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Related Concept Videos

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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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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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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 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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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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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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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Functional principal component analysis for longitudinal data with informative dropout.

Haolun Shi1, Jianghu Dong1,2, Liangliang Wang1

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.

Statistics in Medicine
|November 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces informatively missing functional principal component analysis (imFunPCA) to accurately analyze longitudinal biomarker data with missing values. imFunPCA improves estimation by incorporating missing data, outperforming conventional methods in simulations.

Keywords:
filtration ratesfunctional data analysisinformative missingkidney glomerular likelihoodorthonormal empirical basis functions

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies often face informatively missing biomarker data due to patient dropout.
  • Conventional functional principal component analysis (fPCA) incorrectly assumes missing data is random, leading to biased estimations.
  • Bias in mean function and covariance surface estimation affects the accuracy of functional principal components.

Purpose of the Study:

  • To develop a novel method, informatively missing functional principal component analysis (imFunPCA), to address informative missingness in longitudinal data.
  • To provide a robust approach for estimating functional principal components when data is not missing completely at random.
  • To improve the analysis of longitudinal biomarker trajectories.

Main Methods:

  • imFunPCA computes functional principal components using the data's likelihood, incorporating both observed and missing data.
  • A regression-based orthogonal approximation method is used to decompose the latent stochastic process.
  • The approach utilizes orthonormal empirical basis functions for decomposition.

Main Results:

  • Simulation studies demonstrate imFunPCA's superior performance compared to conventional methods under informative missingness.
  • The method effectively incorporates information from missing data points, reducing estimation bias.
  • Application to kidney transplant patient data shows practical utility.

Conclusions:

  • imFunPCA offers a statistically sound and accurate approach for analyzing longitudinal data with informative missingness.
  • The method enhances the reliability of functional principal component estimation in real-world scenarios.
  • This technique is valuable for biomarker analysis in clinical and public health research.