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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

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Related Experiment Video

Updated: May 9, 2026

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

[Propensity scores in observational research].

Rolf H H Groenwold1

  • 1Universitair Medisch Centrum Utrecht, Julius Centrum voor Gezondheidswetenschappen en Eerstelijns Geneeskunde, Utrecht, the Netherlands. r.h.h.groenwold@umcutrecht.nl

Nederlands Tijdschrift Voor Geneeskunde
|July 18, 2013
PubMed
Summary

Propensity score methods help control for measured confounders in observational studies. These statistical techniques improve the reliability of research findings by balancing participant characteristics.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Observational research often faces challenges with confounding variables.
  • Measured confounders, such as age, can bias study results.
  • Propensity score methods offer a statistical approach to address confounding.

Purpose of the Study:

  • To explain the concept and application of propensity scores in research.
  • To highlight the utility of propensity score methods in controlling for measured confounders.
  • To emphasize the limitations regarding unmeasured confounders.

Main Methods:

  • Propensity score is defined as the probability of treatment/exposure given measured confounders.
  • Methods include stratification, matching, regression adjustment, and weighting using the propensity score.

Related Experiment Videos

Last Updated: May 9, 2026

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

  • Ensuring balance of measured confounders across study groups is crucial.

    • Main Results:

    • Propensity score methods effectively control for measured confounders.
    • These methods can manage more confounders than alternative approaches, especially for rare outcomes.
    • Participants with similar propensity scores exhibit comparable distributions of measured confounders.

    Conclusions:

    • Propensity score methods are valuable tools for mitigating bias in observational studies.
    • Transparency in reporting included confounders and achieved balance is essential for study validity.
    • These methods enhance the robustness of findings by addressing measured confounding effectively.