Modeling the heterogeneity in risk of progression to Alzheimer's disease across cognitive profiles in mild cognitive impairment

Heterogeneity in risk of conversion to Alzheimer's disease (AD) among individuals with mild cognitive impairment (MCI) is well known. Novel statistical methods that are based on partially ordered set (poset) models can be used to create models that provide detailed and accurate information about performance with specific cognitive functions. This approach allows for the study of direct links between specific cognitive functions and risk of conversion to AD from MCI. It also allows for further delineation of multi-domain amnestic MCI, in relation to specific non-amnestic cognitive deficits, and the modeling of a range of episodic memory functioning levels. From the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, conversion at 24 months of 268 MCI subjects was analyzed. It was found that 101 of those subjects (37.7%) converted to AD within that time frame. Poset models were then used to classify cognitive performance for MCI subjects. Respective observed conversion rates to AD were calculated for various cognitive subgroups, and by APOE e4 allele status. These rates were then compared across subgroups. The observed conversion rate for MCI subjects with a relatively lower functioning with a high level of episodic memory at baseline was 61.2%. In MCI subjects who additionally also had relatively lower perceptual motor speed functioning and at least one APOE e4 allele, the conversion rate was 84.2%. In contrast, the observed conversion rate was 9.8% for MCI subjects with a relatively higher episodic memory functioning level and no APOE e4 allele. Relatively lower functioning with cognitive flexibility and perceptual motor speed by itself also appears to be associated with higher conversion rates. Among MCI subjects, specific baseline cognitive profiles that were derived through poset modeling methods, are clearly associated with differential rates of conversion to AD. More precise delineation of MCI by such cognitive functioning profiles, including notions such as multidomain amnestic MCI, can help in gaining further insight into how heterogeneity arises in outcomes. Poset-based modeling methods may be useful for providing more precise classification of cognitive subgroups among MCI for imaging and genetics studies, and for developing more efficient and focused cognitive test batteries

A linear compensatory counterpart to and generalization of the DINA model

In applying cognitive diagnosis models (CDM), it is important to define how mastery of the multiple skills required by a test item combine to determine item performance. The two commonly used assumptions as to a combination rule lead to the distinction between conjunctive versus compensatory models. The present work develops methods to investigate the appropriateness of the conjunctive combination rule used in many CDMs such as the DINA model. First, a linear compensatory model is developed that is a counterpart model to DINA, differing only in the skill-combination rule. This new model is referred to as the "Linear Compensatory" (LC) model. Then, a new generalized model, the "Quasi-Compensatory" (or "QUIC") model, is described. Bayesian methods are developed for estimation of the three models. Two simulation studies are conducted investigating how accurately the parameters of the models can be recovered using the estimation methods. Study 1 assumes larger values for guessing parameters to represent a multiple-choice test and Study 2 simulates an open-ended test by setting a smaller value for the guessing parameters. Finally, in Study 3 the models were applied to the well-known mixed fraction subtraction data collected by K. Tatsuoka. Results of the three studies showed that while it is usually critical to use a model that assumes the correct combination rule, the QUIC model can distinguish conjunctive from compensatory items and fit both types of items and tests well, thus overcoming the need to specify the nature of the combination rule in advance