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Related Concept Videos

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...
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...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

A dynamic approach for reconstructing missing longitudinal data using the linear increments model.

Odd O Aalen1, Nina Gunnes

  • 1Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, 0317 Oslo, Norway. o.o.aalen@medisin.uio.no

Biostatistics (Oxford, England)
|April 15, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a dynamic framework for analyzing longitudinal data with missing observations, reconstructing responses using autoregressive models for both monotone and nonmonotone missingness.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Missing observations are frequent in longitudinal studies, complicating data analysis.
  • Traditional methods may not adequately account for the temporal structure and influence of past data on current and future responses.

Purpose of the Study:

  • To propose a dynamic framework for modeling and analyzing longitudinal data with missing observations.
  • To reconstruct missing responses under various missingness patterns (monotone and nonmonotone).
  • To explore the application of these methods for estimating causal effects in the presence of time-dependent confounding.

Main Methods:

  • Utilizing an autoregressive model as a specific instance of the linear increments model.
  • Developing computational procedures for reconstructing missing data and estimating parameters.
  • Connecting the framework to survival analysis methods, such as the Aalen-Johansen estimator.

Main Results:

  • The proposed computational procedures are simple, easily applicable, and effective for reconstructing missing data.
  • The framework accommodates both monotone and nonmonotone missingness patterns.
  • Demonstrated application to quality of life data from a cancer clinical trial.

Conclusions:

  • The dynamic autoregressive framework provides a robust approach for handling missing data in longitudinal studies.
  • The methods are versatile, applicable to causal inference with time-dependent confounding, and have connections to survival analysis.
  • The approach is validated through analysis of real-world clinical trial data.