Interrupted Time-Series Analysis with Brief Single-Subject Data

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Abstract

Assessing change with short time-series data is difficult because visual inference is unreliable with such data, and current statistical procedures cannot control Type I error because they underestimate positive autocorrelation. This article describes these problems and shows how they can be solved with a new interrupted time-series analysis procedure (ITSACORR) that uses a more accurate estimate of autocorrelation. Monte Carlo analyses show that, with short series, ITSACORR provides better control of Type I error than all previous procedures and has acceptable power. Clinical examples also show that ITSACORR is easy to use and functions well with real data.

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