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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
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...
101
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

224
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
224
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

609
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
609
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

161
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
161
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126

You might also read

Related Articles

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

Sort by
Same author

Determination of small peptides in sour meat with Raman spectroscopy and LC-MS/MS.

Analytical and bioanalytical chemistry·2026
Same author

Identification and Characterization of the <i>Efbzip</i> Gene Family in <i>Erianthus fulvus</i> and Exploration of Functional Genes Involved in Sucrose Metabolism.

Genes·2025
Same author

Effect of oral functional exercise combined with psychological intervention on oral frailty in older adults.

BMC oral health·2025
Same author

Lactylation in digestive system tumors: from mechanisms to therapeutic target.

Frontiers in oncology·2025
Same author

Survival benefits of different immunotherapies for hepatocellular carcinoma: a meta-analysis highlighting age, gender, etiology, and tumor burden.

Frontiers in immunology·2025
Same author

[Effect of porous surface structure on fatigue strength of 3D printed zirconia].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences·2025
Same journal

Developing professional etiquette and networking skills in pharmacy students: Evaluation of an etiquette dinner program.

Currents in pharmacy teaching & learning·2026
Same journal

Comparison of learning gain across face-to-face, mixed, and live online participation in a HyFlex National Pharmacist Examination Preparation Course: A controlled four-month comparative study.

Currents in pharmacy teaching & learning·2026
Same journal

The role of vaccinating pharmacists in public health advancement in Nigeria.

Currents in pharmacy teaching & learning·2026
Same journal

Empowering pharmacy education with core concepts: The big ideas that Australian pharmacy graduates must know for pharmacotherapy.

Currents in pharmacy teaching & learning·2026
Same journal

Comparing simulation modalities for health equity communication training in pharmacy education: A mixed-methods comparison of live-actor video, text-based, and AI-generated scenarios.

Currents in pharmacy teaching & learning·2026
Same journal

Exploring pharmacy students' perceptions on curriculum enhancements in a foundation of pharmacology course: A qualitative study.

Currents in pharmacy teaching & learning·2026
See all related articles

Related Experiment Video

Updated: Sep 11, 2025

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

10.8K

Logistic regression modeling: methodological insights and roadmap.

Lan N Bui1, Qian Ding2

  • 1Palm Beach Atlantic University Gregory School of Pharmacy, 901 S Flagler Drive, West Palm Beach, FL 33401, United States of America.

Currents in Pharmacy Teaching & Learning
|August 12, 2025
PubMed
Summary
This summary is machine-generated.

This review provides a roadmap for logistic regression in pharmacy research, detailing predictor selection, assumption checking, and transparent reporting for binary outcomes. It enhances reproducibility and understanding of risk factors in clinical and educational settings.

Keywords:
Logistic regressionLogistic regression modelingMedication utilization study

More Related Videos

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

3.4K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Related Experiment Videos

Last Updated: Sep 11, 2025

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

10.8K
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

3.4K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Area of Science:

  • Pharmacy Research
  • Biostatistics
  • Clinical Research

Background:

  • Logistic regression is widely used in clinical and educational research for binary outcomes.
  • Pharmacy researchers face challenges in predictor selection, assumption verification, interpretation, and transparent reporting.

Purpose of the Study:

  • To present a structured roadmap for conducting logistic regression in pharmacy research.
  • To address common challenges faced by researchers in applying logistic regression.

Main Methods:

  • Methodology review outlining key steps: outcome definition, predictor selection/coding, assumption checking, model fitting, and diagnostics.
  • Illustrative examples using published studies (OMICU, Spivey et al.) and a simulated pharmacy education dataset.
  • Comparison of statistical software (STATA, R, SAS) for logistic regression.

Main Results:

  • Demonstrates practical application of the roadmap through case studies and a simulated dataset.
  • Provides guidance on best practices for covariate selection, exploratory data analysis, and model development (e.g., stepwise, LASSO).
  • Offers insights into interpreting odds ratios, handling sparse data, evaluating model performance, and ensuring transparent reporting.

Conclusions:

  • The roadmap facilitates robust logistic regression analysis in pharmacy research.
  • Best practices enhance the reliability and interpretability of findings related to risk factors and binary outcomes.
  • Reproducible methods and software comparisons support researchers in applying logistic regression effectively.