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Abstract
Interrupted time series (ITS) is a strong quasi-experimental research design, which
is increasingly applied to estimate the effects of health services and policy interventions.
We describe and illustrate two methods for estimating confidence intervals (CIs) around
absolute and relative changes in outcomes calculated from segmented regression parameter
estimates.
We used multivariate delta and bootstrapping methods (BMs) to construct CIs around
relative changes in level and trend, and around absolute changes in outcome based
on segmented linear regression analyses of time series data corrected for autocorrelated
errors.
Using previously published time series data, we estimated CIs around the effect of
prescription alerts for interacting medications with warfarin on the rate of prescriptions
per 10,000 warfarin users per month. Both the multivariate delta method (MDM) and
the BM produced similar results.
BM is preferred for calculating CIs of relative changes in outcomes of time series
studies, because it does not require large sample sizes when parameter estimates are
obtained correctly from the model. Caution is needed when sample size is small.