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

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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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)...
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.
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...
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...
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:

You might also read

Related Articles

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

Sort by
Same author

Validation of the Standardized Outcomes in Nephrology - Life Participation (SONG-LP) Instrument in People Receiving Dialysis.

Kidney international reports·2026
Same author

Establishing a core outcome measure for cancer in trials in kidney transplantation: a standardized outcomes in nephrology-kidney transplantation consensus workshop report.

Transplant international : official journal of the European Society for Organ Transplantation·2026
Same author

Graft survival and rejection with repeated human leukocyte antigen (HLA) mismatch in kidney transplantation: a retrospective multicentre cohort study protocol.

BMC nephrology·2026
Same author

Bovine Lactoferrin Compared With Ferrous sulfate for Treating Iron-Deficiency Anemia in Bangladeshi Women-A Randomized Controlled Trial.

The Journal of nutrition·2026
Same author

Diagnostic stewardship in emergency departments using the UNTIE framework: a stepped-wedge cluster randomised study protocol.

BMJ open·2026
Same author

Transplantation and Mortality as Competing Outcomes Among Patients on Dialysis With Newly Diagnosed Cancer: A Longitudinal Cohort Study.

American journal of kidney diseases : the official journal of the National Kidney Foundation·2026
Same journal

Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal.

Revstat statistical journal·2021
See all related articles

Related Experiment Video

Updated: May 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

MISSING DATA IN REGRESSION MODELS FOR NON-COMMENSURATE MULTIPLE OUTCOMES.

Armando Teixeira-Pinto1, Sharon-Lise Normand

  • 1Serviço de Bioestatística e Informática Médica, CINTESIS, Faculdade de Medicina, Universidade do Porto, Portugal.

Revstat Statistical Journal
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study addresses missing data in biomedical research with multiple outcomes. Ignoring outcome correlations leads to inefficiency and bias, especially with missing data, necessitating advanced statistical methods.

Keywords:
direct maximizationlatent variablemaximum likelihoodmissing datamixed outcomesmultivariatenon-commensurateweighted generalized estimating equations

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

Related Experiment Videos

Last Updated: May 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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

Area of Science:

  • Biostatistics
  • Biomedical Data Analysis

Background:

  • Biomedical studies frequently measure multiple outcomes on various scales (continuous, binary, ordinal).
  • A common analysis strategy is to model each outcome independently, disregarding potential correlations.
  • This approach can result in reduced statistical efficiency and biased estimates when data are missing.

Purpose of the Study:

  • To investigate the impact of missing data on the analysis of multiple, non-commensurate outcomes in biomedical research.
  • To describe the consequences of missing data when employing likelihood and quasi-likelihood methods.
  • To propose an extension of these methods to handle missing observations within multiple outcome datasets.

Main Methods:

  • Analysis of multiple outcomes with varying scales (continuous, binary, ordinal).
  • Evaluation of likelihood and quasi-likelihood methods in the presence of missing data.
  • Development of an extended methodology to accommodate missing observations in multivariate outcome data.

Main Results:

  • Ignoring correlations among multiple outcomes leads to inefficiency and biased estimates with missing data.
  • Likelihood and quasi-likelihood methods are sensitive to missing data patterns.
  • The proposed extension effectively handles missing observations in multivariate outcome analyses.

Conclusions:

  • Accurate analysis of multiple biomedical outcomes requires accounting for correlations and missing data.
  • The proposed statistical methods improve efficiency and reduce bias in the presence of missing outcome data.
  • This methodology is crucial for reliable interpretation of complex biomedical research findings.