Table 5.

Comparison of the analytical approaches to adjust for time-dependent covariate from simulation-II (one baseline covariate, one time-dependent covariate and time-dependent treatment exposure) of 1, 000 datasets, each containing 2, 500 subjects followed for up to 10 time-intervals.

Approach	Bias	SD	se	Coverage Probability
TD-Cox§	0.000	0.164	0.162	0.952
Sequential Cox#, †	0.271	0.189	0.184	0.688
Modified Sequential Cox*, @	−0.022	0.231	0.234	0.961
MSCM±, ‡	−0.001	0.163	0.162	0.952

TD-Cox, Cox model with time-dependent exposure; MSCM, Marginal structural Cox model.

^§The baseline covariate L₀ and time-dependent covariate L_m are included.

^#Adjusts for L₀ and L̃_m.

^†In the stabilized IPCW model, the numerator model adjusts for A_m and L₀, while the denominator model adjusts for A_m, L₀ and L̃_m via Aalen’s additive model.

^*Adjusts for baseline covariates L₀, lagged values of A_m, the time-dependent confounder L⃗_m, and lagged values of L⃗_m.

^@For the stabilized IPCWs, the numerator model adjusts for A_m and baseline variable L₀, while the denominator model adjusts for L₀, A_m, L⃗_m and lagged values of L⃗_m via Aalen’s additive regression.

^±Adjusts for only L₀.

^‡For the stabilized IPTWs, the numerator model adjusts for the time index, L₀ and lagged values of A_m, while the denominator model additionally adjusts for current and lagged values of L_m to predict future treatment status via pooled logistic models.