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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

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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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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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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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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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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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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Mixed effect gradient boosting for high-dimensional longitudinal data.

Oyebayo Ridwan Olaniran1,2, Saidat Fehintola Olaniran3, Jeza Allohibi4

  • 1Department of Statistics, Faculty of Physical Sciences, University of Ilorin, Ilorin, Kwara State, PMB 1515, Nigeria. olaniran.or@unilorin.edu.ng.

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High-dimensional longitudinal data analysis is challenging. Mixed-Effect Gradient Boosting (MEGB) offers improved prediction and feature selection for complex datasets, outperforming existing methods.

Keywords:
Gradient BoostingHigh-dimensional DataLongitudinal DataMixed Effect Model

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Statistical Modeling

Background:

  • High-dimensional longitudinal data present analytical challenges due to complex within-subject correlations and a high predictor-to-observation ratio.
  • Existing methods struggle to effectively model intricate covariance structures and perform robust feature selection in such settings.

Purpose of the Study:

  • To introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package designed to analyze high-dimensional longitudinal data.
  • To provide a unified framework that integrates gradient boosting with mixed-effects modeling for robust analysis of repeated measures data.

Main Methods:

  • MEGB synergizes gradient boosting with mixed-effects modeling to account for both population-level fixed effects and subject-specific random variability.
  • The approach accommodates complex covariance structures and utilizes gradient boosting's regularization for feature selection and prediction.
  • The R package MEGB is developed for practical implementation.

Main Results:

  • Simulations demonstrated that MEGB achieved 35-76% lower mean squared error (MSE) compared to Mixed-Effect Random Forests (MERF) and REEMForest.
  • MEGB maintained 55-70% true positive rates for variable selection in ultra-high-dimensional settings (p=2000).
  • Application to maternal cell-free plasma RNA data identified 9 key placental transcripts influencing fetal RNA dynamics.

Conclusions:

  • MEGB offers a powerful and effective solution for analyzing high-dimensional longitudinal data, outperforming current state-of-the-art methods.
  • The identified placental transcripts provide insights into fetal RNA dynamics during pregnancy, showcasing MEGB's practical utility in biological research.