fully recorded at the two intermediate visits. In contrast, patient 1 and 2 experience an event after 1.5 and 0.9 years, due to which they have no (i.e. patient 2) or only one (i.e. patient 1) mediator level recorded. Table 2 illustrate how the information of these three hypothetical individuals is encoded in a counting-process format.

  ------------ ------------------- -------- ----- ---------- ---------- ---------- ---------- ----------
   Individual   Following-up time   Status   $A$    $M_1$      $M_2$      $L_0$      $L_1$      $L_2$
                     (years)                                                                  
       1               1.5            1       1    $m_{11}$     $-$      $l_{01}$   $l_{11}$     $-$
       2               0.9            2       0      $-$        $-$      $l_{02}$     $-$        $-$
       3               3.0            0       1    $m_{13}$   $m_{23}$   $l_{03}$   $l_{13}$   $l_{23}$
  ------------ ------------------- -------- ----- ---------- ---------- ---------- ---------- ----------

  : Illustrating example: A toy dataset in standard short format. Here, $m_{ij}$ and $l_{ij}$ denote the mediator and longitudinal confounder level of individual $j$ recorded at visit $t_i$, respectively. The treatment $A$ is binary (0 vs. 1) and there are two competing events, coded as $1$ and $2$.[]{data-label="tab:ma"}

   Individual   Start   Stop   Status   $A$    $M_t$     $M_{t-1}$    $L_t$     $L_{t-1}$    $L_0$
  ------------ ------- ------ -------- ----- ---------- ----------- ---------- ----------- ----------
       1          0      1       0       1       0           0          0           0       $l_{01}$
       1          1     1.5      1       1    $m_{11}$       0       $l_{01}$       0       $l_{01}$
       