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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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.
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Central Limit Theorem

The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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.
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Sampling Soils in a Heterogeneous Research Plot
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Published on: January 7, 2019

An improved delta-centralization method for population stratification.

Prakash Gorroochurn1, Susan E Hodge, Gary A Heiman

  • 1Division of Statistical Genetics, Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA. pg2113@columbia.edu

Human Heredity
|July 23, 2011
PubMed
Summary
This summary is machine-generated.

The delta-centralization (DC) method for controlling population stratification (PS) has been criticized. An adjusted DC method is proposed that addresses issues found with Balding-Nichols simulated data, showing appropriate power.

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Area of Science:

  • Population genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Population stratification (PS) is a confounder in genetic association studies.
  • The delta-centralization (DC) method was developed to control for PS.
  • Recent criticism questioned the validity of the DC method using Balding-Nichols (BN) model simulations.

Purpose of the Study:

  • To address criticisms of the delta-centralization (DC) method for controlling population stratification (PS).
  • To explain why the DC method underestimates the PS parameter (δ) and inflates type I error rates with BN-simulated data.
  • To present an adjusted DC method with improved performance.

Main Methods:

  • Analysis of population stratification (PS) parameter (δ) under the Balding-Nichols (BN) model.
  • Simulation studies comparing the original and adjusted delta-centralization (DC) methods.
  • Evaluation of type I error rates and statistical power across various scenarios.

Main Results:

  • The delta-centralization (DC) method underestimates the population stratification (PS) parameter (δ) and inflates type I error rates when applied to Balding-Nichols (BN) simulated data.
  • A simple adjustment to the DC method was developed.
  • The adjusted DC method demonstrated appropriate statistical power across a range of scenarios.

Conclusions:

  • The conclusion that the delta-centralization (DC) method is invalid based on Balding-Nichols (BN) simulations is premature.
  • The adjusted DC method effectively controls for population stratification (PS) in simulations.
  • The adjusted DC method offers a robust approach for genetic association studies with potential population stratification.