual level enhances clinical practice relevance.
    • A shift towards disaggregated data analysis is crucial for understanding treatment impact.