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

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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...
Behrens–Fisher Test00:57

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...
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

European Society of Contact Dermatitis Guideline for Diagnostic Patch Testing-Recommendations on Best Practice (Update 2026).

Contact dermatitis·2026
Same author

SPIRIT 2025 statement: updated guideline for protocols of randomised trials.

Lancet (London, England)·2026
Same author

Sample Size Calculation for the ROCI Design.

Statistics in medicine·2026
Same author

Bayesian analysis in confirmatory clinical trials: A narrative review and discussion of current practice.

Clinical trials (London, England)·2026
Same author

Adjusting for confounding in population administrative data when confounders are only measured in a linked cohort.

International journal of population data science·2026
Same author

An investigation of the impact of using contrast- and arm-synthesis models for network meta-analysis.

Research synthesis methods·2026

Related Experiment Video

Updated: Jul 16, 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

Standardized mean differences in individually-randomized and cluster-randomized trials, with applications to

Ian R White1, James Thomas

  • 1MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK. ian.white@mrc-bsu.cam.ac.uk

Clinical Trials (London, England)
|November 11, 2005
PubMed
Summary

Standardized mean differences (SMD) compare intervention effects across studies. This research reviews exact formulas for calculating SMD and standard errors, especially for cluster-randomized trials, to improve accuracy.

More Related Videos

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Related Experiment Videos

Last Updated: Jul 16, 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

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Meta-Analysis

Background:

  • Standardized mean difference (SMD) is crucial for comparing intervention effects across diverse quantitative outcomes, particularly in meta-analysis.
  • Calculating SMD involves the outcome's standard deviation, introducing complexity due to uncertainty and potential bias.

Purpose of the Study:

  • To review and advocate for the use of exact formulae for calculating SMD and standard errors, addressing complexities arising from standard deviation uncertainty.
  • To extend these formulae to cluster-randomized trials and provide guidance on implementation using published data.

Main Methods:

  • Review of approximate and exact formulae for calculating standardized mean differences and their standard errors.
  • Extension of formulae to accommodate the specific design of cluster-randomized trials.
  • Description of methods for estimating the standard deviation within these calculations.

Main Results:

  • Exact formulae are recommended over approximate ones for unbiased SMD and standard error calculation.
  • The study provides extended formulae applicable to cluster-randomized trials.
  • Potential pitfalls leading to significant errors, particularly in cluster-randomized settings, are identified.

Conclusions:

  • The use of exact formulae for standardized mean differences is essential for accurate meta-analysis, especially when dealing with standard deviation uncertainty.
  • Accurate calculation methods are critical for cluster-randomized trials to avoid major errors.
  • The findings support improved statistical practices in synthesizing evidence from quantitative studies.