Hello--
I am trying to set up the data for an event history/survival/hazard analysis. I want to implement a multiple failure model with time varying predictors. The data are 6 assessments spaced one month apart. For the time varying predictors, information is gathered for the past 30 days. For the outcome event, it is captured on a daily level. I know that I can collapse the daily level information into 6 one month time periods (e.g., did event happen in the past 30 days: yes/no), but I wanted to take advantage of all of the information available (for example, I would like to be able to capture events occurring at day 5 and at day 20, but in the earlier setup this would count as one event occurring in the first 30 day period). I know that to incorporate time varying covariates, I would set up the data with 6 records per person (one per time), but I am confused how to indicate the occurrence of the event (when the event is a multiple failures). Without time varying predictors,!
I could set up my data as one row, with one variable representing time to each failure (t1 to t6) similar to example 3.2 in the Stata FAQ: analysis of multiple failure-time survival data). However, the FAQ does not provide examples of multiple failures with time varying covariates. Page 139 of the ST manual provides a nice example using -stgen nf=nfailures()- to create a new outcome variable which will handle multiple failures. However, this example does not have time-varying covariates (there are X1 and X2 predictors, but it does not appear that they are meant to represent variable X at time 1 and variable X at time 2). To sum up, I want to know how to set up my dataset to handle multiple failures with time varying predictors. Also, I am not sure how to code my outcome. In the case of recording any failure in the first 30 days as an event occurring in time 1, I would simply have a dichotomous failure variable at each of the 6 time points; however, I would prefer to !
have the outcome modeled continuously. Any help would be greatly app
reciated. Thanks, in advance, for considering this question.
Greg Homish
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