You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
A desire stemmed from working with small-to-medium-sized datasets (400-1500 subjects with 50-70% event rates) is that one should utilise as much data as possible for estimation. This is often the case with many RCTs in the medical literature, where large RCTs, such as the ACCORD, SPRINT, and IST (referenced in gfr resource materials), are rare.
As such, I am wondering whether it is possible to construct TOC plots using estimated RATE in a cross-/sequential-fitting framework. If confidence intervals of RATE are not too critical/required (given that a formal t-test can be done separately), is it valid to simply pool (average or weighted average of) the RATE estimates from all test folds, assuming the distribution of the policy/criteria is not too different across folds. In case of non-comparable distributions, I guess one may be able to set q in each fold so that it reflects the trial-wise distribution.
Ideally, cross-fitting would be preferred, allowing all data to be used. However, would sequential fitting be preferred given the potential correlation of RATE estimates across folds?
Many thanks in advance,
Lee
The text was updated successfully, but these errors were encountered:
Hi grf,
A desire stemmed from working with small-to-medium-sized datasets (400-1500 subjects with 50-70% event rates) is that one should utilise as much data as possible for estimation. This is often the case with many RCTs in the medical literature, where large RCTs, such as the ACCORD, SPRINT, and IST (referenced in gfr resource materials), are rare.
As such, I am wondering whether it is possible to construct TOC plots using estimated RATE in a cross-/sequential-fitting framework. If confidence intervals of RATE are not too critical/required (given that a formal t-test can be done separately), is it valid to simply pool (average or weighted average of) the RATE estimates from all test folds, assuming the distribution of the policy/criteria is not too different across folds. In case of non-comparable distributions, I guess one may be able to set q in each fold so that it reflects the trial-wise distribution.
Ideally, cross-fitting would be preferred, allowing all data to be used. However, would sequential fitting be preferred given the potential correlation of RATE estimates across folds?
Many thanks in advance,
Lee
The text was updated successfully, but these errors were encountered: