An algorithm for the selection of proper group intervals for histograms representing clinical laboratory data.

Area of Science:

• Clinical Chemistry
• Biostatistics
• Medical Laboratory Science

Background:

• Establishing definitive reference ranges for clinical chemistry analytes is complex.
• Existing statistical methods lack standardized approaches for data grouping and analysis.
• Accurate normal value determination is crucial for effective patient diagnosis and monitoring.

Purpose of the Study:

• To propose a novel, objective method for grouping laboratory data.
• To determine the optimal class interval for constructing frequency histograms.
• To enhance the accuracy and reduce arbitrariness in defining clinical chemistry normal values.

Main Methods:

• A new data grouping technique is presented, utilizing "regrouping" and "sign reversal" concepts.
• The method aims to remove subjectivity in selecting class intervals for data analysis.
• A general computer algorithm was employed to validate the proposed technique.

Main Results:

• The proposed method facilitates the selection of the "best" class interval for frequency histograms.
• Objective data grouping enhances the information derived from laboratory test results.
• Arbitrariness in defining normal ranges is significantly reduced.

Conclusions:

• This novel data grouping method offers a more precise approach to determining normal values in clinical chemistry.
• The technique improves the reliability of laboratory data interpretation.
• Further application of this method can standardize reference range determination.