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

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

161
Body: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...
161
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
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
Random and Systematic Errors01:20

Random and Systematic Errors

14.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.0K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.0K
Bias01:22

Bias

7.2K
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...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Two-Step Error-Controlling Classifiers With Application to Cost-Effective Disease Diagnosis.

Statistics in medicine·2026
Same author

Estimating controlled direct treatment effects on pain intensity using structural mean models.

Pain reports·2026
Same author

Factors affecting power in stepped wedge trials when the treatment effect varies with time.

Trials·2026
Same author

Weighted Brier Score - an Overall Summary Measure for Risk Prediction Models with Clinical Utility Consideration.

Statistics in biosciences·2025
Same author

Adolescent Loneliness Trends and Contextual Correlates Across 38 Countries From 2000 to 2022.

The Journal of adolescent health : official publication of the Society for Adolescent Medicine·2025
Same author

Semiparametric joint modeling for biomarker trajectory before disease onset.

Biometrics·2025

Related Experiment Videos

Simple efficient bias corrected instrumental variable estimator for randomized trials with noncompliance.

Kwun Chuen Gary Chan1

  • 1Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. kcgchan@u.washington.edu

Contemporary Clinical Trials
|April 10, 2012
PubMed
Summary

A new bias-corrected instrumental variable (IV) estimator reduces bias in causal effect estimation for compliers in randomized trials. This method offers improved accuracy, especially with small sample sizes or significant noncompliance.

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Causal Inference
  • Econometrics

Background:

  • Instrumental variable (IV) estimators are standard for causal effects in randomized trials with noncompliance.
  • The complier average treatment effect (CATE) estimator, a ratio of unbiased estimators, is inherently biased.
  • Bias can be substantial with small sample sizes or high noncompliance rates.

Purpose of the Study:

  • To introduce and evaluate a simple bias-corrected instrumental variable (IV) estimator.
  • To demonstrate the bias and mean squared error improvements over the standard IV estimator.
  • To explore conditions under which the proposed and standard IV estimators consistently estimate population average treatment effects.

Main Methods:

  • A novel adjustment to the standard IV estimator is proposed.
  • Numerical examples are used to compare the bias and mean squared error of the proposed estimator against the standard IV estimator.
  • The performance of the estimator is assessed across various sample sizes and noncompliance levels.

Main Results:

  • The bias-corrected IV estimator significantly reduces bias, often by an order of magnitude.
  • The proposed estimator demonstrates substantially lower bias and mean squared error compared to the standard IV estimator for small to moderate sample sizes.
  • The estimator is computationally simple, requiring no iterative procedures, and performs well even with non-overlapping outcome distributions.

Conclusions:

  • The proposed bias-corrected IV estimator offers a valuable improvement for estimating causal effects in the presence of noncompliance.
  • This method provides a more accurate and reliable alternative to the standard IV estimator, particularly in challenging scenarios.
  • The study also identifies conditions for consistent estimation of population average treatment effects.