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Statistical properties of continuous composite scales and implications for drug development
Hong Liu-Seifert1, Scott Andersen1, Michael Case1
1a Lilly Research Laboratories , Lilly Corporate Center, Indianapolis , Indiana , USA.
Journal of Biopharmaceutical Statistics
|April 13, 2017
Summary
Composite measures, like the Integrated AD Rating Scale (iADRS), can better detect treatment effects than individual scales alone. Understanding these statistical properties aids clinical trial design.
Area of Science:
- Statistical analysis
- Clinical trial methodology
- Psychometrics
Background:
- Limited research exists on the statistical properties of composite measures formed by linear combinations of continuous scales.
- Assessing the relationship between composite measures and their components is crucial for understanding their performance in detecting treatment effects.
Purpose of the Study:
- To quantitatively assess the relationship between composite measures and their individual components in detecting treatment effects.
- To mathematically derive the treatment effect size of a continuous composite in relation to its components.
- To demonstrate the statistical properties of composite measures.
Main Methods:
- Developed mathematical derivations for the treatment effect size of continuous composites.
- Analyzed the relationship between composite measures and their components using data from Alzheimer's disease (AD) clinical studies.
- Utilized the Integrated AD Rating Scale (iADRS) and its components, ADAS-Cog and ADCS-iADL, as examples.
Main Results:
- The treatment effect size of a composite measure is consistently greater than the minimum effect size of its components.
- Under specific conditions (thresholds of component correlations and effect size ratios), a composite measure can outperform its individual components in detecting treatment effects.
- Empirical data from AD clinical trials supported the theoretical statistical properties of the composite measure.
Conclusions:
- Composite measures offer advantages over individual component scales in detecting treatment effects.
- Understanding the statistical properties of composites is vital for optimizing clinical trial design and developing new measurement tools.
- The findings have broad applicability across various therapeutic areas for scale and composite development.
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