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Related Concept Videos

Dementia01:30

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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Introduction to Statistics01:17

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The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Identifying Statistically Significant Differences: The F-Test01:14

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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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Statistical Methodology for Multiclass Classifications: Applications to Dementia.

Hong Li1, Sue Leurgans2, Jordan Elm1

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Journal of Alzheimer'S Disease : JAD
|February 12, 2019
PubMed
Summary
This summary is machine-generated.

New statistical methods improve Alzheimer's disease (AD) classification by combining biomarkers. These approaches accurately distinguish between no cognitive impairment, mild cognitive impairment, and dementia stages.

Keywords:
Alzheimer’s diseasediagnostic accuracygeneralized additive modelmulticlass classificationoptimal cutoff pointvolume under the surface

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Area of Science:

  • Neurology
  • Biostatistics
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) presents a significant global health and economic challenge.
  • Accurate classification of AD, from no cognitive impairment to dementia, is crucial for early intervention.
  • Current biomarkers lack the precision to differentiate between the multiple ordered stages of AD.

Purpose of the Study:

  • To develop advanced statistical methods for accurate AD classification using multiple biomarkers.
  • To enhance the evaluation of individual and combined biomarkers for diagnostic purposes.
  • To provide guidance on selecting appropriate statistical approaches for graded disease classifications.

Main Methods:

  • Proposed a forward addition procedure using an additive logistic regression model for ordered categories.
  • Extended the Neyman-Pearson Lemma to establish optimal cutoff points for three disease categories.
  • Validated methods via simulation and applied them to the Religious Orders Study data, comparing with existing techniques.

Main Results:

  • The proposed methods effectively combine cross-sectional biomarkers to improve diagnostic accuracy for AD.
  • The novel approaches facilitate the evaluation of biomarkers in graded classification settings.
  • Comparative analysis demonstrated the performance of the new methods against established techniques like ordinal logistic regression and CART.

Conclusions:

  • The developed statistical methods offer a robust framework for classifying Alzheimer's disease into distinct cognitive stages.
  • These approaches enable more precise utilization of biomarkers for diagnosis and disease progression assessment.
  • The study provides valuable insights for selecting and applying statistical tools in AD biomarker research.