Jove
Visualize
Contact Us

Related Concept Videos

Correlation and Regression00:53

Correlation and Regression

3.8K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.8K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

490
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
490
Correlations02:20

Correlations

34.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
34.7K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.0K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.0K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

729
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
729
Coefficient of Correlation01:12

Coefficient of Correlation

7.7K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Impact of tariff on Indian AYUSH products: Evidence from top importers of India.

Journal of Ayurveda and integrative medicine·2026
Same author

Enhancing triapine treatment: strategies for dose optimization and methemoglobin level mitigation.

Cancer chemotherapy and pharmacology·2026
Same author

Modernizing Preclinical Drug Development: The Role of New Approach Methodologies.

ACS pharmacology & translational science·2025
Same author

Genomic introgression and expression profiling of the KTI null allele in soybean through elite-by-elite backcrossing.

Plant physiology and biochemistry : PPB·2025
Same author

Large Language Models and Their Applications in Drug Discovery and Development: A Primer.

Clinical and translational science·2025
Same author

Vonoprazan in Management of Refractory Gastroesophageal Reflux Disease: An Indian Expert Group Consensus Statements.

The Journal of the Association of Physicians of India·2025
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 Video

Updated: May 1, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

Implications of choosing a correlation structure on model selection and parameter estimation.

Lokesh Jain, Pravin Jadhav, Jogarao Gobburu

    International Journal of Clinical Pharmacology and Therapeutics
    |April 15, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Choosing between statistical or biological correlation structures impacts pharmacokinetic model selection and parameter estimation. Ignoring true biological correlation can inflate variability estimates but still reflects true variability when accounting for covariates like weight.

    More Related Videos

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.1K
    The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
    14:14

    The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

    Published on: May 13, 2022

    5.8K

    Related Experiment Videos

    Last Updated: May 1, 2026

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
    04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    2.9K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.1K
    The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
    14:14

    The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

    Published on: May 13, 2022

    5.8K

    Area of Science:

    • Pharmacokinetics and Pharmacometrics
    • Statistical Modeling in Drug Development

    Background:

    • Understanding the correlation structure between pharmacokinetic parameters like clearance (CL) and volume of distribution (Vd) is crucial for accurate model building.
    • The choice between modeling biological correlation (e.g., via covariates) or statistical correlation impacts model performance and parameter interpretation.

    Purpose of the Study:

    • To evaluate the implications of selecting a statistical versus a biological correlation structure on pharmacokinetic model selection and parameter estimation.
    • To compare model performance in terms of parameter equivalence, bias, and imprecision under different correlation assumptions.

    Main Methods:

    • Simulations were conducted using a one-compartment model with IV bolus administration and 30% interindividual variability on CL and Vd.
    • Biological correlation (weight as covariate) and statistical correlation (off-diagonal elements in omega matrix) were implemented and compared.
    • Model selection, parameter equivalence, bias, and imprecision were assessed across different correlation scenarios.

    Main Results:

    • Fixed-effect parameter estimation (CL, Vd) remained robust regardless of correlation structure inclusion.
    • Random-effect parameter estimates were not significantly influenced by the inclusion or exclusion of statistical correlation.
    • Coefficient of variation for CL (CVCL) and Vd (CVV) were inflated (18-35%) when true biological correlation was ignored or modeled statistically, but these estimates still reflected true total variability.

    Conclusions:

    • While ignoring true biological correlation can inflate variability estimates, using statistical correlation in the absence of covariate information yields similar ranges for individual parameters in future simulations.
    • Statistical correlation serves as a suitable alternative when the true biological correlation structure is unknown in real-world pharmacokinetic modeling scenarios.