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Related Concept Videos

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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Column Efficiency: Rate Theory01:12

Column Efficiency: Rate Theory

The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
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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)...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

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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

Computationally efficient marginal models for clustered recurrent event data.

Dandan Liu1, Douglas E Schaubel, John D Kalbfleisch

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, Tennessee 37232, USA. dandanl@gmail.com

Biometrics
|October 1, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing recurrent health events in large patient databases. The method simplifies complex data, improving computational efficiency for health services research.

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Epidemiology

Background:

  • Large observational databases are crucial for health services utilization studies.
  • Recurrent events in these databases present computational challenges.
  • Existing methods may struggle with the complexity of recurrent event data.

Purpose of the Study:

  • To propose a flexible statistical model for analyzing recurrent event data in large observational databases.
  • To address computational difficulties associated with recurrent events and terminal events.
  • To provide a method applicable to health services research and disease registries.

Main Methods:

  • A possibly stratified marginal proportional rates model with a piecewise-constant baseline event rate is proposed.
  • The model accounts for both the presence and absence of a terminal event.
  • Large-sample distributions for estimators are derived, and simulation studies are conducted.

Main Results:

  • The proposed model effectively handles recurrent event data and reduces computational burden.
  • Simulation studies demonstrate the model's robustness, even under misspecification.
  • Guidelines for interval selection are provided and validated through numerical studies.

Conclusions:

  • The new statistical procedure is computationally feasible and implementable in standard software (SAS, R).
  • This approach offers a valuable tool for analyzing recurrent events in health services research, as shown with end-stage renal disease patient data.
  • The method enhances the utility of large observational databases for studying complex health outcomes.