Table 2.

Example of stacked right censoring and left truncation weighted data to allow prediction with time-covariate interactions

ID	Exposed	Start	Stop	Event	$\frac{\widehat{G}\left(t_j-\right)}{\widehat{G}\left(X_j-\right)}$	$\frac{\widehat{H}\left(t_j-\right)}{\widehat{H}\left(X_j-\right)}$	wi (tj)	Exposed × log(Stop)
1	1	0.6	3.8	1	1.0	1.0	1.0	1.3
2	0	2.7	3.8	1	1.0	1.0	1.0	0.0
2	0	3.8	5.0	1	1.0	1.0	1.0	0.0
2	0	5.0	6.8	1	1.0	1.0	1.0	0.0
3	1	0.2	3.8	1	1.0	1.0	1.0	1.3
3	1	3.8	5.0	1	1.0	1.0	1.0	1.6
4	1	1.2	3.8	0	1.0	1.0	1.0	1.3
4	1	3.8	5.0	0	1.0	1.0	1.0	1.6
4	1	5.0	5.1	0	1.0	1.0	1.0	1.6
5	0	3.0	3.8	0	1.0	1.0	1.0	0.0
5	0	3.8	5.0	0	1.0	1.0	1.0	0.0
5	0	5.0	6.5	0	1.0	1.0	1.0	0.0
6	1	1.4	2.3	2	1.0	1.0	1.0	0.8
6	1	2.3	3.8	2	1.0	1.3	1.3	1.3
6	1	3.8	5.0	2	1.0	1.3	1.3	1.6
6	1	5.0	6.8	2	0.3	1.3	0.4	1.9

Event takes value 1 for AIDS, 2 for SI switching, and 0 for censoring. $\frac{\widehat{G}\left(t_j-\right)}{\widehat{G}\left(X_j-\right)}$ is the right censoring weight, $\frac{\widehat{H}\left(t_j-\right)}{\widehat{H}\left(X_j-\right)}$ is the left truncation weight, and $w_i\left(t_j\right)=\frac{\widehat{G}\left(t_j-\right)\widehat{H}\left(t_j-\right)}{\widehat{G}\left(X_j-\right)\widehat{H}\left(X_j-\right)}$ is the inverse probability of censoring and left truncation weight.