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Errors in ADAS-cog administration and scoring may undermine clinical trials results
K Schafer1, S De Santi, L S Schneider
1University of Southern California, San Diego, USA.
Current Alzheimer Research
|May 12, 2011
Summary
Most experienced raters made errors on the Alzheimer's Disease Assessment Scale - cognitive subscale (ADAS-cog). These ADAS-cog errors can impact clinical trial results by biasing outcomes and increasing score variability.
Area of Science:
- Neuroscience
- Clinical Trials
- Cognitive Assessment
Background:
- The Alzheimer's Disease Assessment Scale - cognitive subscale (ADAS-cog) is a standard measure in Alzheimer's disease (AD) clinical trials.
- Concerns exist that administration and scoring errors may obscure accurate treatment effect estimates.
- Formal examinations of ADAS-cog errors are limited.
Purpose of the Study:
- To investigate the frequency and impact of errors made by experienced raters during ADAS-cog administration and scoring.
- To determine if rater errors affect the reliability and validity of ADAS-cog scores in clinical trials.
Main Methods:
- Experienced raters for two AD clinical trials received standardized ADAS-cog administration training.
- Raters' scores from a model ADAS-cog administration were compared to established criterion scores.
- Error analysis focused on identifying types of errors and their correlation with overall scores.
Main Results:
- 80.6% of 72 raters made at least one error, with 18% making three or more.
- Common errors occurred in word finding difficulty (67%), word recognition (22%), and orientation (22%).
- Raters with errors, particularly in word finding, showed significantly different ADAS-cog means and increased score variance compared to those without errors.
Conclusions:
- Most experienced raters commit errors in ADAS-cog administration and scoring.
- These errors can introduce bias and increase variability in ADAS-cog scores, potentially affecting the detection of treatment effects in clinical trials.
- Standardized training and rigorous quality control are crucial for accurate ADAS-cog assessments.
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