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

Goodness-of-Fit Test01:16

Goodness-of-Fit Test

3.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.3K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

205
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...
205
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

179
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...
179
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

125
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.
125
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

424
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...
424

You might also read

Related Articles

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

Sort by
Same author

Prognostic Implications of Combined p53 and Mismatch-Repair Immunophenotypes in Uterine Carcinosarcoma.

Medicina (Kaunas, Lithuania)·2026
Same author

Long-Term Durability and Dynamics of Anti-S-RBD IgG Response in Healthcare Workers: A Comparative Analysis of Homologous and Heterologous SARS-CoV-2 Vaccination Schedules in a 1-Year Serial Cross-Sectional Study.

Medical science monitor : international medical journal of experimental and clinical research·2026
Same author

Clinical impact of toxin detection in children with PCR-confirmed Clostridioides difficile infection.

European journal of pediatrics·2026
Same author

Epidemiological Characteristics and Mortality Predictors of Candidemia Due to <i>Candida albicans</i>: A Single-Center Experience from Türkiye.

Journal of fungi (Basel, Switzerland)·2025
Same author

Protective effect of dexpanthenol in tacrolimus-induced nephrotoxicity in rats.

Scientific reports·2025
Same author

Clinical impact and risk factors of enterococcal bacteremia in children: a focus on vancomycin resistance and mortality.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2025

Related Experiment Video

Updated: Jun 29, 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

14.5K

Balance diagnostics in propensity score analysis following multiple imputation: A new method.

Sevinc Puren Yucel Karakaya1, Ilker Unal1

  • 1Department of Biostatistics, Cukurova University, School of Medicine, Adana, Turkey.

Pharmaceutical Statistics
|April 6, 2024
PubMed
Summary

This study introduces a new method for assessing covariate balance after multiple imputation in propensity score analysis. The new combined method improves accuracy and resolves discrepancies found with existing approaches.

Keywords:
balancemissing datamultiple imputationobservational studiespropensity score analysis

More Related Videos

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

2.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Related Experiment Videos

Last Updated: Jun 29, 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

14.5K
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

2.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Area of Science:

  • Epidemiological research
  • Statistical methods in health research

Background:

  • Propensity score analysis (PSA) combined with multiple imputation (MI) is increasingly used in epidemiology.
  • Limited research exists on evaluating balance assessment methods in the context of PSA with MI.

Purpose of the Study:

  • To propose and evaluate a novel method for assessing covariate balance in propensity score analysis following multiple imputation.
  • To compare the performance of the new method against existing balance assessment techniques.

Main Methods:

  • A simulation study was designed to assess balance evaluation methods, including Leyrat's, Leite's, and a newly proposed combined method.
  • Simulated scenarios manipulated the presence and location of missing data and the inclusion of the outcome in the imputation model.

Main Results:

  • Leyrat's method demonstrated higher bias across all scenarios.
  • Leite's method and the new combined method achieved better balance, indicated by lower mean absolute differences.
  • The new combined method and Leite's method showed higher specificity and accuracy, particularly when the outcome was excluded from the imputation model.

Conclusions:

  • The proposed combined method effectively assesses balance in propensity score analysis with multiple imputation.
  • This new approach resolves discrepancies between existing methods (Leyrat's and Leite's) and offers improved accuracy.