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

Introduction to z Scores01:06

Introduction to z Scores

11.2K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
11.2K
Introduction to z Scores01:05

Introduction to z Scores

1.3K
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
1.3K
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

19.6K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
19.6K
z Scores and Unusual Values01:07

z Scores and Unusual Values

11.0K
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
11.0K
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

480
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
480
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
16.6K

You might also read

Related Articles

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

Sort by
Same author

Development and validation of a multimodal artificial intelligence-based model for predicting post-prostatectomy treatment outcomes from baseline biparametric prostate magnetic resonance imaging.

Diagnostic and interventional radiology (Ankara, Turkey)·2026
Same author

Assessing the Importance of Variation in Diagnostic Coding Among the Three Countries in the UK Biobank.

Learning health systems·2026
Same author

Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.

Nature communications·2026
Same author

Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.

medRxiv : the preprint server for health sciences·2026
Same author

Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.

Journal of the American Chemical Society·2026
Same author

Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals.

Nature machine intelligence·2026

Related Experiment Video

Updated: Jan 30, 2026

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
06:49

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS

Published on: October 6, 2015

20.9K

The correlation between baseline score and post-intervention score, and its implications for statistical analysis.

Lei Clifton1, David A Clifton2

  • 1Centre for Statistics in Medicine (CSM), NDORMS, University of Oxford, Oxford, UK. lei.clifton@csm.ox.ac.uk.

Trials
|January 13, 2019
PubMed
Summary

Always adjust for baseline scores in randomized controlled trials (RCTs) using analysis of covariance (ANCOVA) to avoid biased treatment effect estimates. This statistical approach accounts for baseline imbalances and regression to the mean in continuous outcome measures.

Keywords:
Analysis of covariance (ANCOVA)BaselineBland-Altman plotChange scoreCorrelationIndependentMeansOutcomePost-interventionRandomised controlled trial (RCT)Regression to the mean (RTM)Sample sizeStandard deviation (SD)Standard error (SE)Statistical analysisTreatment

More Related Videos

Z-Scores for Assessing Ovarian Reserve in Young Patients Undergoing Fertility Preservation
05:42

Z-Scores for Assessing Ovarian Reserve in Young Patients Undergoing Fertility Preservation

Published on: October 25, 2024

1.6K
The Ladder Rung Walking Task: A Scoring System and its Practical Application.
09:38

The Ladder Rung Walking Task: A Scoring System and its Practical Application.

Published on: June 12, 2009

26.7K

Related Experiment Videos

Last Updated: Jan 30, 2026

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
06:49

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS

Published on: October 6, 2015

20.9K
Z-Scores for Assessing Ovarian Reserve in Young Patients Undergoing Fertility Preservation
05:42

Z-Scores for Assessing Ovarian Reserve in Young Patients Undergoing Fertility Preservation

Published on: October 25, 2024

1.6K
The Ladder Rung Walking Task: A Scoring System and its Practical Application.
09:38

The Ladder Rung Walking Task: A Scoring System and its Practical Application.

Published on: June 12, 2009

26.7K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Analysis

Background:

  • Continuous outcome measures in randomized controlled trials (RCTs) require both baseline and post-intervention scores.
  • Appropriate statistical analysis is crucial for interpreting these continuous outcomes.

Purpose of the Study:

  • To derive and analyze the correlation between change scores and baseline/post-intervention scores.
  • To demonstrate the necessity of adjusting for baseline scores in statistical analyses of continuous outcomes.
  • To highlight the limitations of using change scores alone.

Main Methods:

  • Mathematical derivation of correlations between change scores and baseline, post-intervention, and average scores.
  • Application of these derivations to parallel, two-arm RCTs and other studies with continuous outcomes.
  • Discussion of correlations in the context of Bland-Altman plots for comparing measurement methods.

Main Results:

  • A consistent correlation (typically negative) exists between change scores and baseline scores.
  • Using change scores does not inherently address regression to the mean or baseline imbalance.
  • Analysis of covariance (ANCOVA) is essential for adjusting baseline scores to prevent biased treatment effect estimation, regardless of whether change or post-scores are used.

Conclusions:

  • The correlation between baseline and post-intervention scores can be calculated using the variance sum law.
  • This derived correlation aids in sample size calculation during the study design phase.
  • ANCOVA is recommended for adjusting baseline imbalances in RCTs during the analysis phase.