improve narrative

Author: NightlordTW

inst/app/modules/mod_narrative.R

@@ -41,7 +41,7 @@ mod_narrative_server <- function(
         )
       } else {
         sprintf(
-          "This design simulates a two-arm trial to %s, using a one-sided significance level of %s.",
+          "This design simulates a two-arm trial to %s. The threshold γ is calibrated to control the Type-I error at %s.",
           margin_txt, fmt_pct(alpha())
         )
       }
@@ -57,35 +57,28 @@ mod_narrative_server <- function(
       # 4. Prior description
       prior_type <- switch(
         prior(),
-        "flat" = "a non-informative flat prior (Beta(1,1))",
-        "power" = sprintf(
-          "a power prior (discount a₀ = %.0f%%) incorporating %s/%s historical responders and a baseline Beta prior (%.1f, %.1f)",
-          100 * prior_args()$a0,
-          fmt_int(prior_args()$y_0),
-          fmt_int(prior_args()$n_0),
-          prior_args()$a_base %||% 1,
-          prior_args()$b_base %||% 1
-        )
+        "flat" = "a non-informative flat prior for the response rate in each arm",
+        "power" = {
+          eff_n0 <- prior_args()$a0 * prior_args()$n_0
+          sprintf(
+            "a power prior incorporating %s/%s historical responders. The historical data is down-weighted using a factor of %.0f%%, resulting in an effective contribution equivalent to %.1f patients",
+            fmt_int(prior_args()$y_0),
+            fmt_int(prior_args()$n_0),
+            100 * prior_args()$a0,
+            eff_n0
+          )
+        }
       )
 
-      # 5. Decision logic
-      decision_txt <- if (decision_mode() == "gamma") {
-        sprintf("The trial is declared successful if the posterior probability that the true treatment effect exceeds Δ is at least γ = %s.", fmt_pct(gamma()))
-      } else {
-        sprintf("γ is calibrated to achieve a target Type-I error of %s.", fmt_pct(alpha()))
-      }
-
       # 6. Final narrative
       HTML(sprintf(
         "<p>%s</p>
-         <p>%s</p>
          <p>%s</p>
          <p>The analysis uses %s.</p>
          <p>Posterior probabilities are computed using %s draws per simulated trial, across %s simulated trials.</p>
          <p>Seed = %s (for reproducibility).</p>",
         design_txt,
         arm_txt,
-        decision_txt,
         prior_type,
         fmt_int(ndraws()), fmt_int(B()),
         fmt_int(seed())