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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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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Regression analysis of longitudinal data with random change point.

Peng Zhang1, Xuerong Chen1, Jianguo Sun2

  • 1Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.

Statistical Methods in Medical Research
|February 24, 2024
PubMed
Summary

This study introduces a novel joint modeling approach for longitudinal data with random change points, allowing for subject-specific effects and covariate heterogeneity before and after the change point. The method demonstrates effectiveness in simulations and real-world COVID-19 data analysis.

Keywords:
Generalized linear mixed effect modeljoint modelinglongitudinal datarandom change point

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Existing regression methods for longitudinal data with change points often limit analysis to continuous responses.
  • Current approaches typically focus on change points affecting only the response or individual trajectory trends, not subject-specific variations.

Purpose of the Study:

  • To develop a new joint modeling approach for longitudinal data accommodating subject-specific random change points.
  • To address effect heterogeneity of covariates occurring before and after the change point.

Main Methods:

  • Combines a generalized linear mixed-effects model for longitudinal response with a random change point.
  • Integrates a log-linear regression model to handle the random change point.
  • Employs a maximum likelihood estimation procedure for inference.

Main Results:

  • The proposed method allows for subject-specific change points and varying covariate effects.
  • Asymptotic properties of the estimators, distinct from standard results, are established.
  • Simulation studies indicate the method's practical utility.

Conclusions:

  • The novel joint modeling approach effectively handles longitudinal data with random, subject-specific change points and covariate heterogeneity.
  • The method is validated through simulations and applied to COVID-19 data, showing practical applicability.