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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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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.
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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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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...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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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...
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Related Experiment Video

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A tractable method to account for high-dimensional nonignorable missing data in intensive longitudinal data.

Chengbo Yuan1, Donald Hedeker2, Robin Mermelstein3

  • 1Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA.

Statistics in Medicine
|May 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new, computationally feasible method called nonlinear indexes of local sensitivity to nonignorability (NISNI) for analyzing complex missing data in intensive longitudinal data (ILD). NISNI efficiently assesses potential bias from nonignorable missingness without computationally intensive modeling.

Keywords:
linear mixed effects modelmissing datanonignorabilitynonlinear sensitivity indexsensitivity analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Intensive longitudinal data (ILD) often exhibits complex, non-monotonic missing data patterns.
  • Sensitivity analysis for nonignorable missingness in ILD is computationally challenging due to high-dimensional integrals and large data volumes.
  • Existing methods struggle with the unique features of ILD, hindering accurate analysis.

Purpose of the Study:

  • To develop a computationally feasible method for sensitivity analysis of nonignorable missingness in ILD.
  • To address the limitations of existing methods in handling complex missing data patterns and large datasets.
  • To introduce nonlinear indexes of local sensitivity to nonignorability (NISNI) as a novel solution.

Main Methods:

  • Utilized linear mixed effects models for outcomes and covariates.
  • Employed Markov multinomial models to capture complex missing data patterns and mechanisms.
  • Developed NISNI using a second-order Taylor series approximation of the likelihood under nonignorability.
  • Derived closed-form expressions for NISNI, enabling efficient computation.

Main Results:

  • The NISNI method provides a computationally feasible approach to sensitivity analysis for nonignorable missingness in ILD.
  • The method can simultaneously handle missing outcomes and covariates, common in ILD.
  • NISNI can detect U-shaped impacts of nonignorability near the missing at random model without fitting alternative models or evaluating complex integrals.
  • Performance was validated using simulated and real-world ecological momentary assessment data.

Conclusions:

  • NISNI offers a practical and efficient tool for assessing the impact of nonignorable missing data in ILD.
  • The method overcomes computational barriers associated with traditional approaches.
  • This advancement facilitates more robust statistical inference from complex longitudinal studies.