Abstract :
Alzheimer’s disease (AD) is a complex disease process, so finding a single biomarker to track in
clinical trials has proven difficult. This paper describes and contrasts statistical methods that might be used with
biomarkers in clinical trials for AD, highlighting their differences, limitations and interpretations. The first
method is traditional regression, within which one dependent variable, the Best Empirically Supported Indicator
(BESI), must be identified. In this approach one biomarker (e.g., the ratio of tau to A 42 from CSF) is the
indicator for an individual’s disease status, and change in that status. The second approach is an exploratory
factor analysis (EFA) to consolidate a multitude of candidate dependent variables into a sample-dependent,
mathematically-optimized smaller set of ‘factors’. The third method is latent variable (LV) modeling of multiple
indicators of an entity (e.g., “disease burden”). The LV approach can yield a complex ‘dependent variable’, the
Best Measurement Model Indicator (BMMI). A measurement model represents an entity that several dependent
variables reflect or measure, and so can include many ‘dependent variables’, and estimate their relative
contributions to the underlying entity. The selection of a single BESI is an artifact of regression that limits the
investigator’s ability to utilize all relevant variables representing the entity of interest. EFA results in samplespecific
combination of biomarkers that might not generalize to a new sample – and fit of the EFA results cannot
be tested. Latent variable methods can be useful to construct powerful, efficient statistical models that optimally
combine diverse biomarkers into a single, multidimensional dependent variable that can generalize across
samples when they are theory-driven and not sample-dependent. This paper shows that EFA can work to
uncover underlying structure, but that it does not always yield solutions that ‘fit’ the data. It is not recommended
as a method to build BMMIs, which will be useful in establishing diagnostic criteria, creating and evaluating
benchmarks, and monitoring progression in clinical trials
