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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Hazard Ratio01:12

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...

You might also read

Related Articles

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

Sort by
Same author

Influence of Gestational Age Measurement Technique on the Associations Between Maternal Characteristics and Birth Outcomes.

Paediatric and perinatal epidemiology·2026
Same author

Pregnancy Length Measurement Error: A Comparison of Last Menstrual Period and Ultrasonography with Ovulation-based Estimation.

Epidemiology (Cambridge, Mass.)·2025
Same author

The exposure potential restriction rule revisited.

American journal of epidemiology·2025
Same author

[Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration - a Korean translation].

Ewha medical journal·2025
Same author

Evaluating behavioral goals for eating timing, frequency, and distribution of daily carbohydrate consumption among youth with type 1 diabetes (MyPlan): A single arm pilot and feasibility study.

Diabetes research and clinical practice·2025
Same author

Advancing Principled Pharmacoepidemiologic Research to Support Regulatory and Healthcare Decision Making: The Era of Real-World Evidence.

Clinical pharmacology and therapeutics·2025
Same journal

Application of the E-value under non-proportional hazards.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Can the All of Us sample be reweighted to mirror a nationally representative sample? A comparison of mortality predictors.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Gut health, systemic inflammation, and linear growth among Indonesian infants: findings from the Action Against Stunting Hub observation cohort: Erratum.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Evaluating Estimators in Partially Identified Models.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Stratification and accumulation? Explaining changing mortality inequities between business owners and non-owners in the U.S. (1984-2022).

Epidemiology (Cambridge, Mass.)·2026
Same journal

Be wary of age-stratum aging in early-onset cancer trends.

Epidemiology (Cambridge, Mass.)·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 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

Nonexperimental comparative effectiveness research using linked healthcare databases

Til Stürmer1, Michele Jonsson Funk, Charles Poole

  • 1Pharmacoepidemiology Program, Department of Epidemiology, UNC Gillings School of Global Public Health University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. sturmer@unc.edu

Epidemiology (Cambridge, Mass.)
|April 6, 2011
PubMed
Summary

No abstract available in PubMed .

More Related Videos

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Related Experiment Videos

Last Updated: Jun 3, 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

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018