The nature of facilitation and interference in the multilingual language system: insights from treatment in a case of trilingual aphasia

The mandate for evidence-based practice has prompted careful consideration of the weight of the scientific evidence regarding the therapeutic value of various clinical treatments. In the field of aphasia, a large number of single-subject research studies have been conducted, providing clinical outcome data that are potentially useful for clinicians and researchers; however, it has been difficult to discern the relative potency of these treatments in a standardized manner. In this paper we describe an approach to quantify treatment outcomes for single-subject research studies using effect sizes. These values provide a means to compare treatment outcomes within and between individuals, as well as to compare the relative strength of various treatments. Effect sizes also can be aggregated in order to conduct meta-analyses of specific treatment approaches. Consideration is given to optimizing research designs and providing adequate data so that the value of treatment research is maximized.

Frequentist methods are available for comparison of a patient's test score (or score difference) to a control or normative sample; these methods also provide a point estimate of the percentage of the population that would obtain a more extreme score (or score difference) and, for some problems, an accompanying interval estimate (i.e., confidence limits) on this percentage. In the present paper we develop a Bayesian approach to these problems. Despite the very different approaches, the Bayesian and frequentist methods yield equivalent point and interval estimates when (a) a case's score is compared to that of a control sample, and (b) when the raw (i.e., unstandardized) difference between a case's scores on two tasks are compared to the differences in controls. In contrast, the two approaches differ with regard to point estimates of the abnormality of the difference between a case's standardized scores. The Bayesian method for standardized differences has the advantages that (a) it can directly evaluate the probability that a control will obtain a more extreme difference score, (b) it appropriately incorporates error in estimating the standard deviations of the tasks from which the patient's difference score is derived, and (c) it provides a credible interval for the abnormality of the difference between an individual's standardized scores; this latter problem has failed to succumb to frequentist methods. Computer programs that implement the Bayesian methods are described and made available.