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

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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

190
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,...
190
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

340
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
340
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

99
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
99
Study Design in Statistics01:15

Study Design in Statistics

9.4K
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...
9.4K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

869
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
869
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

215
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
215

You might also read

Related Articles

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

Sort by
Same author

Comparison of adaptive seamless Phase 2/3 designs for dose selection in clinical trials with multiple endpoints.

Clinical trials (London, England)·2025
Same author

Practical considerations in utilizing cluster randomized controlled trials conducted in biopharmaceutical industry.

Clinical trials (London, England)·2022
Same author

Sample Size Calculation When Planning Clinical Trials with Intercurrent Events.

Therapeutic innovation & regulatory science·2021
Same author

An adaptive seamless Phase 2-3 design with multiple endpoints.

Statistical methods in medical research·2021
Same author

A method for sample size calculation via E-value in the planning of observational studies.

Pharmaceutical statistics·2020
Same author

Two-level approaches to missing data in longitudinal trials with daily patient-reported outcomes.

Statistical methods in medical research·2019

Related Experiment Video

Updated: Oct 5, 2025

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

14.7K

Sequential modeling for a class of reference-based imputation methods in clinical trials with quantitative or binary

Yixin Fang1, Man Jin1

  • 1AbbVie Inc., North Chicago, Illinois, USA.

Statistics in Medicine
|January 25, 2022
PubMed
Summary

This study introduces new reference-based imputation methods for analyzing clinical trial data with missing outcomes. These methods offer a broader approach to sensitivity analysis for missing not at random data.

Keywords:
clinical trialsestimandintercurrent eventslongitudinal studiesmissing datamultiple imputationsensitivity analysis

More Related Videos

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

10.4K
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

2.2K

Related Experiment Videos

Last Updated: Oct 5, 2025

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

14.7K
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

10.4K
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

2.2K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Missing data in longitudinal clinical trials complicates treatment effect estimation.
  • Standard methods often assume data are missing at random, necessitating sensitivity analyses for missing not at random scenarios.
  • Existing reference-based imputation methods like Jump to Reference (J2R) and Copy Reference (CR) have limitations.

Purpose of the Study:

  • To propose a novel, comprehensive framework of reference-based imputation methods for sensitivity analysis in clinical trials.
  • To extend existing methods by considering a spectrum of potential treatment carry-over effects after treatment deviation.
  • To provide a flexible approach applicable to both quantitative and categorical longitudinal outcomes.

Main Methods:

  • Developed a wide class of reference-based imputation methods, encompassing J2R and CR as boundary cases.
  • The framework models the potential carried-over effect of an investigative treatment after deviation.
  • Demonstrated implementation through sequential modeling, suitable for diverse outcome types.

Main Results:

  • The proposed methods provide a unified framework for sensitivity analysis.
  • The sequential modeling approach allows for practical application in various clinical trial settings.
  • Causal-inference arguments and numerical examples validate the performance of the new methods.

Conclusions:

  • The novel reference-based imputation methods offer a robust approach to handling missing data in longitudinal clinical trials.
  • This framework enhances the reliability of treatment effect estimation under missing not at random assumptions.
  • The methods are versatile, applicable to both quantitative and categorical outcomes, and implementable via sequential modeling.