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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...
Longitudinal Research02:20

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...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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A time-varying effect model for intensive longitudinal data.

Xianming Tan1, Mariya P Shiyko, Runze Li

  • 1The Methodology Center, The Pennsylvania State University, 204 East Calder Way, Suite 400, State College, PA 16801, USA. xzt1@psu.edu

Psychological Methods
|November 23, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces time-varying effect models (TVEMs) for analyzing intensive longitudinal data (ILD). TVEMs flexibly model how relationships between variables change over time, offering new insights into behavioral science research.

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Area of Science:

  • Behavioral Sciences
  • Psychological Processes
  • Longitudinal Data Analysis

Background:

  • Understanding temporal dynamics in human behavior is crucial.
  • Intensive longitudinal data (ILD) provide rich insights into behavioral changes over time.
  • Traditional methods often rely on restrictive assumptions about change.

Purpose of the Study:

  • Introduce time-varying effect models (TVEMs) for flexible analysis of ILD.
  • Address unique research questions concerning temporal associations in behavioral data.
  • Provide tools and examples for applying TVEMs in practice.

Main Methods:

  • Developed time-varying effect models (TVEMs) to analyze ILD.
  • Outlined model-estimation procedures for TVEMs.
  • Created a SAS macro for practical implementation of TVEMs.

Main Results:

  • Demonstrated TVEM utility with simulated data.
  • Applied TVEMs to a smoking-cessation study dataset.
  • Explored dynamic relationships between smoking urges and self-efficacy.

Conclusions:

  • TVEMs offer a flexible approach to modeling temporal changes in ILD.
  • The methods allow for detailed examination of evolving relationships between variables.
  • TVEMs enhance the analysis of complex behavioral processes over time.