Each measure is associated with specific cognitive functions

Each measure is associated with specific cognitive functions MLN8237 that are involved in performing well on that measure, so that the ties between observed responses and functioning levels are clear. This approach is feasible because posets have essential statistical convergence properties such as assuring that a subject’s state is identified accurately with sufficient measurement, even in the presence of measures that are associated with multiple functions. Theoretically derived validation tools are available as well. Statistical theory and data-analytic frameworks for the poset approach have been established in Tatsuoka and Ferguson (2003) [7] and Tatsuoka (2002) [8]. In this paper, our goal is to demonstrate that posets can improve our understanding of MCI heterogeneity.

The modeling results in the development of states associated with profiles of cognitive functioning that summarize performance levels for each of the cognitive functions being tested by a given NP battery. Hence, a state can be viewed as similar to a diagnostic classification in which the diagnosis represents a particular pattern of cognitive strengths and weaknesses. States are ordered by comparing the associated performance levels for each of the functions included in the analysis. One state is considered greater than a second state if its associated performance level on at least one function is strictly higher than the performance level for the second state, and its performance levels for all other functions are at least as high.

However, posets are flexible in that it is not necessary that one state be greater than another, in other words, the states can be partially ordered. This arises when one state in comparison to another state has a higher performance level with respect to one function, while having a lower performance level with respect to another function. This enables models to reflect a complex range of responses from an NP battery. A probability distribution on the states is used to represent belief about which state best describes the cognitive capabilities of a subject. Bayes’ rule is used to obtain updated posterior probabilities of state membership once responses to measures are observed. This allows for a systematic manner in which Dacomitinib the information obtained from observing multiple measures can be combined for statistical classification. Two response distributions are estimated per NP measure, one representing the response tendencies of subjects who perform at a relatively high level on all functions associated with the particular measure and another for those subjects who do not. These distributions are used selleck products to weigh the relative likelihood of an observed response indicating that a subject has the associated higher level functioning.

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