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

Sampling Plans01:23

Sampling Plans

307
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
307
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

80
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
80
Cluster Sampling Method01:20

Cluster Sampling Method

12.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.9K

You might also read

Related Articles

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

Sort by
Same author

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.

Biometrics·2026
Same author

Dental Anxiety Management Techniques of North Carolina Dental Hygienists.

Journal of dental hygiene : JDH·2026
Same author

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.

The annals of applied statistics·2026
Same author

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

The annals of applied statistics·2026
Same author

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.

Journal of machine learning research : JMLR·2026
Same author

Data fusion methods for the heterogeneity of treatment effect and confounding function.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability·2026

Related Experiment Video

Updated: Sep 28, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

PARTITIONING AROUND MEDOIDS CLUSTERING AND RANDOM FOREST CLASSIFICATION FOR GIS-INFORMED IMPUTATION OF FLUORIDE

Yu Gu1, John S Preisser1, Donglin Zeng1

  • 1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.

The Annals of Applied Statistics
|March 31, 2022
PubMed
Summary

A new machine learning method, Partitioning Around Medoids and Random Forest (PAMRF), accurately imputes missing water fluoride data for dental caries research. This improves precision in oral health studies by leveraging spatial information.

Keywords:
clusteringmissing valuesmultiple imputationrandom forestspatial interpolation

More Related Videos

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

217

Related Experiment Videos

Last Updated: Sep 28, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

217

Area of Science:

  • Oral Health Epidemiology
  • Environmental Health
  • Biostatistics

Background:

  • Community water fluoridation is key for preventing dental caries.
  • Measuring domestic water fluoride is crucial for assessing caries risk but logistically difficult for large studies.
  • Significant data gaps exist in water fluoride concentrations for oral health research.

Purpose of the Study:

  • To develop and evaluate a novel spatial autocorrelation-informed method for imputing missing domestic water fluoride concentration data.
  • To address data limitations in a state-wide pediatric oral health study in North Carolina.
  • To improve the accuracy and precision of epidemiological estimates related to fluoride exposure and dental caries.

Main Methods:

  • Development of a machine learning-based imputation method combining Partitioning Around Medoids clustering and Random Forest classification (PAMRF).
  • Application of PAMRF to a North Carolina state-wide pediatric oral health study with ~75% missing water fluoride data.
  • Validation using leave-one-out cross-validation and simulation studies, comparing PAMRF against Inverse-Distance Weighting (IDW), Universal Kriging (UK), k-Nearest Neighbors (KNN), and Classification and Regression Trees (CART).

Main Results:

  • PAMRF significantly outperforms existing spatial interpolation and supervised learning methods in imputing water fluoride concentrations.
  • Imputed values can be filtered based on error rates or sample size requirements.
  • Inclusion of PAMRF-imputed data led to no substantial change in fluoride-caries association estimates but increased precision due to a larger effective sample size.

Conclusions:

  • PAMRF is a powerful and accurate method for imputing missing domestic water fluoride data, especially when geographical information is available.
  • This method enhances the utility of existing epidemiological datasets for oral health research.
  • PAMRF offers a robust solution for overcoming data limitations in studies of community water fluoridation and dental caries prevention.