Abstract
PurposeThis work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT).MethodsA baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model.ResultsIRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis.ConclusionA combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-014-1315-5) contains supplementary material, which is available to authorized users.
Highlights
The Alzheimer’s Disease Assessment Scale—cognitive subscale (ADAS-cog) has served as the de-facto standard for the assessment of cognition in clinical trials in mild to moderate Alzheimer’s Disease (AD) patients for the past 20 years [1]
item response theory (IRT) uses a hidden or latent variable approach to describe the unobservable quantity an assessment aims to measure [5]. In this manuscript we focus on the ADAS-cog and the interpretation of each task of the assessment as a surrogate measure for the patient’s cognitive disability
The 95% prediction intervals of cognitive disability in the MCI and in the mild AD population as estimated from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database are shown in Fig. 4
Summary
The Alzheimer’s Disease Assessment Scale—cognitive subscale (ADAS-cog) has served as the de-facto standard for the assessment of cognition in clinical trials in mild to moderate Alzheimer’s Disease (AD) patients for the past 20 years [1]. Recent failures of promising drug candidates developed for the treatment of AD, and a movement towards earlier forms of the disease for future studies, have led some to question the sensitivity of the ADAS-cog. The ADAS-cog score has proven to be non-uniformly sensitive to measuring cognitive decline in AD across mild to moderate patients and has been recognized as less appropriate for earlier stages of the disease [2]. Fragmentation of cognitive assessments into specific measures for specific stages of AD reduces the comparability of study outcomes for things like comparative effectiveness research, and diminishes the possibility to acquire useful knowledge across the lifespan of an individual patient, or across trials. There is the risk of lower assessment completion rate, improper application by raters due to lack of familiarity with the test, and improper scoring for missing data due to the lack of standardized scoring algorithms across organizations for the new instrument
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