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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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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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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Fast Univariate Inference for Longitudinal Functional Models.

Erjia Cui1, Andrew Leroux2, Ekaterina Smirnova3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, USA.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

We developed fast statistical methods for analyzing longitudinal functional data, suitable for both Gaussian and non-Gaussian outcomes. These novel approaches accurately analyze complex data, like Diffusion Tensor Imaging (DTI) and physical activity, offering computational feasibility.

Keywords:
DTIlongitudinal functional datamixed modelwearable devices

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Area of Science:

  • Statistics
  • Biostatistics
  • Functional Data Analysis

Background:

  • Longitudinal functional data analysis presents computational challenges.
  • Existing methods may not be feasible for high-dimensional or complex datasets.

Purpose of the Study:

  • To propose fast univariate inferential approaches for longitudinal Gaussian and non-Gaussian functional data.
  • To provide computationally efficient and accurate methods for analyzing complex functional data.

Main Methods:

  • Fitting massively univariate pointwise mixed effects models.
  • Applying smoothers along the functional domain.
  • Obtaining joint confidence bands using analytic approaches or bootstrapping.

Main Results:

  • Extensive simulations show accurate model fitting and inference.
  • The proposed methods are significantly faster than existing approaches.
  • The approach is computationally feasible for applications like physical activity data analysis.

Conclusions:

  • The proposed methods offer a fast and accurate solution for longitudinal functional data analysis.
  • The methods are applicable to diverse datasets, including Diffusion Tensor Imaging (DTI) and accelerometer data.
  • The R software implementation makes these methods accessible to analysts familiar with mixed effects models.