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The delivery and assessment of quality health care is complex with manyinteracting and interdependent components. In terms of research design andstatistical analysis, this complexity and interdependency makes it difficult toassess the true impact of interventions designed to improve patient health careoutcomes. Interrupted time series (ITS) is a quasi-experimental designdeveloped for inferring the effectiveness of a health policy intervention whileaccounting for temporal dependence within a single system or unit. Currentstandardized ITS methods do not simultaneously analyze data for several units,nor are there methods to test for the existence of a change point and to assessstatistical power for study planning purposes in this context. To address thislimitation we propose the `Robust Multiple ITS' (R-MITS) model, appropriate formulti-unit ITS data, that allows for inference regarding the estimation of aglobal change point across units in the presence of a potentially lagged (oranticipatory) treatment effect. Under the R-MITS model, one can formally testfor the existence of a change point and estimate the time delay between theformal intervention implementation and the over-all-unit intervention effect.We conducted empirical simulation studies to assess the type one error rate ofthe testing procedure, power for detecting specified change-point alternatives,and accuracy of the proposed estimating methodology. R-MITS is illustrated byanalyzing patient satisfaction data from a hospital that implemented andevaluated a new care delivery model in multiple units.

Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations