Monte Carlo Simulation Procedure
Step 1:	Bootstrap the CHIS dataset with selection probabilities proportional to the sampling weights.
Step 2:	Perform the T-IR estimation procedure, simulating a value of ${\hat{N}}_{i1}^{\left(\mathcal{I}\right)}$ from (18) for each $X_i$, subsequently obtaining estimates $\left({\hat{\gamma }}_0,\hat{\mathit{\gamma }}\right)$.
Step 3:	Using $\left({\hat{\gamma }}_0,\hat{\mathit{\gamma }}\right)$ obtained in Step 2, perform the T-CFR estimation procedure, simulating a value of ${\hat{N}}_{i1}^{\left(\mathcal{M}\right)}$ from (19) for all $X_i$, subsequently obtaining estimates $\left({\hat{\delta }}_0,\hat{\mathit{\delta }}\right)$.
Step 4:	Repeat Steps 1–3 to obtain a total of $\mathcal{B}$ estimates of $\left({\gamma }_0,\mathit{\gamma }\right)$ and $\left({\delta }_0,\mathit{\delta }\right)$, which we denote $\left\{\left({\hat{\gamma }}_0^{\left(b\right)},{\hat{\mathit{\gamma }}}^{\left(b\right)}\right):b=1,\dots ,\mathcal{B}\right\}$ and $\left\{\left({\hat{\delta }}_0^{\left(b\right)},{\hat{\mathit{\delta }}}^{\left(b\right)}\right):b=1,\dots ,\mathcal{B}\right\}$, respectively.
Step 5:	For each set of bootstrap coefficient estimates $\left({\gamma }_0^{\left(b\right)},{\mathit{\gamma }}^{\left(b\right)},{\delta }_0^{\left(b\right)},{\mathit{\delta }}^{\left(b\right)}\right)$, $b=1,\dots ,\mathcal{B}$, estimate the T-IR and T-CFR for covariate setting $\mathit{z}$ using Eqs. (8), (17), respectively.