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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

311
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...
311
Censoring Survival Data01:09

Censoring Survival Data

612
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
612
Biostatistics: Overview01:20

Biostatistics: Overview

960
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
960
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

472
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.
472
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Longitudinal Studies01:26

Longitudinal Studies

584
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
584

You might also read

Related Articles

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

Sort by
Same author

Pediatric healthcare worker perspectives on implementation of a secure firearm storage program: a qualitative study.

BMC pediatrics·2026
Same author

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
Same author

Sex Differences in Fall Frequency, Risk Factors, and Outcomes in Parkinson's Disease: A Cross-Sectional Analysis.

Movement disorders clinical practice·2026
Same author

Nudging implementation of low tidal volume ventilation: a stepped wedge, cluster randomized trial.

Implementation science : IS·2026
Same author

A mixed methods evaluation of mechanisms for facilitation in pediatric primary care.

Implementation science communications·2026
Same author

STRONGER INSTRUMENTS VIA INTEGER PROGRAMMING IN AN OBSERVATIONAL STUDY OF LATE PRETERM BIRTH OUTCOMES.

The annals of applied statistics·2026
Same journal

Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

American journal of epidemiology·2026
Same journal

A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

American journal of epidemiology·2026
Same journal

Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

American journal of epidemiology·2026
Same journal

Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

American journal of epidemiology·2026
Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026
Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026
See all related articles

Related Experiment Video

Updated: Mar 5, 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

15.4K

Instrumental Variable Methods for Continuous Outcomes That Accommodate Nonignorable Missing Baseline Values.

Ashkan Ertefaie1,2,3, James H Flory4, Sean Hennessy3

  • 1Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, New York.

American Journal of Epidemiology
|March 25, 2017
PubMed
Summary
This summary is machine-generated.

Instrumental variable (IV) methods can estimate unbiased treatment effects, even with missing data. A new 2-step procedure using provider preference effectively handles nonignorable missing values, supporting sulfonylureas

Keywords:
instrumental variablesnonignorable missing valuesprovider preferenceunmeasured confounders

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

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

Published on: May 13, 2022

6.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Related Experiment Videos

Last Updated: Mar 5, 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

15.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

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

Published on: May 13, 2022

6.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Economics

Background:

  • Instrumental variable (IV) methods are crucial for unbiased treatment effect estimation.
  • Unmeasured confounders and nonignorable missing covariate data can bias these estimates.
  • Existing imputation methods may fail when missingness is nonignorable.

Purpose of the Study:

  • To propose a novel 2-step instrumental variable (IV) procedure.
  • To address challenges of nonignorable missing baseline variables in treatment effect estimation.
  • To provide a valid method for analyzing treatment effects with complex missing data patterns.

Main Methods:

  • A 2-step procedure utilizing health-care provider preference as an instrumental variable (IV).
  • Step 1: Estimate IV using complete-case analysis with a random-effects model including IV-confounders.
  • Step 2: Estimate treatment effect via 2-stage least squares IV, excluding IV-confounders with missing values.

Main Results:

  • Simulation results demonstrate the method's validity.
  • Applied to compare sulfonylureas vs. metformin effects on body mass index (BMI).
  • Baseline BMI and glycosylated hemoglobin data exhibited nonignorable missingness.

Conclusions:

  • The proposed 2-step IV method effectively handles nonignorable missing covariate data.
  • The analysis supports an association between sulfonylureas and weight gain.
  • This approach enhances the reliability of treatment effect estimation in epidemiological studies.