Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

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 Cox...

Randomized Experiments

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 Statistics

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...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

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...

You might also read

Related Articles

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

Sort by

Same author

Genetic underpinnings of the heterogeneous impact of obesity on lipid levels and cardiovascular disease.

Genome medicine·2025

Same author

Polygenic risk score for type 2 diabetes shows context-dependent effects across populations.

Nature communications·2025

Same author

Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program.

American journal of human genetics·2025

Same author

Genome-wide association study provides novel insight into the genetic architecture of severe obesity.

PLoS genetics·2025

Same author

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals.

Genome biology·2025

Same author

An Efficient Lasso Framework for Admixture-Aware Polygenic Scores.

bioRxiv : the preprint server for biology·2025

Search research articles

Related Experiment Video

Updated: Jul 5, 2026

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

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

Published on: January 8, 2020

Semiparametric estimation exploiting covariate independence in two-phase randomized trials.

James Y Dai¹, Michael LeBlanc, Charles Kooperberg

¹Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. ydai@fhcrc.org

|May 16, 2008

Summary

Exploiting gene-environment independence in two-phase sampling significantly improves efficiency for estimating treatment-biomarker interactions in clinical trials. New methods offer faster computation and reliable variance estimates.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: Jul 5, 2026

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

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

Published on: January 8, 2020

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

Biostatistics
Clinical Trials
Genomics

Background:

Semiparametric estimation can leverage gene-environment independence for efficiency gains.
Two-phase sampling in randomized clinical trials requires efficient estimation of treatment-biomarker interactions.

Purpose of the Study:

To develop efficient semiparametric methods for estimating treatment-biomarker interactions in two-phase randomized clinical trials.
To incorporate the independence between randomized treatment and baseline markers.

Main Methods:

Developed a Newton-Raphson algorithm using profile likelihood for semiparametric maximum likelihood estimation (SPMLE).
Algorithm handles continuous phase-one outcomes and phase-two biomarkers.
Proposed a maximum estimated likelihood estimator (MELE) for computational efficiency, using a one-step empirical covariate distribution.

Main Results:

The proposed methods efficiently estimate treatment-biomarker interactions by exploiting covariate independence.
SPMLE and MELE accommodate continuous outcomes and biomarkers.
MELE provides a closed-form variance estimate with minimal efficiency loss compared to SPMLE.

Conclusions:

Exploiting covariate independence in two-phase sampling substantially increases estimation efficiency.
The developed methods are effective for analyzing treatment-biomarker interactions in complex clinical trial designs.