Auto-generated report from BCEA"
In BCEA: Bayesian Cost Effectiveness Analysis

options(scipen = 999)

align <- 
  if (rmd_params$ext == "pdf") {
    "center"
  } else {
    "default"
  }

# check whether Info-rank should also be computed & shown
eval_IR <- 
  if (!is.null(rmd_params$psa_sims)) {
    TRUE
  } else {
    FALSE}

Cost-effectiveness analysis

The cost-effectiveness analysis is based on the maximisation of the expected utility, defined as the monetary net benefit $nb_t=ke_t-c_t$. Here $t$ indicates one of the interventions (treatments) being assessed, while $(e,c)$ indicate the relevant measures of effectiveness and cost. For each intervention, the expected utility is computed as $\mathcal{NB}t=k\mbox{E}[e_t]-\mbox{E}[c_t]$. When comparing two interventions (say, $t=1$ vs $t=0$), or using a pairwise comparison, we can determine the ``best'' alternative by considering the difference in the expected utilities $\mbox{EIB}=\mathcal{NB}_1-\mathcal{NB}_0$. This can also be expressed in terms of the _population effectiveness and cost differentials $\mbox{EIB}=k\mbox{E}[\Delta_e]-\mbox{E}[\Delta_c]$, where $\Delta_e=\mbox{E}[e\mid\bm\theta_1]-\mbox{E}[e\mid\bm\theta_0]$ and $\Delta_c=\mbox{E}[c\mid\bm\theta_1]-\mbox{E}[c\mid\bm\theta_0]$ are the average effectiveness and cost, as function of the relevant model parameters $\bm\theta=(\bm\theta_0,\bm\theta_1)$.

This sub-section presents a summary table reporting basic economic results as well as the optimal decision, given the selected willingness-to-pay threshold $k=$r rmd_params$wtp. The table below presents a summary of the optimal decision, as well as the values of the Expected Incremental Benefit $\mbox{EIB}=k\mbox{E}[\Delta_e]-\mbox{E}[\Delta_c]$, Cost-Effectiveness Acceptability Curve $\mbox{CEAC}=\Pr(k\Delta_e-\Delta_c)$ and Incremental Cost-Effectiveness Ratio $\mbox{ICER}=\displaystyle\frac{\mbox{E}[\Delta_c]}{\mbox{E}[\Delta_e]}$, for the set willingness-to-pay value.