Exploring large weight deletion and the ability to balance confounders when using inverse probability of treatment weighting in the presence of rare treatment decisions.
OBJECTIVE
When medications are modified in response to changing clinical conditions, confounding by indication arises that cannot be controlled using traditional adjustment. Inverse probability of treatment weights (IPTWs) can address this confounding given assumptions of no unmeasured confounders and that all patients have a positive probability of receiving all levels of treatment (positivity). We sought to explore these assumptions empirically in the context of epoetin-alfa (EPO) dosing and mortality.


METHODS
We developed a single set of IPTWs for seven EPO dose categories and evaluated achieved covariate balance, mortality hazard ratios, and confidence intervals using two levels of treatment model parameterization and weight deletion.


RESULTS
We found that IPTWs improved covariate balance for most confounders, but was not optimal for prior hemoglobin. Including more predictors in the treatment model or retaining highly weighted individuals resulted in estimates closer to the null, although precision decreased.


CONCLUSIONS
We chose to evaluate weights and covariate balance at a single time-point to facilitate an empirical analysis of model assumptions. These same assumptions are applicable to a time-dependent analysis, although empirical examination is not straight forward in that case. We find that the inclusion of rare treatment decisions and the high weights that result is needed for covariate balance under the positivity assumption. Removal of these influential weights can result in bias in either direction relative to the original confounding. It is therefore important to determine the reason for these rare patterns and whether inference is possible for all treatment levels.
Center for Observational Research, Amgen Inc., Thousand Oaks, CA 91320, USA. rkilpatr@amgen.com
Ryan D
RD
Kilpatrick
Dave
D
Gilbertson
M Alan
MA
Brookhart
Eric
E
Polley
Kenneth J
KJ
Rothman
Brian D
BD
Bradbury
D000328:Adult; D000368:Aged; D001835:Body Weight / Q000187:drug effects* / Q000502:physiology; D003657:Decision Making; D004921:Erythropoietin / Q000008:administration & dosage*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011336:Probability; D006435:Renal Dialysis / Q000639:trends; D016896:Treatment Outcome
D004921:Erythropoietin