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

Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
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:
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
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...
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

Effectiveness of maternal COVID-19 vaccination varies by gestational timing: results from a claims-based cohort study, 2020-2022.

Research square·2026
Same author

Comparative effectiveness of alternative times to opioid agonist treatment taper initiation on taper completion and all-cause mortality among people with opioid use disorder: A retrospective population-based target trial emulation study in British Columbia, Canada, 2010-2020.

Addiction (Abingdon, England)·2026
Same author

SARS-CoV-2 vaccination and attenuation of breakthrough infection severity: A systematic global review and meta-analysis.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Influenza vaccine responses differ between young children previously exposed to influenza antigens via infection versus vaccination.

Communications medicine·2026
Same author

Negatives about positivity and consistency as conditions for causal inference.

American journal of epidemiology·2026
Same author

Measuring population immunity against influenza using individual antibody titres: a multicountry, retrospective observational study.

The Lancet. Infectious diseases·2026
Same journal

Dental amalgam, chronic disease risk, and removing mercury from dental practice.

International journal of epidemiology·2026
Same journal

Age at menarche and adverse pregnancy and perinatal outcomes: triangulating evidence from multivariable and Mendelian randomization analyses.

International journal of epidemiology·2026
Same journal

Life-course trajectories of cardiovascular disease risk factors in rural India: Andhra Pradesh Children and Parents Study (APCAPS) 2003-2023.

International journal of epidemiology·2026
Same journal

Cohort Profile Update: The Young Lives study.

International journal of epidemiology·2026
Same journal

From the departing Editors in Chief.

International journal of epidemiology·2026
Same journal

Data Resource Profile: Cheeloo Lifespan Electronic-health reseArch Data-library (Cheeloo LEAD).

International journal of epidemiology·2026
See all related articles

Related Experiment Video

Updated: May 16, 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

Bayesian regression in SAS software.

Sheena G Sullivan1, Sander Greenland

  • 1Department of Epidemiology, University of California, Los Angeles, CA, USA. sheena.sullivan@influenzacentre.org

International Journal of Epidemiology
|December 12, 2012
PubMed
Summary
This summary is machine-generated.

Bayesian data augmentation offers an efficient alternative to Markov-chain Monte Carlo (MCMC) for regression analyses. This method, implemented in SAS, significantly speeds up analyses with sparse data or prior information.

More Related Videos

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

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 16, 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

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

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:

  • Epidemiology
  • Biostatistics
  • Statistical Computing

Background:

  • Bayesian methods are valuable for epidemiologic analyses with sparse data or substantial prior information.
  • Existing data augmentation methods for Bayesian analysis are underutilized.
  • Efficient implementation is needed for broader adoption.

Purpose of the Study:

  • To provide practical guidance on conducting Bayesian regression analyses using data augmentation.
  • To demonstrate implementation with readily available statistical software (SAS).
  • To compare the efficiency of data augmentation against Markov-chain Monte Carlo (MCMC).

Main Methods:

  • Detailed description and SAS code for data augmentation in Bayesian and semi-Bayes regression.
  • Application to a real-world logistic regression analysis.
  • Comparison with traditional MCMC procedures for model fitting.

Main Results:

  • Data augmentation and MCMC yielded similar results for logistic regression.
  • Data augmentation was substantially faster than MCMC, completing in approximately 0.5% of the time.
  • The approach is extendable to other regression models.

Conclusions:

  • Bayesian data augmentation is a computationally efficient method for regression analyses.
  • This approach facilitates the use of Bayesian methods in epidemiology and other fields.
  • The provided SAS code and guidance can increase the accessibility of these powerful statistical techniques.