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Longitudinal Studies01:26

Longitudinal Studies

238
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

12.5K
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 Mean01:52

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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...
6.5K
Multiple Regression01:25

Multiple Regression

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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.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

285
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...
285
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

300
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.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
300

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Updated: Sep 10, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Mejora del gradiente de efectos mixtos para datos longitudinales de alta dimensión

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.

Scientific reports
|August 22, 2025
PubMed
Resumen

El análisis longitudinal de datos de alta dimensión es un desafío. El aumento de gradiente de efectos mixtos (MEGB) ofrece una mejor predicción y selección de características para conjuntos de datos complejos, superando los métodos existentes.

Palabras clave:
Mejora del gradienteDatos de alta dimensiónDatos longitudinalesModelo de efecto mixto

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Área de la Ciencia:

  • Estadísticas biológicas
  • Biología computacional
  • Modelado estadístico

Sus antecedentes:

  • Los datos longitudinales de alta dimensión presentan desafíos analíticos debido a correlaciones complejas dentro del sujeto y una alta relación predictor-observación.
  • Los métodos existentes luchan para modelar de manera efectiva estructuras de covarianza complejas y realizar una selección de características robustas en tales entornos.

Objetivo del estudio:

  • Introducir el aumento de gradiente de efectos mixtos (MEGB), un nuevo paquete R diseñado para analizar datos longitudinales de alta dimensión.
  • Proporcionar un marco unificado que integre el aumento del gradiente con el modelado de efectos mixtos para un análisis sólido de los datos de las medidas repetidas.

Principales métodos:

  • MEGB sinergiza el aumento del gradiente con el modelado de efectos mixtos para tener en cuenta tanto los efectos fijos a nivel de población como la variabilidad aleatoria específica del sujeto.
  • El enfoque se adapta a estructuras de covarianza complejas y utiliza la regularización del aumento del gradiente para la selección y predicción de características.
  • El paquete R MEGB se desarrolla para su aplicación práctica.

Principales resultados:

  • Las simulaciones demostraron que MEGB logró un error cuadrado medio (MSE) del 35-76% menor en comparación con los bosques aleatorios de efecto mixto (MERF) y REEMForest.
  • MEGB mantuvo tasas de positivos verdaderos del 55 al 70% para la selección de variables en entornos de dimensiones ultra altas (p = 2000).
  • La aplicación a los datos de ARN de plasma libre de células maternas identificó 9 transcripciones placentarias clave que influyen en la dinámica del ARN fetal.

Conclusiones:

  • MEGB ofrece una solución potente y eficaz para el análisis de datos longitudinales de alta dimensión, superando a los métodos actuales de vanguardia.
  • Las transcripciones placentarias identificadas proporcionan información sobre la dinámica del ARN fetal durante el embarazo, mostrando la utilidad práctica de MEGB en la investigación biológica.