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Related Concept Videos

Experimental Designs01:16

Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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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

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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.
On...
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Investigating immediacy in multiple-phase-change single-case experimental designs using a Bayesian unknown

Prathiba Natesan Batley1, Tom Minka2, Larry Vernon Hedges3

  • 1University of North Texas, 1155 Union Circle #311335, Denton, TX, 76203, USA. pnbatley@gmail.com.

Behavior Research Methods
|February 28, 2020
PubMed
Summary

This study introduces a new variational Bayesian (VB) method to quantify treatment effect immediacy in single-case experimental designs (SCEDs). The VB approach accurately identifies phase changes, outperforming previous methods for robust evidence in SCED research.

Keywords:
ABAB designsBayesianMCMCSimulationSingle case experimental designs

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Area of Science:

  • Behavioral Science
  • Statistical Modeling
  • Psychometrics

Background:

  • Immediacy of treatment effect is crucial for robust evidence in single-case experimental designs (SCEDs).
  • Existing inferential statistical tools for quantifying immediacy are limited, with few exceptions like Natesan and Hedges (2017).

Purpose of the Study:

  • To investigate and quantify immediacy in SCEDs by treating phase change points as unknown.
  • To extend existing methods to handle multiple-phase-change designs (e.g., ABAB).

Main Methods:

  • Developed a variational Bayesian (VB) unknown change-point model.
  • Utilized VB methods as an alternative to Markov Chain Monte Carlo (MCMC) for improved efficiency with multiple change points.
  • Employed combined and individual probabilities of change point estimation to assess algorithm accuracy.

Main Results:

  • The VB method demonstrated high accuracy in recovering change points, even with short time series.
  • VB significantly reduced computation time compared to MCMC across all time-series lengths.
  • The algorithm was successfully illustrated using 13 real-world datasets.

Conclusions:

  • The proposed VB unknown change-point model offers an accurate and efficient tool for quantifying immediacy in SCEDs.
  • Bayesian and VB estimation methods provide significant advantages for analyzing SCED data, particularly in complex designs.