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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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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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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A moving blocks empirical likelihood method for longitudinal data.

Jin Qiu1, Lang Wu2

  • 1School of Mathematics and Statistics, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, 310018, P. R. China.

Biometrics
|May 14, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new moving blocks empirical likelihood method for analyzing longitudinal data. It improves inference efficiency by properly modeling serial correlations in repeated measurements.

Keywords:
Empirical likelihoodGeneral estimating equationLongitudinal dataNonparametric methodSerial correlation

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Area of Science:

  • Statistics
  • Biostatistics

Background:

  • Longitudinal data analysis requires accounting for serial correlations within subjects.
  • Ignoring these correlations can lead to inefficient statistical inference, especially with many repeated measurements.

Purpose of the Study:

  • To propose a novel nonparametric method for modeling serial correlations in longitudinal data.
  • To develop a moving blocks empirical likelihood approach for general estimating equations.

Main Methods:

  • A moving blocks empirical likelihood method is proposed.
  • Asymptotic results are derived under sequential limits.
  • The method is compared to existing empirical likelihood techniques via simulations.

Main Results:

  • The proposed moving blocks empirical likelihood method demonstrates improved performance.
  • Simulation studies show its effectiveness in finite sample situations.
  • The method is applied to a real-world AIDS longitudinal study.

Conclusions:

  • The moving blocks empirical likelihood method offers a robust approach for longitudinal data analysis.
  • It provides more efficient inference when serial correlations are present.
  • This method enhances the analysis of complex panel data structures.