I have data on individuals, consisting of an individual identifier column, starting date column and length date column. This indicates an individual being in financial distress for a specific period of days.
ID STARTDATE LENGTH_DAYS 1 2016-05-01 12 1 2016-03-12 7 2 2016-01-08 33 3 2016-06-01 24 3 2016-07-23 12 ... ... ...
I have about 100.000 observations such as these and I'm aiming to predict the length of days in financial distress, when entering financial distress, based on some variables. As this is basically time-to-event data, I wanted to model this using a survival model. However, for 1000 to 2000 observations (the more recent observations) I do not yet know how may days the individual will be in financial distress. Which lets me doubt whether a survival model will be appropriate as only between 1% and 2% of my observations are censored. As far as I know, the presence of censoring is the main reason for using survival models.
Due to the low amount of censoring I thought maybe a panel data approach may therefore be a more suitable and easier approach. From what I've read implementing a survival model seems more involved than using standard regression techniques. Furthermore, if I discard the last month of data from my dataset (which is about 3%), less than 1% of the data is censored. Although discarding this data seems like bad practice, I'm wondering how much information is actually discarded as I have 18 months of data.
So my questions basically are:
- Is there a rule of thumb as to how much censoring needs to be present for time-to-event data to use survival models?
- If so, what would the downsides of a (panel data) approach using standard regression techniques be? (Except for discarding possible valuable information)