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

Curve Sketching and Derivatives01:22

Curve Sketching and Derivatives

Understanding the behavior of a function through its first and second derivatives is essential for analyzing its graph. Derivatives provide insight into where a function increases or decreases, where it attains local maxima or minima, and how its curvature behaves across different intervals.The first derivative of a function reveals the slope of the tangent line at any given point. Points where the derivative is zero or undefined are considered critical, as they often indicate potential extrema...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

You might also read

Related Articles

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

Sort by
Same author

Combination Therapy with Olmesartan and Amlodipine in the Treatment of Hypertension.

Pharmaceuticals (Basel, Switzerland)·2016
Same author

Clinical trials: robust tests are wonderful for imperfect data.

American journal of therapeutics·2013
Same author

Multistage regression, a novel method for making better predictions from your efficacy data.

American journal of therapeutics·2013
Same author

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

American journal of therapeutics·2013
Same author

Machine Learning in Therapeutic Research: The Hard Work of Outlier Detection in Large Data.

American journal of therapeutics·2013
Same author

Assessing seasonality in clinical research.

Clinical chemistry and laboratory medicine·2012
Same journal

Deucravacitinib for Generalized Pustular Psoriasis and Comorbid Polymyositis: Dual Efficacy and Favorable Safety.

American journal of therapeutics·2026
Same journal

Repurposed Drugs and Cardiovascular Morbidity: A Cost-Effectiveness Analysis.

American journal of therapeutics·2026
Same journal

Do Not Treat a Sequencing Report: Therapeutic Stewardship in Endometrial Microbiome Testing.

American journal of therapeutics·2026
Same journal

Right Side Accessory Pathway Mediated Cardiomyopathy Treated with Amiodarone: First Adult Case Report.

American journal of therapeutics·2026
Same journal

Dose-Dependent Escitalopram-Induced Anejaculation and Anorgasmia: Rapid Reversal After Discontinuation and Bupropion Therapy.

American journal of therapeutics·2026
Same journal

Critical Appraisal of "Continued versus Interrupted Oral Anticoagulation During Transcatheter Aortic Valve Replacement in Patients with Atrial Fibrillation: A Meta-Analysis".

American journal of therapeutics·2026
See all related articles

Related Experiment Video

Updated: May 11, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

Clinical Trials: Spline Modeling is Wonderful for Nonlinear Effects.

Ton J Cleophas1

  • 1European College Pharmaceutical Medicine, Lyon, France.

American Journal of Therapeutics
|May 22, 2013
PubMed
Summary
This summary is machine-generated.

Spline modeling accurately assesses exposure-outcome relationships in clinical trials, detecting subtle patterns missed by traditional regression. This advanced method enhances prediction for better health decisions.

More Related Videos

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Related Experiment Videos

Last Updated: May 11, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Area of Science:

  • Biostatistics
  • Clinical Research Methodology

Background:

  • Historically, nonlinear shapes were modeled using physical splines.
  • Mathematical spline methods have largely replaced mechanical approaches.
  • Assessing complex exposure-outcome relationships in clinical trials remains a challenge.

Purpose of the Study:

  • To evaluate spline modeling for assessing exposure-outcome relationships in clinical trials.
  • To determine if spline modeling can identify patterns missed by simpler regression models.
  • To explore the potential of spline modeling in clinical research.

Main Methods:

  • Utilized spline curves composed of cubic functions.
  • Applied spline modeling to a clinical trial on quantity and quality of care.
  • Conducted analysis using SPSS statistical software.

Main Results:

  • Spline curves accurately captured top quality of care, unlike traditional methods.
  • Spline models avoided spurious sinusoidal patterns observed in traditional analyses.
  • Spline modeling achieved a near-perfect match to original data values.

Conclusions:

  • Spline modeling is effective for assessing clinical trial exposure-outcome relationships.
  • Spline modeling identifies relevant patterns often overlooked by simpler models.
  • Spline modeling offers significant potential for improving clinical research predictions and health decisions.