  The delivery and assessment of quality health care is complex with many
interacting and interdependent components. In terms of research design and
statistical analysis, this complexity and interdependency makes it difficult to
assess the true impact of interventions designed to improve patient health care
outcomes. Interrupted time series (ITS) is a quasi-experimental design
developed for inferring the effectiveness of a health policy intervention while
accounting for temporal dependence within a single system or unit. Current
standardized 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 assess
statistical power for study planning purposes in this context. To address this
limitation we propose the `Robust Multiple ITS' (R-MITS) model, appropriate for
multi-unit ITS data, that allows for inference regarding the estimation of a
global change point across units in the presence of a potentially lagged (or
anticipatory) treatment effect. Under the R-MITS model, one can formally test
for the existence of a change point and estimate the time delay between the
formal intervention implementation and the over-all-unit intervention effect.
We conducted empirical simulation studies to assess the type one error rate of
the testing procedure, power for detecting specified change-point alternatives,
and accuracy of the proposed estimating methodology. R-MITS is illustrated by
analyzing patient satisfaction data from a hospital that implemented and
evaluated a new care delivery model in multiple units.
