“…1 % CRTFASTGEEPWR ( alpha =0.05 , m =% str ( J (6 ,1 ,2) ) , corr_type = ED , alpha0 = 0.03 , 2 R0 = 0.8 , i nt e r ve n t io n _ ef f e ct _ t y pe = AVE , delta = -0.511 , period_effect_type = LIN , 0 0 0 4 4 4 4 4 4 4 4 0 0 4 4 4 4 4 4 4 0 0 , 8 0 0 0 0 4 4 4 4 4 4 4 4 4 0 0 4 4 4 4 4 4 0 , 9 0 0 0 0 0 4 4 4 4 4 4 4 4 4 4 0 0 4 4 4 4 4} 0 0 0 0 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 , 12 2 0 0 0 0 0 0 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 , 13 2 2 0 0 0 0 0 0 0 2 2 1 1 1 1 1 1 1 1 2 2 2 , 14 2 2 2 0 0 0 0 0 0 0 0 2 2 1 1 1 1 1 1 1 2 2 , 15 2 2 2 2 0 0 0 0 0 0 0 0 0 2 2 1 1 1 1 1 1 2 , 16 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 2 2 1 1 1 1 1}) ) ; The third example illustrates power calculation for a cross-sectional SW-CRT to improve pre-operative decision-making, by the use of a patient-driven question prompt list intervention [Taylor et al, 2017, Schwarze et al, 2020]. In the third example, 480 patients enrolled across six periods are clustered within 40 surgeons who are randomized to transition from control (blue cells) to intervention condition (green cells) at one of five randomly assigned sequences (8 surgeons per sequence; Figure 2).…”