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Related Concept Videos

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...
Study Design in Statistics01:15

Study Design in Statistics

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...
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...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

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 phenomenon...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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...

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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
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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

COADVISE: covariate adjustment with variable selection in randomized controlled trials.

Yi Liu1,2, Ke Zhu1,3, Larry Han4

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|July 15, 2026
PubMed
Summary

Covariate adjustment in randomized controlled trials improves treatment effect estimation. The COADVISE framework efficiently selects relevant covariates, including nonlinear adjustments, for more reliable results.

Keywords:
complete randomizationcovariate balanceefficiency gainhigh-dimensional datamodel misspecification

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Last Updated: Jul 16, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

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

Background:

  • Adjusting for covariates in randomized controlled trials (RCTs) enhances treatment effect estimation.
  • Handling numerous covariates and their nonlinear transformations presents a significant challenge in statistical analysis.
  • The Best Apnea Interventions for Research (BestAIR) trial data (N=196, p=114) highlights the need for efficient covariate adjustment methods when the number of covariates approaches the sample size.

Purpose of the Study:

  • To propose a principled covariate adjustment with variable selection (COADVISE) framework.
  • To enable selection of covariates most relevant to the outcome, accommodating both linear and nonlinear adjustments.
  • To provide consistent and efficient treatment effect estimates with robust variance estimation, even with model misspecification.

Main Methods:

  • Developed the COADVISE framework for covariate adjustment and variable selection.
  • Incorporated methods for handling both linear and nonlinear covariate adjustments.
  • Utilized theoretical analysis, extensive simulations, and re-analysis of the BestAIR trial data.

Main Results:

  • COADVISE ensures consistent estimates with improved efficiency over unadjusted estimators.
  • The framework provides robust variance estimation, even under outcome model misspecification.
  • Demonstrated efficiency gains through theoretical analysis, simulations, and re-analysis of the BestAIR trial data, comparing alternative variable selection strategies.

Conclusions:

  • The COADVISE framework offers a principled approach to covariate adjustment and variable selection in RCTs.
  • It enhances the credibility and efficiency of treatment effect estimation, particularly with high-dimensional covariates.
  • A user-friendly R package, Coadvise, is available for practical implementation, with cautionary recommendations provided.