Testing for Efficacy in Single-Subject Trials with Intervention Analysis

Single subject or n-of-1 research designs have been widely used to evaluate treatment interventions. Many statistical procedures such as split-middle trend lines, regression trend line, Shewart-chart trend line, binomial tests, randomization tests and Tryon C-statistics have been used to analyze single-subject data, but they fail to control Type I error due to serially-dependent time-series observations. The interrupted time series analysis maintains Type I error but assumes that the intervention effect to be a linear trend change from baseline. In this paper, we consider an improved intervention analysis model (Box and Tiao, 1975) for dynamic characteristics of an intervention effect in a short series of single-subject data. The maximum likelihood estimates are derived and a hypothesis testing procedure is proposed. The method is illustrated with a real clinical trial on constraint induced language therapy for aphasia patients.

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