Comparative effectiveness research in rehabilitation medicine requires the development and validation of clinically meaningful and scientifically rigorous measurements of patient states and theories that explain and predict outcomes of intervention. Patient traits are latent (unobservable) variables that can be measured only by inference from observations of surrogate manifest (observable) variables. In the behavioral sciences, latent variables are analogous to intensive physical variables such as temperature and manifest variables are analogous to extensive physical variables such as distance. Although only one variable at a time can be measured, the variable can have a multidimensional structure that must be understood in order to explain disagreements among different measures of the same variable. The use of Rasch theory to measure latent trait variables can be illustrated with a balance scale metaphor that has randomly added variability in the weights of the objects being measured. Knowledge of the distribution of the randomly added variability provides the theoretical structure for estimating measures from ordinal observation scores (e.g., performance measures or rating scales) using statistical inference. In rehabilitation medicine, the latent variable of primary interest is the patient's functional ability. Functional ability can be estimated from observations of surrogate performance measures (e.g., speed and accuracy) or self-report of the difficulty the patient experiences performing specific activities. A theoretical framework borrowed from project management, called the Activity Breakdown Structure (ABS), guides the choice of activities for assessment, based on the patient's value judgments, to make the observations clinically meaningful. In the case of low vision, the functional ability measure estimated from Rasch analysis of activity difficulty ratings was discovered to be a two-dimensional variable. The two visual function dimensions are independent of physical limitations and psychological state. To explain outcome measures (latent variable estimated from difficulty ratings), a theory must be developed that explicitly defines how latent variables are related to the observed manifest variables and to each other. The latent variables are categorized as primary variables, which in the case of low vision are the two visual function dimensions, and as effect modifiers, which in the case of low vision are other physical and psychological latent traits of the patients that can influence the outcome measures. Interventions give rise to latent intervention effect variables that can alter the latent primary variables or independently affect the outcome measures. The latent effect modifier variables, in turn, can alter the latent intervention effect variables. Once developed and validated, a theory of this form will predict the rehabilitation potential of individual patients, i.e., the probability of obtaining criterion outcome measures given the observed state of the patient and the choice of interventions.
|Original language||English (US)|
|Number of pages||18|
|Journal||Journal of applied measurement|
|State||Published - Dec 1 2010|
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