Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sample Proportion and Population Proportion

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Dosage Regimens: Partial Pharmacokinetic Parameters

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...

Dosage Regimen Designs: Nomograms and Tabulations

Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...

You might also read

Related Articles

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

Sort by

Same author

Expert consensus on the detection and clinical application of tumor mutational burden.

Cancer biology & medicine·2026

Same author

Scalable ultrathin separator coatings engineered by initiated chemical vapor deposition for high-performance Lithium metal batteries.

Journal of colloid and interface science·2026

Same author

Successful Management of Coronary Guidewire Fracture Using Intravascular Ultrasound-Guided Stent Jailing Technique During Retrograde Chronic Total Occlusion Recanalization.

Clinical case reports·2026

Same author

Association Between Heroin Use and Depression: NHANES 2005-2018.

Addiction biology·2026

Same author

Persistence of interleukin-17 and interleukin-23 inhibitors in patients with plaque psoriasis: a real-world study in Taiwan.

The Journal of dermatological treatment·2026

Same author

Two Decades of Real-World Study in Newly Diagnosed Multiple Myeloma: Evolving Treatment and Outcomes in China with Reference to the United States.

Cancers·2026

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

[Making nomogram to estimate the population attributable fraction].

Hong Qiu¹, Ignatius Tak-Sun Yu

¹Department of Community and Family Medicine, School of Public Health, Chinese University of Hong Kong, HKSAR, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi

|September 13, 2008

Summary

This study introduces a nomogram for quickly estimating the population attributable fraction (PAF) using relative risk (RR) and exposure prevalence (Pe). Public health professionals can use this tool for rapid and accurate PAF calculations.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Related Experiment Videos

Last Updated: Jun 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Area of Science:

Epidemiology
Public Health
Biostatistics

Context:

Population attributable fraction (PAF) is a key metric in public health research.
Calculating PAF typically requires relative risk (RR) and population exposure prevalence (Pe).
Existing methods for PAF estimation can be time-consuming.

Purpose:

To introduce a novel nomogram for estimating population attributable fraction (PAF).
To link PAF directly with relative risk (RR) and population exposure prevalence (Pe).
To provide a user-friendly tool for public health professionals.

Summary:

A nomogram was developed to visually represent the relationship between PAF, RR, and Pe.
This graphical tool simplifies the calculation of PAF.
The method allows for quick and accurate estimation of PAF when RR and Pe data are available.

Impact:

Facilitates rapid PAF estimation in public health settings.
Enhances the accessibility of epidemiological data analysis.
Supports evidence-based decision-making in health interventions.