title: Different analyses estimate different parameters of the effect of erythropoietin stimulating agents on survival in end stage renal disease: a comparison of payment policy analysis, instrumental variables, and multiple imputation of potential outcomes.
abstract: OBJECTIVE
  To compare the assumptions and estimands across three approaches to estimate the effect of erythropoietin-stimulating agents (ESAs) on mortality.
  
  
  METHODS
  Using data from the Renal Management Information System, we conducted two analyses using a change to bundled payment that, we hypothesized, mimicked random assignment to ESA (pre-post, difference-in-difference, and instrumental variable analyses). A third analysis was based on multiply imputing potential outcomes using propensity scores.
  
  
  RESULTS
  There were 311,087 recipients of ESAs and 13,095 non-recipients. In the pre-post comparison, we identified no clear relationship between bundled payment (measured by calendar time) and the incidence of death within 6 months (risk difference -1.5%; 95% confidence interval [CI] -7.0%, 4.0%). In the instrumental variable analysis, the risk of mortality was similar among ESA recipients (risk difference -0.9%; 95% CI -2.1, 0.3). In the multiple imputation analysis, we observed a 4.2% (95% CI 3.4%, 4.9%) absolute reduction in mortality risk with the use of ESAs, but closer to the null for patients with baseline hematocrit level >36%.
  
  
  CONCLUSIONS
  Methods emanating from different disciplines often rely on different assumptions but can be informative about a similar causal contrast. The implications of these distinct approaches are discussed.
authors.affiliation: Department of Health Services, Policy & Practice, Brown University, Providence, RI MH082773, USA. david_dore@brown.edu
authors.forename: David D
authors.initials: DD
authors.lastname: Dore
authors.forename: Shailender
authors.initials: S
authors.lastname: Swaminathan
authors.forename: Roee
authors.initials: R
authors.lastname: Gutman
authors.forename: Amal N
authors.initials: AN
authors.lastname: Trivedi
authors.forename: Vincent
authors.initials: V
authors.lastname: Mor
mesh_terms: D000368:Aged; D000740:Anemia / Q000097:blood / Q000188:drug therapy*; D015984:Causality; D057186:Comparative Effectiveness Research / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006397:Hematinics / Q000009:adverse effects / Q000191:economics / Q000627:therapeutic use*; D006400:Hematocrit; D006801:Humans; D007676:Kidney Failure, Chronic / Q000097:blood / Q000191:economics / Q000401:mortality*; D008297:Male; D008875:Middle Aged; D017891:Pharmacoepidemiology / Q000379:methods*; D057766:Policy; D057216:Propensity Score; D019984:Quality Indicators, Health Care; D012051:Reimbursement Mechanisms; D006435:Renal Dialysis / Q000191:economics / Q000592:standards; D012107:Research Design; D016896:Treatment Outcome; D014481:United States
chemical_list: D006397:Hematinics
keywords: Causal inference; Comparative effectiveness research; Dialysis; End-stage renal disease; Methods; Pharmacoepidemiology