Dynamic prediction of repeated events data based on landmarking model: application to colorectal liver metastases data

Table 3 Summary of data generated in the presence of a terminal event

Simulation parameters					True proportion of event numbers				Censored proportion	Kendall’s tau
u _i	λ ₀₁	λ ₀₂	λ _{0 D}	λ _c	No events	1	2	terminal	Censored proportion	(t_i1, t_i2 − t_i1)	(t_i1, t_iD)	(t_i2 − t_i1, t_D)
1	1	1	0.3	0.5	27.3	27.2	19.5	26.0	34.1	−0.0003	0.002	0.001
1	1	2	0.3	0.5	27.3	17.1	29.5	26.0	34.1	−0.0003	0.002	0.001
Γ(0.5,0.5)	1	1	0.3	0.5	52.7	14.4	11.8	21.1	35.2	0.501	0.500	0.499
Γ(0.5,0.5)	1	2	0.3	0.5	52.7	10.3	16.0	21.1	35.2	0.501	0.500	0.499
1	1	1	0.3	2	27.2	27.2	19.6	26.9	77.9	−0.001	0.0003	0.002
1	1	2	0.3	2	27.2	17.4	29.5	26.9	77.7	−0.001	0.001	0.004
Γ(0.5,0.5)	1	1	0.3	2	52.3	14.7	12.2	20.9	78.6	0.499	0.498	0.497
Γ(0.5,0.5)	1	2	0.3	2	52.2	10.8	16.2	20.9	78.5	0.497	0.495	0.495

ISSN: 1471-2288