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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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Crossover Experiments01:16

Crossover Experiments

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Comparing the Survival Analysis of Two or More Groups01:20

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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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Transient and Steady-state Response01:24

Transient and Steady-state Response

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In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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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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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Adaptive response-dependent two-phase designs: Some results on robustness and efficiency.

Ce Yang1, Liqun Diao2, Richard J Cook2

  • 1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Statistics in Medicine
|July 8, 2022
PubMed
Summary

This study refines adaptive two-phase sampling for biomarker studies in biobanks, optimizing subsampling for cost-effective analysis of expensive biomarkers like MMP-3 in psoriatic arthritis.

Keywords:
adaptive designefficiencyincomplete dataresponse-dependent samplingrobustnesssurrogate variable

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Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Epidemiology

Background:

  • Biobanks in large cohort studies store biospecimens for future biomarker research.
  • Two-phase sampling designs are used to manage costs by subsampling individuals for expensive biomarker assays.
  • Adaptive two-phase designs improve efficiency by using preliminary data to optimize subsequent sampling phases.

Purpose of the Study:

  • To refine the definition of optimal subsampling schemes in adaptive two-phase designs.
  • To extend adaptive two-phase designs to likelihood and conditional likelihood analyses.
  • To investigate efficiency and robustness for continuous biomarkers and explore applications in surrogate variable analysis.

Main Methods:

  • Refinement of optimal subsampling definitions within adaptive two-phase frameworks.
  • Application of adaptive two-phase designs to likelihood and conditional likelihood analyses.
  • Nonparametric estimation of nuisance covariate distributions at design and analysis stages.
  • Exploration of methods for surrogate variable problems and multi-stage phase II subsampling.

Main Results:

  • Demonstration of adaptive two-phase designs for efficient biomarker analysis under budget constraints.
  • Investigation into efficiency and robustness for continuous biomarkers with nonparametric covariate estimation.
  • Successful application to a psoriatic arthritis study assessing MMP-3 and joint damage.

Conclusions:

  • Adaptive two-phase designs offer a robust and efficient framework for biomarker studies in biobanks.
  • The refined methods accommodate various analytical approaches and complex sampling scenarios.
  • These approaches are valuable for cost-effective biomarker research, as illustrated by the psoriatic arthritis example.