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Living beyond our "means": new methods for comparing distributions

¹Department of Health and Social Behavior, Harvard School of Public Health, Boston, MA 02115, USA.

American Journal of Epidemiology

|January 8, 1998

Summary

New projection methods offer robust tools for comparing continuous distributions. These techniques, including the projection plot and spline, enhance statistical analysis in epidemiology.

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Area of Science:

Statistics
Epidemiology
Data Analysis

Background:

Comparing continuous distributions is crucial in statistical and epidemiologic research.
Existing methods may lack the power or accessibility for routine data analysis.

Purpose of the Study:

To introduce novel projection methods for describing and testing differences between continuous distributions.
To provide accessible tools for routine epidemiologic data analysis.

Main Methods:

Projection plot: Visualizes differences between quantiles.
Projection spline: Summarizes deviations and classifies differences (shape, spread, location).
Iter-1 test: A global test for distribution differences, more powerful than chi-square or Kolmogorov-Smirnov tests.

Main Results:

Projection methods effectively describe and test differences in distributions.
The projection spline classifies the nature of distribution differences.
The iter-1 test demonstrates superior power compared to traditional tests in simulations.

Conclusions:

Projection methods offer an accessible and powerful approach to comparing continuous distributions.
These methods can enhance epidemiologic practice by facilitating routine full distribution comparisons.

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