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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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Longitudinal Research02:20

Longitudinal Research

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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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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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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...
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Longitudinal multivariate normative comparisons.

Zheng Wang1, Yu Cheng1,2, Eric C Seaberg3

  • 1Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Statistics in Medicine
|December 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces new methods to accurately classify cognitive impairment using longitudinal data, crucial for tracking changes over time in studies like the Multicenter AIDS Cohort Study (MACS). These techniques effectively control statistical errors, improving diagnostic reliability.

Keywords:
cognitive impairmentfalse discovery ratefamily-wise error ratelongitudinal analysismultivariate mixed-effect model

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Psychometrics

Background:

  • Cognitive impairment assessment often relies on cross-sectional data, which may not capture disease progression.
  • Existing methods like multivariate normative comparisons (MNC) control for statistical errors but require adaptation for longitudinal data.
  • Repeated testing in longitudinal studies can inflate family-wise error rates, necessitating robust statistical approaches.

Purpose of the Study:

  • To develop and validate statistical procedures for classifying cognitive impairment using longitudinal data.
  • To adapt the cross-sectional multivariate normative comparisons (MNC) method for longitudinal application, controlling for family-wise error.
  • To address the challenges of multiple testing over repeated assessments in longitudinal cognitive studies.

Main Methods:

  • Development of longitudinal multivariate mixed-effects models for cognitive domain scores.
  • Adaptation of a multivariate test procedure for classifying impairment in longitudinal data with normal distributions.
  • Implementation of a permutation procedure to manage skewed data in longitudinal cognitive assessments.
  • Utilizing data from the Multicenter AIDS Cohort Study (MACS) neuropsychological substudy for illustration.

Main Results:

  • The proposed longitudinal MNC procedures effectively control family-wise error rates at a predetermined level.
  • Simulations demonstrated the reliability and accuracy of the developed classification methods.
  • The study successfully applied the new procedures to a real-world dataset, showcasing their practical utility.

Conclusions:

  • Longitudinal multivariate mixed-effects models provide a robust framework for classifying cognitive impairment.
  • The adapted MNC and permutation procedures offer reliable tools for analyzing longitudinal cognitive data while controlling for statistical errors.
  • These methods enhance the ability to accurately diagnose and monitor cognitive changes in longitudinal research, particularly in vulnerable populations.