fix COPD example

Author: NightlordTW

COPD-NMA.qmd

@@ -95,14 +95,40 @@ nma_consistency <- nma(
 # Extract DIC
 dic_consistency <- dic(nma_consistency)
 
-# Extract tau (mean)
+# Extract tau (posterior mean)
 tau_consistency <- summary(nma_consistency)$summary %>%
   as.data.frame() %>%
   filter(parameter == "tau") %>%
   pull(mean)
 ```
 
-We find the following estimate for tau, the between-study standard deviation: `r round(tau_consistency, 3)`. The DIC for this model is `r round(dic_consistency$DIC, 1)`.
+We find the following estimate for tau, the between-study standard deviation: `r round(tau_consistency, 3)`. The DIC for this model is `r round(dic_consistency$dic, 1)`.
 
+## Assessing Inconsistency
+To assess local inconsistency, we apply the node-splitting approach to each comparison with both direct and indirect evidence.
 
+```{r}
+#| echo: true
+#| message: false
+#| warning: false
+nma_inconsistency <- nma(
+  net,
+  consistency = "ume",
+  trt_effects = "random",
+  prior_trt = normal(scale = 10),
+  prior_het = half_normal(scale = 5),
+  refresh = 0
+)
+
+# Extract DIC
+dic_inconsistency <- dic(nma_inconsistency)
+
+# Extract tau (posterior mean)
+tau_inconsistency <- summary(nma_inconsistency)$summary %>%
+  as.data.frame() %>%
+  filter(parameter == "tau") %>%
+  pull(mean)
+```
+
+We find the following estimate for tau, the between-study standard deviation: `r round(tau_inconsistency, 3)`. The DIC for this model is `r round(dic_inconsistency$dic, 1)`.