Table 10.

Illustration of stabilized IPTW and IPCW definitions with multiple time points

Stabilized WeightsTime 1Time 2Time 3
IPTWNumeratoraP(X1=a1)P(X2=a2|X1=a1) ×P(X1=a1)P(X3=a3|X1=a1, X2=a2) ×P(X2=a2|X1=a1) ×P(X1=a1)
DenominatoraP(X1=a1| )P(X2=a2|X1=a1, ) ×P(X1=a1| )P(X3=a3|X1=a1, X2=a2, ) ×P(X2=a2|X1=a1, ) ×P(X1=a1| )
IPCWNumeratoraP(C1=0| )P(C2=0|C1=0, ) ×P(C1=0| )P(C3=0| C1=0, C2=0, ) × P(C2=0| C1=0, ) ×P(C1=0| )
DenominatoraP(C1=0| , )P(C2=0|C1=0, , ) ×P(C1=0| , )P(C3=0| C1=0, C2=0, , ) × P(C2=0| C1=0, , ) ×P(C1=0| , )
• X1, X2, and X3 are the exposure; a1, a2, and a3 are the values of exposure; and the confounder history (i.e., confounder values since baseline to this time point) is , , and at time points 1, 2, and 3. C1, C2, and C3 are the censoring indicators at time points 1, 2, and 3 for one subject. They are defined as 1 if right-censored by that time point, and 0 otherwise. , , and are the exposure history at time points 1, 2, and 3. IPTW, inverse probability treatment weight; P, probability of; IPCW, inverse probability censoring weight.

• a We can adjust for baseline covariates in all models.