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

Probability Laws01:49

Probability Laws

44.3K
Overview
44.3K
Probability Distributions01:32

Probability Distributions

12.1K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
12.1K
Probability in Statistics01:14

Probability in Statistics

23.4K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.4K
Probability Histograms01:17

Probability Histograms

13.2K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.2K
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.1K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
12.1K
Margin of Error01:27

Margin of Error

7.6K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.6K

You might also read

Related Articles

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

Sort by
Same author

Development of a population-anchored Z-score for MRI-based knee osteoarthritis disease activity: Data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage open·2026
Same author

Impact of COVID-19 Lockdown on Depressive and Behavioral Symptoms in US Nursing Home Residents.

Journal of the American Medical Directors Association·2026
Same author

Patterns of Multimorbidity among Older Adults with Colorectal Cancer Living in Puerto Rico.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Early detection of knee osteoarthritis - The role of a composite disease activity metric: Data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage open·2026
Same author

Antidepressant Use Among US Nursing Home Residents With Dementia.

Journal of the American Geriatrics Society·2026
Same author

Citizenship status and report of trouble sleeping to a healthcare provider among U.S. adults with high-risk sleep symptoms: NHANES 2015-2018.

Sleep health·2026

Related Experiment Video

Updated: Jan 30, 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

Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse

Shao-Hsien Liu1,2, Stavroula A Chrysanthopoulou3, Qiuzhi Chang4

  • 1Clinical and Population Health Research Program, Graduate School of Biomedical Sciences.

Medical Care
|January 22, 2019
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) offers better validity and precision for marginal structural models (MSMs) with missing data compared to inverse probability weighting (IPW). This study highlights MI

More Related Videos

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
15:05

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation

Published on: May 20, 2020

9.3K

Related Experiment Videos

Last Updated: Jan 30, 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
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
15:05

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation

Published on: May 20, 2020

9.3K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Marginal structural models (MSMs) are increasingly used in epidemiology to address time-varying confounding.
  • Contradictory recommendations exist for handling missing data within MSMs.
  • This study addresses the need for clear guidance on missing data methods in MSMs.

Purpose of the Study:

  • To compare the validity and precision of MSMs estimates using complete case analysis (CC), multiple imputation (MI), and inverse probability weighting (IPW).
  • To evaluate these methods under various missing data scenarios, including different missing mechanisms, percentages, and confounder types.

Main Methods:

  • A plasmode simulation study was conducted using data from the Osteoarthritis Initiative.
  • 81 scenarios were simulated, varying missing mechanisms (MCAR, MAR, MNAR), missing percentages (10-30%), confounder types (time-independent/varying), and analytical approaches (CC, IPW, MI).
  • Performance was assessed using relative bias, mean squared error, and empirical power.

Main Results:

  • Multiple imputation (MI) generally yielded less biased estimates (1.2%-6.7%) with better precision (0.17-0.18) than inverse probability weighting (IPW) (relative bias: -5.3% to 8.0%; precision: 0.19-0.53).
  • MI demonstrated consistent empirical power across all simulated scenarios.
  • Complete case analysis (CC) was not explicitly detailed in results but implied to be less performant.

Conclusions:

  • Multiple imputation (MI) demonstrates a clear advantage over inverse probability weighting (IPW) for handling missing data in marginal structural models (MSMs).
  • These findings provide practical guidance for researchers using MSMs in the presence of missing confounder data.