Furthermore, denote the censoring indicator variable as (delta _{i}=I_{(X_{i} leq C_{i})}phantom {dot {i}!}), denote the ordered observed T i 's as t (1)
We used multiple multivariate imputation, using all variables included in any analyses, the censoring indicator and the log of survival time for Cox models, to impute missing values for those variables with some missing data [17].
Below, we have substituted the Weibull density and survivor functions into the likelihood function:
as the censoring indicator.
Δ i denotes the censoring indicator Δ i = I (= T i ), where I is the indicator function.
If all participants had entered each wave on the same date, only one censoring indicator per MONICA wave would be visible.
The observed survival time is defined by Y i = min{ T i, C i} and the censoring indicator is δ i = I (T i ≤ C i).
28 29 30 We included all the candidate predictor variables listed above in the multiple imputation models, along with the log of survival time and the censoring indicator.
In particular, Let T i be the event time for subject i, C i be the censoring time for subject i, and δ i = 1{ T i < C i } be the censoring indicator.
The observations are Y ij = min(T ij, C ij) and the censoring indicator Δij = I(T ij ≤ C ij) is one if the observation j is a failure and 0 if it is a censoring.
Survival data for the i-th patient are denoted as follows: t i stands for the event time, c i for the censoring time and δ i for the censoring indicator (δ i =1 if t i ≤ c i and δ i =0 if t i > c i ).
