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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)...
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...
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...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Related Experiment Video

Updated: Jul 14, 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

Handling Missing Data in Intensive Longitudinal Data with Mixed Missing Mechanisms.

Zhilin Wan1, Yue Liu1

  • 1Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China.

Multivariate Behavioral Research
|July 12, 2026
PubMed
Summary

Intensive longitudinal studies (ILS) often have missing data. The Kalman filter handles ignorable missingness well, but Bayesian models are better for non-ignorable missing data, though only a small amount is tolerable.

Keywords:
Bayesian selection modelsIntensive longitudinal dataKalman filterdynamic structural equation modelsmixed missing mechanisms

Related Experiment Videos

Last Updated: Jul 14, 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

Area of Science:

  • Psychological methods
  • Statistical modeling
  • Longitudinal data analysis

Background:

  • Intensive longitudinal studies (ILS) frequently encounter substantial missing data.
  • Missing data mechanisms in ILS can be complex, involving mixtures of MCAR, MAR, and MNAR.
  • Existing research offers limited guidance on handling missing data in ILS.

Purpose of the Study:

  • To evaluate the performance of the Kalman filter and Bayesian selection models for missing data in ILS.
  • To examine how varying levels of missingness and mixed missing mechanisms affect estimation.
  • To provide data-driven recommendations for missing data handling in ILS.

Main Methods:

  • Two simulation studies were conducted within a Discrete-time Structural Equation Modeling (DSEM) framework.
  • Evaluated the Kalman filter and Bayesian selection models under different missingness proportions and mechanisms (MCAR, MAR, MNAR).
  • Assessed performance across varying sample sizes and measurement occasions.

Main Results:

  • The Kalman filter demonstrated robust performance with up to 70% ignorable missingness (MCAR/MAR).
  • Estimation quality degraded significantly with increasing proportions of non-ignorable missingness (MNAR).
  • Correctly specified Bayesian selection models outperformed other methods, but tolerated only ~3% MNAR.

Conclusions:

  • The Kalman filter is suitable for ILS with ignorable missing data, even at high proportions.
  • Bayesian selection models are superior when non-ignorable missingness is present, but their applicability is limited by the low tolerable MNAR proportion.
  • Understanding missingness mechanisms is crucial for accurate estimation in ILS.