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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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...
Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
Comparing the Survival Analysis of Two or More Groups01:20

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...
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...

You might also read

Related Articles

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

Sort by
Same author

Multi-arm multi-stage platform trials for neurological disease: accelerating progress.

Lancet (London, England)·2026
Same author

Dose Accuracy and Content Uniformity of Low-Dose Metoprolol Tablets: 3D Printing Compared with Tablet Splitting in Hospital Pharmacy Setting.

Pharmaceutics·2026
Same author

Repurposed drug prioritization pipeline for a multi-arm platform trial in clinical Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Sample Size Calculation for the ROCI Design.

Statistics in medicine·2026
Same author

Age of onset of bipolar disorder: Association with lifetime psychiatric disorders and health-related quality of life.

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Bayesian analysis of the causal reference-based model for missing data in clinical trials.

Journal of biopharmaceutical statistics·2026

Related Experiment Video

Updated: May 21, 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

Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with

Vanina Héraud-Bousquet1, Christine Larsen, James Carpenter

  • 1Département des maladies infectieuses, Institut de Veille Sanitaire, 12 rue du Val d'Osne, 94415 St Maurice, France. v.bousquet@invs.sante.fr

BMC Medical Research Methodology
|June 12, 2012
PubMed
Summary
This summary is machine-generated.

Sensitivity analysis after multiple imputation is crucial for exploring missing data. This method helps assess how inferences change under different missing data assumptions, ensuring more robust epidemiological findings.

Related Experiment Videos

Last Updated: May 21, 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

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Multiple Imputation (MI) typically assumes data are Missing At Random (MAR).
  • Departures from MAR, where missing data depend on unobserved values, can bias results.
  • Sensitivity analysis is vital to assess the impact of potential non-random missingness.

Purpose of the Study:

  • To explore the sensitivity of inferences to departures from the MAR assumption using a reweighting approach.
  • To apply and demonstrate the utility of a post-multiple imputation sensitivity analysis method.
  • To develop guidelines for applying this approach in epidemiological studies.

Main Methods:

  • Utilized a method proposed by Carpenter et al. (2007) to approximate Missing Not At Random (MNAR) inferences.
  • Applied the sensitivity analysis to epidemiological data on 4343 Hepatitis C Virus (HCV) infected patients in France (2001-2007).
  • Assessed three risk factors for severe liver disease: alcohol consumption, HIV co-infection, and HCV genotype 3.

Main Results:

  • The association between severe liver disease and HIV co-infection was underestimated under MAR assumptions when HIV status was likely observed if positive.
  • Inferences for alcohol consumption and HCV genotype 3 were robust to plausible departures from the MAR assumption.
  • Demonstrated the practical utility of the sensitivity analysis method.

Conclusions:

  • Advocates for a pragmatic, widely applicable approach to explore departures from MAR post-MI.
  • Highlights the importance of sensitivity analyses for robust epidemiological conclusions.
  • Provides practical guidelines for implementing this approach in real-world studies.