I was told that handling missing not at random (MNAR) is complicated. I’m tempted to perform imputation from different methods (chosen blindly) and present the outcomes with sensitivity analysis using these methods vs complete cases. However, I’m writing here to ask if there is a specific concept/ method that could help my specific problem.
The registry started collected data for the covariate of my interest at a certain year (e.g. 2005) but the data was manly missing for patients who died or censored before that point. After 2005, I have only 100 patients missing the data from 100,000. Furthermore, most patients who survived beyond 2005 recalled the data and it was available in the registry (30,000 vs 5000 [missing data] patients). Using log-rank test for survival difference between the two groups, there was a statistically significant difference between patients in the registry before 2005 who are missing the data for that covariate and those who had the data.
I was advised to use left truncation, but I already have all outcomes data.
I’d appreciate any insight or advice to module with this covariate the best way possible.