Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

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...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...

Assumptions of Survival Analysis

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.

Multiple Regression

Multiple Regression

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...

You might also read

Related Articles

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

Sort by

Same author

Impact of Lactation Consultants on the Breastfeeding Prevalence at 6 Months: Systematic Review and Meta-Analysis.

Breastfeeding medicine : the official journal of the Academy of Breastfeeding Medicine·2025

Same author

Demographic and health community-based surveys to inform a malaria elimination project in Magude district, southern Mozambique.

BMJ open·2020

Same author

Global determinants of mortality in under 5s: 10 year worldwide longitudinal study.

BMJ (Clinical research ed.)·2013

Same author

Dose-response association of physical activity with acute myocardial infarction: do amount and intensity matter?

Preventive medicine·2013

Same author

Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys.

BMC medical research methodology·2012

Same author

Trends in in-hospital mortality and six-month outcomes in patients with a first acute myocardial infarction. Change over the last decade.

Revista espanola de cardiologia·2010

Search research articles

Related Experiment Videos

Multiple imputation analysis of case-cohort studies.

Helena Marti¹, Michel Chavance

¹Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Biostatistics team, F-94807 Villejuif, France. helena.marti-soler@inserm.fr

Statistics in Medicine

|February 26, 2011

Summary

Multiple imputation offers an efficient alternative to weighted estimators for analyzing case-cohort studies. This method provides unbiased and precise results, especially when the imputation model is correctly specified, improving data analysis efficiency.

Related Experiment Videos

Area of Science:

Epidemiology
Biostatistics

Background:

Traditional case-cohort study analysis often uses weighted estimators, which may not be fully efficient.
Multiple imputation (MI) presents a promising alternative by utilizing all available data.

Purpose of the Study:

To evaluate the efficiency and accuracy of multiple imputation for analyzing case-cohort studies.
To demonstrate that a correctly specified imputation model improves estimator performance.

Main Methods:

Simulated case-cohort data and case-cohort data sampled from real cohorts were analyzed.
The study compared multiple imputation estimators with traditional weighted estimators.
Imputation models were estimated using fully observed data, including case status as an explanatory variable.

Main Results:

When the imputation model was correct, multiple imputation yielded unbiased and efficient estimates, with precision gains of 8-37% for phase-1 and 5-19% for phase-2 variables.
Misspecified imputation models resulted in slightly biased but still more efficient MI estimators compared to weighted estimators.
MI proved unbiased and precise for phase-2 variables, and slightly more precise for phase-1 variables in real cohort data.

Conclusions:

Multiple imputation is an efficient and viable technique for analyzing case-cohort data.
Correct specification of the imputation model is crucial for unbiased and efficient estimation.
The study suggests using weighted estimators for initial analysis and MI for refining precision.