Exploring large weight deletion and the ability to balance confounders when using inverse probability of treatment weighting in the presence of rare treatment decisions

Pharmacoepidemiol Drug Saf. 2013 Feb;22(2):111-21. doi: 10.1002/pds.3297. Epub 2012 Jun 4.


Purpose: 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.

Conclusion: 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Body Weight / drug effects*
  • Body Weight / physiology
  • Decision Making*
  • Erythropoietin / administration & dosage*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Biological*
  • Probability*
  • Renal Dialysis / trends
  • Treatment Outcome


  • Erythropoietin