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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Variability: Analysis01:11

Variability: Analysis

954
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
954
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

595
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
595
Randomized Experiments01:13

Randomized Experiments

6.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Psychological Distress, Caregiver Burden and Quality of Life Among Informal Carers of First Nations Australians Diagnosed With Cancer: A Cross-Sectional Study.

Cancer control : journal of the Moffitt Cancer Center·2026
Same author

Promoting CHANGE cluster randomised controlled trial to improve food outlet healthiness in Australian sport and recreation facilities: protocol.

BMJ open·2026
Same author

The Ideal Trial: Defining Causal Estimands that Balance Relevance and Feasibility in Target Trial Emulations and Actual Randomized Trials.

Epidemiology (Cambridge, Mass.)·2026
Same author

"Identifying variables that independently predict…" is not a well-defined research task.

Journal of clinical epidemiology·2025
Same author

Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.

Statistics in medicine·2025
Same author

Efficacy of an online mindfulness program (<i>MindOnLine</i>) to reduce fear of recurrence in people living with-and beyond-breast, prostate or colorectal cancer: a randomized controlled trial.

EClinicalMedicine·2025
Same journal

Integrating health economics and implementation science: a call to action.

BMC medical research methodology·2026
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: Apr 30, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K

Comparison of methods for imputing limited-range variables: a simulation study.

Laura Rodwell1, Katherine J Lee, Helena Romaniuk

  • 1Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, Flemington Road, Parkville, Melbourne, Victoria 3052, Australia. laura.rodwell@mcri.edu.au.

BMC Medical Research Methodology
|April 29, 2014
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) can produce biased marginal mean estimates for limited-range variables, especially with skewed data. Methods restricting imputed values may lead to inaccurate results when dealing with missing data in health research.

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.0K
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

2.9K

Related Experiment Videos

Last Updated: Apr 30, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.0K
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

2.9K

Area of Science:

  • Statistics
  • Biostatistics
  • Psychiatric Epidemiology

Background:

  • Multiple imputation (MI) is a statistical method for handling missing data.
  • The plausibility of imputed values for limited-range variables is not well understood.
  • Imputed values may fall outside the observable range for limited-range variables.

Purpose of the Study:

  • To compare methods for imputing limited-range variables.
  • To focus on imputation methods that restrict the range of imputed values.
  • To assess the impact of imputation on statistical inference.

Main Methods:

  • Used adolescent health data with three skewed General Health Questionnaire (GHQ) variables.
  • Introduced missing data (33%) at random or missing at random.
  • Compared imputation methods: no rounding, post-imputation rounding, truncated normal regression, and predictive mean matching.
  • Estimated marginal means and associations, comparing to complete data.

Main Results:

  • Imputation without rounding performed well on the raw scale.
  • Rounding and truncated normal regression led to biased marginal means with moderate/severe skew.
  • Predictive mean matching also resulted in biased marginal means.
  • All methods yielded similar estimates for the association between variables.

Conclusions:

  • Restricting imputed values in multiple imputation can bias marginal mean estimates for skewed, limited-range data.
  • Careful selection of imputation methods is crucial for accurate statistical inference.
  • Findings are relevant for handling missing psychiatric data in health studies.