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

Randomized Experiments01:13

Randomized Experiments

9.2K
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...
9.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

511
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,...
511
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

505
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...
505
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
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...
7.3K
Blinding01:11

Blinding

4.0K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
4.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

677
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...
677

You might also read

Related Articles

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

Sort by
Same author

Fetal Echocardiography and Hyperoxia Testing after Serial Amnioinfusions: Results from the RAFT Trial Bilateral Renal Agenesis Arm.

Fetal diagnosis and therapy·2026
Same author

Neonatal Survival After Serial Amnioinfusions for Anhydramnios Due to Fetal Kidney Failure: The RAFT Clinical Trial.

JAMA·2026
Same author

Does early gastrostomy tube placement after stroke improve functional recovery and quality of life? A literature-informed pathway-decomposition analysis.

Neurological research·2026
Same author

Corticospinal tract risk modifies motor recovery after minimally invasive surgery for intracerebral hemorrhage: a secondary analysis of MISTIE-III.

medRxiv : the preprint server for health sciences·2026
Same author

Conducting multicenter trials through the trial innovation network comprehensive consultation.

Contemporary clinical trials·2026
Same author

Hematoma Interleukin-1 Receptor Antagonist Concentrations Predict Long-Term Outcome in Acute Human Intracerebral Hemorrhage.

Annals of neurology·2026

Related Experiment Video

Updated: Mar 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

15.4K

Improving precision by adjusting for prognostic baseline variables in randomized trials with binary outcomes, without

Jon Arni Steingrimsson1, Daniel F Hanley2, Michael Rosenblum3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States.

Contemporary Clinical Trials
|January 9, 2017
PubMed
Summary

This study offers guidance on selecting statistical methods for covariate adjustment in randomized clinical trials. It recommends standardization methods to improve precision and avoid issues with treatment effect heterogeneity.

Keywords:
Covariate adjustmentPost-stratification

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Related Experiment Videos

Last Updated: Mar 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

15.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Adjusting for prognostic baseline variables in randomized clinical trials can enhance precision and reduce sample size.
  • A significant challenge exists in choosing the optimal statistical method for covariate adjustment due to multiple available options.
  • The selection of a statistical method can impact trial validity, particularly if it yields uninterpretable estimates in the presence of treatment effect heterogeneity.

Purpose of the Study:

  • To provide practical guidance on selecting appropriate statistical methods for covariate adjustment in randomized clinical trials.
  • To address the challenge of choosing between multiple statistical methods for baseline variable adjustment.
  • To offer solutions that retain the benefits of covariate adjustment while avoiding potential pitfalls like uninterpretable estimates.

Main Methods:

  • Discusses the advantages and disadvantages of various statistical methods for covariate adjustment.
  • Recommends simple standardization methods from recent statistical literature as a preferred approach.
  • Provides software implementations in R and Stata for the recommended methods.

Main Results:

  • Identifies potential issues with commonly used covariate adjustment methods, such as uninterpretable estimates with treatment effect heterogeneity.
  • Demonstrates how standardization methods can overcome these limitations while preserving the benefits of adjustment.
  • Illustrates the application of these methods using a data example from a stroke clinical trial.

Conclusions:

  • Recommends the use of specific standardization methods for covariate adjustment in randomized clinical trials.
  • Highlights the importance of method selection for ensuring interpretable and valid trial conclusions.
  • Offers practical tools (R and Stata software) to facilitate the implementation of recommended statistical approaches.