Abstract

It is common to assess disability of stroke patients using standardized scales, such as the Rankin Stroke Outcome Scale (RS) and the Barthel Index (BI). The Rankin Scale, which was designed for applications to stroke, is based on assessing directly the global conditions of a patient. The Barthel Index, which was designed for general applications, is based on a series of questions about the patient’s ability to carry out 10 basis activities of daily living. As both scales are commonly used, but few studies use both, translating between scales is important in gaining an overall understanding of the efficacy of alternative treatments, and in developing prognostic models that combine several data sets.

The objective of our analysis is to provide a tool for translating between BI and RS. Specifically, we estimate the conditional probability distributions of each given the other. Subjects consisted of 459 individuals who sustained a stroke and who were recruited for the Kansas City Stroke Study from 1995 to 1998. Patients were assessed with BI and RS measures 1, 3 and 6 months after stroke. In addition, we included data from the Framingham study, in the form of cross-classifying patients by RS and coarsely aggregated BI.

Our statistical estimation approach is motivated by several goals: (a) overcoming the difficulty presented by the fact that our two sources report data at different resolutions; (b) smoothing the empirical counts to provide estimates of probabilities in regions of the table that are sparsely population; (c) avoiding estimates that would conflict with medical knowledge about the relationship between the two measures and (d) estimating the relationship between RS and BI at three months after the stroke, while borrowing strength from measurements made at one and six months. We address these issues via a Bayesian analysis combining data augmentation and constrained semiparametric inference.

Our results provide the basis for (a) comparing and integrating the results of clinical trials using different measures, and (b) integrating clinical trials results into comprehensive decision model for the assessment of long term implications and cost-effectiveness of stroke prevention and acute treatment interventions. In addition, our results indicate that the degree of agreement between the two measures is less strong than commonly reported, and emphasize the importance of trial designs that include multiple assessments of outcome.

Disciplines

Categorical Data Analysis | Clinical Trials | Disease Modeling | Epidemiology | Longitudinal Data Analysis and Time Series | Numerical Analysis and Computation