# Group sequential design using average hazard ratio under non-proportional hazards ```r gs_update_ahr( x = NULL, alpha = NULL, ustime = NULL, lstime = NULL, event_tbl = NULL ) ``` ## Arguments - `x`: A design created by either `gs_design_ahr()` or `gs_power_ahr()`. - `alpha`: Type I error for the updated design. - `ustime`: Default is NULL in which case upper bound spending time is determined by timing. Otherwise, this should be a vector of length k (total number of analyses) with the spending time at each analysis. - `lstime`: Default is NULL in which case lower bound spending time is determined by timing. Otherwise, this should be a vector of length k (total number of analyses) with the spending time at each analysis. - `event_tbl`: A data frame with two columns: (1) analysis and (2) event, which represents the events observed at each analysis per piecewise interval. This can be defined via the `pw_observed_event()` function or manually entered. For example, consider a scenario with two intervals in the piecewise model: the first interval lasts 6 months with a hazard ratio (HR) of 1, and the second interval follows with an HR of 0.6. The data frame `event_tbl = data.frame(analysis = c(1, 1, 2, 2), event = c(30, 100, 30,200))` indicates that 30 events were observed during the delayed effect period, 130 events were observed at the IA, and 230 events were observed at the FA. ## Returns A list with input parameters, enrollment rate, failure rate, analysis, and bound. ## Examples ```r library(gsDesign) library(gsDesign2) alpha <- 0.025 beta <- 0.1 ratio <- 1 # Enrollment enroll_rate <- define_enroll_rate( duration = c(2, 2, 10), rate = (1:3) / 3) # Failure and dropout fail_rate <- define_fail_rate( duration = c(3, Inf), fail_rate = log(2) / 9, hr = c(1, 0.6), dropout_rate = .0001) # IA and FA analysis time analysis_time <- c(20, 36) # Randomization ratio ratio <- 1 # ------------------------------------------------- # # Two-sided asymmetric design, # beta-spending with non-binding lower bound # ------------------------------------------------- # # Original design x <- gs_design_ahr( enroll_rate = enroll_rate, fail_rate = fail_rate, alpha = alpha, beta = beta, ratio = ratio, info_scale = "h0_info", info_frac = NULL, analysis_time = c(20, 36), upper = gs_spending_bound, upar = list(sf = sfLDOF, total_spend = alpha), test_upper = TRUE, lower = gs_spending_bound, lpar = list(sf = sfLDOF, total_spend = beta), test_lower = c(TRUE, FALSE), binding = FALSE) |> to_integer() planned_event_ia <- x$analysis$event[1] planned_event_fa <- x$analysis$event[2] # Updated design with 190 events observed at IA, # where 50 events observed during the delayed effect. # IA spending = observed events / final planned events, the remaining alpha will be allocated to FA. gs_update_ahr( x = x, ustime = c(190 / planned_event_fa, 1), lstime = c(190 / planned_event_fa, 1), event_tbl = data.frame(analysis = c(1, 1), event = c(50, 140))) # Updated design with 190 events observed at IA, and 300 events observed at FA, # where 50 events observed during the delayed effect. # IA spending = observed events / final planned events, the remaining alpha will be allocated to FA. gs_update_ahr( x = x, ustime = c(190 / planned_event_fa, 1), lstime = c(190 / planned_event_fa, 1), event_tbl = data.frame(analysis = c(1, 1, 2, 2), event = c(50, 140, 50, 250))) # Updated design with 190 events observed at IA, and 300 events observed at FA, # where 50 events observed during the delayed effect. # IA spending = minimal of planned and actual information fraction spending gs_update_ahr( x = x, ustime = c(min(190, planned_event_ia) / planned_event_fa, 1), lstime = c(min(190, planned_event_ia) / planned_event_fa, 1), event_tbl = data.frame(analysis = c(1, 1, 2, 2), event = c(50, 140, 50, 250))) # Alpha is updated to 0.05 gs_update_ahr(x = x, alpha = 0.05) ```

gs_update_ahr function