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Related Concept Videos

Longitudinal Research02:20

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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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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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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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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Conditional modeling of longitudinal data with terminal event.

Shengchun Kong1, Bin Nan2, John D Kalbfleisch3

  • 1Gilead Sciences, Inc., Foster City, CA 94404.

Journal of the American Statistical Association
|March 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for longitudinal data with informative terminal events. The approach treats the event time as a covariate, offering clearer interpretation of its impact on the response variable.

Keywords:
Cox regressionEmpirical processMixed effects modelPseudo-maximum likelihood estimation

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Analyzing longitudinal data with informative terminal events is challenging.
  • Existing methods (joint modeling, marginal estimating equations) lack explicit interpretation of the terminal event's effect.
  • Right censoring complicates the analysis of terminal events.

Purpose of the Study:

  • To develop a novel statistical model for longitudinal data incorporating informative terminal events.
  • To provide a more straightforward interpretation of the terminal event's influence on the longitudinal response.
  • To propose a robust estimation method for regression parameters in this complex data structure.

Main Methods:

  • A two-stage semiparametric likelihood-based approach is proposed.
  • Stage 1: Estimate the conditional distribution of right-censored terminal event time.
  • Stage 2: Maximize the likelihood function for the longitudinal data, conditional on the terminal event and covariates.

Main Results:

  • The proposed method allows for explicit interpretation of the terminal event's effect.
  • Numerical simulations demonstrate the method's performance.
  • The approach was successfully applied to medical cost data for end-stage renal disease patients.

Conclusions:

  • The new conditional modeling approach offers a significant improvement for analyzing longitudinal data with informative terminal events.
  • The method provides interpretable results and is supported by desirable asymptotic properties.
  • This framework enhances understanding of disease progression and healthcare costs in chronic conditions.