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ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
Published on: July 3, 2017
Notes on the statistics of growth standards
1Medical Research Council Clinical Research Centre, Northwick Park, Harrow.
Annals of Human Biology
|January 1, 1974
Summary
Constructing population standards for measurements like height and menarche presents challenges. Extreme centiles derived from Gaussian distributions show significant imprecision, necessitating alternative estimation methods.
Area of Science:
- Biostatistics
- Human Growth and Development
- Population Health
Background:
- Establishing accurate population standards for biological measurements is crucial for health assessments.
- Traditional methods for estimating extreme centiles, such as the 3rd, can be imprecise when using Gaussian distributions.
Purpose of the Study:
- To discuss the challenges in constructing population standards for measurements like height and age-related milestones (e.g., menarche).
- To evaluate the imprecision of extreme centile estimation from Gaussian distributions.
- To compare different estimation methodologies for population standards.
Main Methods:
- Discussion of parametric estimation using Gaussian distributions.
- Comparison with non-parametric estimation methods, commonly used for weight.
- Exploration of transformation to Gaussianity for parametric estimation.
- Analysis of regression standards for population comparisons.
Main Results:
- Extreme centiles (e.g., 3rd) estimated from Gaussian distributions exhibit considerable imprecision.
- Non-parametric estimation and transformation to Gaussianity followed by parametric estimation are compared.
- Regression standards offer a method for comparing individuals to relevant population subgroups.
Conclusions:
- The imprecision of extreme centile estimation from Gaussian distributions is a significant problem.
- Alternative methods like non-parametric estimation and regression standards should be considered for robust population standard construction.
- Velocity of growth standards could potentially be framed using regression-based approaches.
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