y**3
发帖数: 267 | 1 For time-to-event survival model,we may do time -sliced classification
models to predict event(yes or no). For the whole data set, the claims9 in
my data) are censored at different times. I can build different models using
data censored at 1 year, 2 year, 3 years, 4 years etc. For example, for the
2 year model, I use all the claims IDs of age >=2 years and censor them at
2 years. Then use the two years data to find the target and predictors. I
think that is statistically how we do it. Then this is the typical
classification models. No time component is involved.Then we cant predict
into future using each model.
But a different suggestion is using the 2 years data to find predictors,
but for the target, using the whole life of claim to find the event occured
or not.In this way, all the target values for all the time-sliced models
such as 1 year model 2-year modle, 3-years model etc are gonna be the same.
Is this statistically right?
Thanks so much! | y**3
发帖数: 267 | | y**3
发帖数: 267 | 3 Anybody help? I know we should go with the first. but Need DaNou confirm.
Thanks | a*******g
发帖数: 80 | 4 没看明白 你的outcome 是什么 IV 是什么 timing of claims？
【在 y**3 的大作中提到】
: Anybody help? I know we should go with the first. but Need DaNou confirm.
: Thanks
| y**3
发帖数: 267 | 5 Ny outcome is time dependent event. FOr ex, some claims expereinced the
event at the first year, some at the 2nd year etc, some are censored | a*******g
发帖数: 80 | 6 是不是就可以理解成 time-to-claim，没claim的censor了。这样是time-to-event啊
【在 y**3 的大作中提到】
: Ny outcome is time dependent event. FOr ex, some claims expereinced the
: event at the first year, some at the 2nd year etc, some are censored
| y**3
发帖数: 267 | 7 this is time -to-event data. But we want to perform time-sliced model
instead of survival model. for instance, to build a model at 1 year to
predict the event or not. The set up of the data should censor all the data
beyond 1 year and then to identify event and predictors within 1-year and
then train the model. Not using data beyond 11 year to identify event or not
for the 1-year model?
Anybody confirm? thanks | j*****e
发帖数: 182 | 8 What your doing could be very dangerous.
What does "censer all the data beyond 1 year" mean? You could have changed
the underlying population of interest without recognizing it.
In my experience, the hardest part of survival analysis is to recognize your
actual study population and the sampling scheme. |
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