Apply a design rule to paired replicate output

Turns the output of run_paired_scenario into the decisions of one design, post hoc. The efficacy look is only checked if the trial was not stopped at the futility look. A missing test statistic never crosses a boundary (as in apply_GSD_to_trial).

Usage

apply_design_rule(raw, rule)

Arguments

raw

The raw element returned by run_paired_scenario.

rule

A list with type ("D1" to "D5") and the rule's parameters: crit_final (D1); crit_eff, crit_fin (D2-D5); kappa (D3); z_fut (D4, D5); mw_t_star (D5).

Value

A data frame with one row per replicate and columns decision ("Stop for efficacy", "Stop for futility", "Successful at final" or "Unsuccessful at final"), success, early_fut, early_eff, sample_size and duration. Rows for failed replicates (non-missing error) are NA.

Details

D1

Fixed design: success iff Z_fin_LRT > crit_final.

D2

Efficacy look then final: "Stop for efficacy" if Z_eff_LRT > crit_eff, otherwise the final analysis with Z_fin_LRT > crit_fin.

D3

D2 plus PP futility: "Stop for futility" if PP_val < kappa. Stops with an error if PP_val is NA for any replicate that did not fail (e.g. output from compute_PP = FALSE).

D4

D2 plus Z futility: "Stop for futility" if Z_int_LRT < z_fut.

D5

As D4 with the modestly weighted statistics Z_int_MW_t<t>, Z_eff_MW_t<t>, Z_fin_MW_t<t> for t = mw_t_star.

D1, D2, D4 and D5 do not use PP_val, so they also work on output from run_paired_scenario(compute_PP = FALSE).

See also

run_paired_scenario, summarize_grid_by_kappa