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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sampling Methods: Overview01:06

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Study Design in Statistics01:15

Study Design in Statistics

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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
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Stratified Sampling Method01:16

Stratified Sampling Method

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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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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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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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Optimal multiwave sampling for regression modeling in two-phase designs.

Tong Chen1, Thomas Lumley1

  • 1Department of Statistics, University of Auckland, Auckland, New Zealand.

Statistics in Medicine
|October 5, 2020
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Summary
This summary is machine-generated.

This study introduces a multiwave sampling design to efficiently estimate regression parameters in two-phase studies. Using informative priors and influence functions, this method approximates optimal designs for improved statistical analysis.

Keywords:
Neyman allocationdesign-based estimatorsinfluence functionoptimal designprior

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Area of Science:

  • Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Two-phase designs efficiently analyze cohort data by measuring additional variables on a subsample.
  • The primary goal is to optimize subsample selection for efficient regression parameter estimation.
  • Achieving an optimal design is crucial for maximizing the efficiency of estimates.

Purpose of the Study:

  • To propose a multiwave sampling design that approximates the optimal design for two-phase studies.
  • To enhance the efficiency of design-based estimators for regression parameters.
  • To investigate the use of informative priors for deriving optimal wave-1 sampling probabilities.

Main Methods:

  • Utilizing influence functions to compute optimal sampling allocations.
  • Employing informative priors on regression parameters for initial sampling probabilities.
  • Iteratively updating priors with posterior distributions from previous waves.
  • Applying generalized raking for the final statistical analysis.

Main Results:

  • A two-wave sampling design with informative priors yields highly efficient estimations.
  • The proposed method closely approximates the underlying optimal design.
  • Informative priors significantly improve design efficiency compared to prespecified probabilities.

Conclusions:

  • The multiwave sampling design effectively approximates optimal designs in two-phase studies.
  • The use of informative priors is key to achieving high estimation efficiency.
  • This approach offers a practical method for optimizing complex cohort study designs.