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

8.8K
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...
8.8K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

535
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...
535
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

348
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,...
348
Study Design in Statistics01:15

Study Design in Statistics

9.9K
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...
9.9K
Blinding01:11

Blinding

3.8K
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.
3.8K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
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:  
1.2K

You might also read

Related Articles

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

Sort by
Same author

Approximate Bayesian Analysis for Borrowing External Controls for Randomized Controlled Trials With Dynamic Borrowing and Covariate Balancing Adjustment.

Pharmaceutical statistics·2025
Same author

Adaptive designs for best treatment identification with top-two Thompson sampling and acceleration.

Pharmaceutical statistics·2023
Same author

Propensity score matching and stratification using multiparty data without pooling.

Pharmaceutical statistics·2022
Same author

Response-adaptive trial designs with accelerated Thompson sampling.

Pharmaceutical statistics·2021
Same author

To use or not to use propensity score matching?

Pharmaceutical statistics·2020
Same author

A simple, doubly robust, efficient estimator for survival functions using pseudo observations.

Pharmaceutical statistics·2017

Related Experiment Video

Updated: Jan 1, 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

15.0K

Covariate adjustment for randomized controlled trials revisited.

Jixian Wang1

  • 1Celgene International, Boudry, Switzerland.

Pharmaceutical Statistics
|December 22, 2019
PubMed
Summary

Covariate adjustment in randomized controlled trials (RCTs) is a valuable method for estimating treatment effects. Recent advancements highlight its robustness and practical implications, recommending wider use in pharmaceutical statistics.

Keywords:
Neyman-Rubin modelaverage treatment effectcovariate adjustmentmodel misspecification

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K
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

2.5K

Related Experiment Videos

Last Updated: Jan 1, 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

15.0K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K
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

2.5K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Pharmaceutical Statistics

Background:

  • Covariate adjustment is a long-established technique in randomized controlled trials (RCTs).
  • Recent research has focused on model assumptions and robustness to mis-specification.
  • The Neyman-Rubin model and average treatment effect estimand are key areas of development.

Purpose of the Study:

  • To review recent advancements in covariate adjustment for RCTs.
  • To discuss the practical implications of these developments in pharmaceutical statistics.
  • To advocate for increased utilization of covariate adjustment in RCTs.

Main Methods:

  • Review of recent literature on covariate adjustment in RCTs.
  • Analysis of model assumptions and robustness to mis-specification.
  • Discussion of practical applications in pharmaceutical statistics.

Main Results:

  • Significant investigation and development have occurred regarding model assumptions and robustness.
  • The Neyman-Rubin model and average treatment effect estimand have been central to recent progress.
  • Covariate adjustment offers practical benefits for hypothesis testing and estimation in RCTs.

Conclusions:

  • Appropriate covariate adjustment should be more widely adopted in RCTs.
  • This method enhances both hypothesis testing and estimation of treatment effects.
  • The findings have direct implications for pharmaceutical statistics and clinical trial design.