Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study

Figures & data

Figure 1 Example simulation scenarios for (A) less time points vs (B) more time points; (C) smaller sample size per time point vs (D) larger sample size per time point.

Figure 2 Empiracle power to detect a relative 34% reduction in outcome, where mean pre-intervention incidence is 3.5%: by the number of time points and mean sample size per time point: (A) slope change (B) step change.

Figure 3 Empirical power in the case study^a (stratified by effect size) to detect an intervention resulting in (A) a slope change or (B) step change.

Note: ^aAssuming a mean pre-intervention outcome of 3.5%, mid-time series intervention, and 28 total time points.

Figure 4 Empirical power in the case study^a (stratified by intervention location) to detect an intervention resulting in (A) a slope change or (B) step change.

Note: ^aAssuming a mean pre-intervention outcome of 3.5%, 28 total time points, and an average 34% relative reduction post intervention (early/late slope changes were identical to midway scenario, therefore, achieved a different effect size).

Figure 5 Percentage bias in estimates of intervention impact in the case study^a: stratified by the nature of impact.

Note: ^aAssuming a mean pre-intervention outcome of 3.5%, total of 28 time points, and an average 34% relative reduction post intervention.