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

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Sampling Plans01:23

Sampling Plans

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...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

You might also read

Related Articles

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

Sort by
Same author

Estimating diagnostic accuracy under uncertainty about disease status: a sepsis case study.

Journal of clinical epidemiology·2026
Same author

Circulating Tumour DNA and Extracellular Vesicle-Associated DNA as Biomarkers for Cancer Detection: A Systematic Review and Meta-Analysis.

Journal of extracellular biology·2026
Same author

Counting sheep: Louis Pasteur and the first registered public vaccine trial.

European journal of epidemiology·2026
Same author

The Estimand Framework in Diagnostic Accuracy Studies.

Statistics in medicine·2025
Same author

Novel and optimized diagnostics for pediatric TB in endemic countries: NOD-pedFEND study protocol.

BMC pediatrics·2025
Same author

Parallel use of low-complexity automated nucleic acid amplification tests on respiratory and stool samples with or without lateral flow lipoarabinomannan assays to detect pulmonary tuberculosis disease in children.

The Cochrane database of systematic reviews·2025
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Jun 30, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Efficient sampling approaches to address confounding in database studies.

James A Hanley1, Nandini Dendukuri

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada. james.hanley@mcgill.ca

Statistical Methods in Medical Research
|September 26, 2008
PubMed
Summary
This summary is machine-generated.

Population databases are crucial for pharmacoepidemiology research on medication side effects. This review explores sampling and analysis methods to address missing confounding variables in these valuable datasets.

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: Jun 30, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Pharmacoepidemiology
  • Biostatistics
  • Health Informatics

Background:

  • Population-based databases are vital for pharmacoepidemiology.
  • These databases facilitate large-scale studies of medication effects without direct patient contact or recall bias.
  • A key limitation is the frequent absence of crucial confounding variables.

Purpose of the Study:

  • To review sampling approaches for addressing confounding in population-based databases.
  • To describe data-analysis methods for efficiently managing confounding.
  • To enhance the validity of pharmacoepidemiological research using administrative data.

Main Methods:

  • Review of existing literature on sampling strategies.
  • Description of statistical techniques for confounding assessment.
  • Discussion of methods for handling missing data in large databases.

Main Results:

  • Identified various sampling techniques applicable to population databases.
  • Outlined data-analysis methods to mitigate confounding bias.
  • Demonstrated efficient strategies for managing unmeasured confounders.

Conclusions:

  • Effective sampling and analysis methods can overcome limitations of population-based databases.
  • These approaches improve the reliability of pharmacoepidemiological findings.
  • Researchers can better assess medication effects by addressing confounding variables.