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

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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 observed.
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Multiple Regression01:25

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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...

You might also read

Related Articles

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

Sort by
Same author

Prediction Model for Children With Anaphylaxis Who May Not Require Emergency Department Care: A Multicenter Retrospective Cohort Study.

The journal of allergy and clinical immunology. In practice·2026
Same author

Early Implementation and Clinical Outcomes of an Integrated Rheumatic Heart Disease Screening and Treatment Program in Northern Uganda.

Circulation. Population health and outcomes·2026
Same author

Implementation and evaluation of a pragmatic community streptococcal treatment programme to improve rheumatic heart disease primary prevention in Uganda.

BMJ global health·2026
Same author

Effectiveness and safety of ustekinumab in pediatric Crohn's disease: Results of the REALITI study.

Journal of pediatric gastroenterology and nutrition·2026
Same author

Non-injectable versus injectable epinephrine treatment thresholds for acute allergic reactions in the community.

The journal of allergy and clinical immunology. In practice·2026
Same author

Attention-deficit hyperactivity disorder as a moderator of the efficacy of family-based problem solving after pediatric traumatic brain injury.

Rehabilitation psychology·2025

Related Experiment Video

Updated: May 26, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A pseudo-Bayesian shrinkage approach to regression with missing covariates.

Nanhua Zhang1, Roderick J Little

  • 1Department of Epidemiology & Biostatistics, College of Public Health, University of South Florida, Tampa, Florida 33612-3085, USA. nzhang1@health.usf.edu

Biometrics
|December 14, 2011
PubMed
Summary

This study introduces a novel pseudo-Bayesian method for regression analysis with missing covariate data. The approach balances complete-case analysis and dropping variables, improving efficiency and reducing bias in statistical modeling.

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 26, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

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

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

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Linear regression models are frequently used in scientific research to understand relationships between variables.
  • Missing covariate data present a significant challenge in regression analysis, potentially leading to biased estimates and reduced statistical power.
  • Existing methods like complete-case analysis, ignorable likelihood methods, and nonignorable modeling have limitations in handling missing data.

Purpose of the Study:

  • To propose and evaluate a new pseudo-Bayesian approach for linear regression with missing covariates.
  • To offer a method that compromises between complete-case analysis and dropping variables with missing data.
  • To leverage information from incomplete cases when assumptions for dropping variables are met.

Main Methods:

  • A pseudo-Bayesian method is developed to handle missing values in the regressor W when analyzing the effect of Z on Y, controlling for W.
  • The proposed method is compared against complete-case analysis and dropping variables (DV) approaches.
  • Simulation studies are conducted to assess the performance and favorable properties of the new method.

Main Results:

  • The proposed pseudo-Bayesian approach demonstrates favorable properties in simulation studies.
  • The method effectively compromises between complete-case analysis and dropping variables, potentially improving estimation accuracy.
  • The approach is applied to a real-world liver cancer study, showing its practical utility.

Conclusions:

  • The novel pseudo-Bayesian method offers a valuable alternative for regression analysis with missing covariates.
  • This approach can enhance statistical inference by more effectively utilizing available data compared to traditional methods.
  • The method's extension to scenarios with multiple missing covariates is also discussed, highlighting its potential broad applicability.