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Review and Preview

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

Review and Preview

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...

How Data are Classified: Numerical Data

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...

z Scores and Unusual Values

z Scores and Unusual Values

The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...

Introduction to z Scores

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...

Introduction to z Scores

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...

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Related Experiment Video

Updated: Jun 20, 2026

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Published on: August 28, 2021

Numerical scoring for the Classic BILAG index.

Lynne Cresswell¹, Chee-Seng Yee, Vernon Farewell

¹MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Rheumatology (Oxford, England)

|September 26, 2009

Summary

A new numerical scoring system was developed for the Classic BILAG index in Systemic Lupus Erythematosus (SLE) patients. The proposed scheme (A=12, B=5, C=1, D=0, E=0) offers improved disease activity assessment.

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Area of Science:

Rheumatology
Immunology
Clinical Trials

Background:

The Classic BILAG index is a tool for assessing Systemic Lupus Erythematosus (SLE) disease activity.
Current scoring methods may not accurately reflect disease severity or guide treatment decisions.

Purpose of the Study:

To develop and validate an improved additive numerical scoring scheme for the Classic BILAG index.
To enhance the accuracy of SLE disease activity assessment.

Main Methods:

Multi-center cross-sectional study involving 369 SLE patients and 1510 assessments.
Logistic regression modeling to correlate BILAG scores with increased therapy as a marker of active disease.
Receiver operating characteristic (ROC) curve analysis to compare different scoring schemes.

Main Results:

The existing BILAG coding (A=9, B=3, C=1, D/E=0) showed poor data fit.
Regression analysis identified potential schemes, with A=12, B=6, C=1, D/E=0 initially favored.
Validation suggested A=12, B=5, C=1, D/E=0 as a more appropriate scheme.

Conclusions:

A refined additive numerical scoring scheme for the Classic BILAG index is proposed: A=12, B=5, C=1, D=0, E=0.
This new scheme aims to provide a more accurate and practical assessment of SLE disease activity based on treatment decisions.