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

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)...
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...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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...

You might also read

Related Articles

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

Sort by
Same author

A Pilot Randomized Controlled Trial Examining the Impact of Therapy Dog Visitation on Mood, Anxiety, and Depression in Patients Hospitalized for the Treatment of Mental Illness.

Healthcare (Basel, Switzerland)·2026
Same author

Service Dog Training Interventions for Veterans with Post-Traumatic Stress: Examining Gender-Based Differences in Psychosocial Outcomes.

Healthcare (Basel, Switzerland)·2026
Same author

Exploring the dynamic biopsychosocial health needs of middle-aged adults living alone: a qualitative study.

International journal of qualitative studies on health and well-being·2026
Same author

Veterans with Service, Emotional Support, and Companion Dogs: Examining the Relationship Between Demographics, Health Characteristics, and Intensity of Human-Dog Relationships.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Feasibility of Recruiting Psychiatrically Hospitalized Adults for a Randomized Controlled Trial of an Animal-Assisted Intervention.

Healthcare (Basel, Switzerland)·2026
Same author

A pilot randomized controlled trial to examine the impact of a therapy dog intervention on loneliness in adult patients hospitalized in a psychiatric unit.

Frontiers in psychiatry·2025

Related Experiment Video

Updated: May 22, 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

Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.

Heesook Son1, Erika Friedmann, Sue A Thomas

  • 1School of Nursing, George Washington University, DC, USA.

Nursing Research
|May 4, 2012
PubMed
Summary

Pattern mixture models help nursing researchers analyze longitudinal data by assessing informative missingness. This method prevents bias in linear mixed models (LMMs) when data are not missing completely at random.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

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 22, 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

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

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:

  • Nursing Research
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Traditional longitudinal analyses are limited to complete cases, risking bias from missing data.
  • Missing data can arise from participant withdrawal or omission, leading to biased results.
  • Imputing missing data may introduce bias toward the null if data are not missing completely at random.

Purpose of the Study:

  • To demonstrate applying pattern mixture models for evaluating missing data informativeness in longitudinal studies.
  • To illustrate adjusting linear mixed model (LMM) analyses for informative missingness using SPSS.
  • To provide a practical example for nursing researchers handling incomplete longitudinal datasets.

Main Methods:

  • Utilized the Patients' and Families' Psychological Response to Home Automated External Defibrillator Trial dataset.
  • Applied pattern mixture models to assess the informativeness of missing data patterns.
  • Incorporated missing data patterns as fixed effects in linear mixed models (LMMs).

Main Results:

  • Assessing the informativeness of missing data is crucial for preventing withdrawal, omission, and null bias in longitudinal LMMs.
  • Pattern mixture models effectively evaluate whether missing data influence study outcomes.
  • Demonstrated a method to control for the impact of informative missingness on longitudinal data.

Conclusions:

  • Pattern mixture models are valuable for identifying and addressing informative missingness in longitudinal nursing research.
  • Incorporating missing data patterns as fixed effects in LMMs allows for bias assessment and control.
  • This approach enhances the validity of longitudinal study findings when dealing with incomplete data.