Timeline for Analysis strategy for rare outcome with matching
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 11, 2016 at 16:06 | comment | added | user22 | Missed the deadline for the bounty. I haven't managed to solve the problem but @Frank's solution will most likely be the preferred one. Thanks for help! | |
| Dec 11, 2016 at 16:05 | vote | accept | CommunityBot | moved from User.Id=22 by developer User.Id=357270 | |
| Dec 6, 2016 at 21:10 | comment | added | Frank Harrell | You have to decide which variables are unlikely to interact with the other variables. When effects are additive you don't need to have all combinations of values well represented in the data. | |
| Dec 6, 2016 at 13:39 | comment | added | user22 | And how about situations when certain combinations of sex, age and disease type when no treatment was found? Should they be excluded beforehand? Or kept in the model (since they will not provide any information I believe)? Also the opposite situation might raise a challenge - there are few rare cases when age/sex/disease combination has disease only, but no observations without disease.. | |
| Dec 6, 2016 at 11:18 | comment | added | Frank Harrell | Yes with careful and fairly liberal covariate adjustment. Propensity score analysis is for the opposite situation where the outcome is what is rare. | |
| Dec 6, 2016 at 9:07 | comment | added | user22 | Thanks @Frank. Should I then simply start considering models using all data and use an estimate on a binary categorical variable (received treatment) as an estimate of difference for treated group? | |
| Dec 2, 2016 at 14:15 | history | answered | Frank Harrell | CC BY-SA 3.0 |