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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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Weibull Distribution
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Longitudinal Research

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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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 Cox...
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Variable selection for semiparametric mixed models in longitudinal studies.

Xiao Ni1, Daowen Zhang, Hao Helen Zhang

  • 1Discovery Analytics, GlaxoSmithKline, Five Moore Drive, Research Triangle Park, North Carolina 27709, USA.

Biometrics
|April 29, 2009
PubMed
Summary
This summary is machine-generated.

We introduce a novel double-penalized likelihood method for semiparametric mixed models, enhancing model selection and estimation in longitudinal data analysis. This approach offers efficient and valid inference, even with missing data, and simplifies computation.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Semiparametric mixed models are widely used for longitudinal data.
  • Existing methods for model selection and estimation can be inefficient or lack valid inference for missing data.
  • Simultaneous selection and estimation are crucial for complex models.

Purpose of the Study:

  • To propose a double-penalized likelihood approach for simultaneous model selection and estimation in semiparametric mixed models.
  • To provide a method with valid inference for data with missing at random.
  • To develop an efficient and computationally convenient procedure.

Main Methods:

  • A double-penalized likelihood approach combining roughness and nonconcave shrinkage penalties.
  • Reformulation into a linear mixed model framework for computational ease.
  • Derivation of frequentist and Bayesian variance estimation for parametric and nonparametric components.

Main Results:

  • The proposed method achieves simultaneous model selection and estimation.
  • It offers valid inference for longitudinal data with missing at random.
  • The approach is computationally efficient and can be implemented using existing software.

Conclusions:

  • The double-penalized likelihood method provides a robust and efficient tool for semiparametric mixed models.
  • It outperforms existing methods in terms of inference validity and efficiency.
  • The method is applicable to real-world data, as demonstrated in a lactation study.