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Use of Marginal Structural Models in a Comparative Effectiveness Study of Intravenous Iron Formulations in End-Stage Renal Disease M. Alan Brookhart, Ph.D.

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Presentation on theme: "Use of Marginal Structural Models in a Comparative Effectiveness Study of Intravenous Iron Formulations in End-Stage Renal Disease M. Alan Brookhart, Ph.D."— Presentation transcript:

1 Use of Marginal Structural Models in a Comparative Effectiveness Study of Intravenous Iron Formulations in End-Stage Renal Disease M. Alan Brookhart, Ph.D. Department of Epidemiology, UNC Gillings School of Global Public Health University of North Carolina at Chapel Hill

2 Overview End-stage renal disease and anemia management Overview of my ARRA-funded CER study of iron Use of MSMs to estimate long-terms effects of iron

3 Conflict of Interest / Acknowledgments Project is supported by a contract from AHRQ’s DEcIDE center I have received research support from Amgen (that placed no restrictions on publications) and have sat on advisory boards for Amgen and Pfizer (honorarium declined or paid to institution).

4 End-Stage Renal Disease 50% have diabetes 85% have hypertension 27% have ischemic heart disease 2 major hospitalizations /year 20-25% annual mortality rate 2006 USRDS Annual Data Report

5 Anemia is a common complication of ESRD Anemia = low hemoglobin/hematocrit levels Anemia leads to –Cardiovascular problems –Decreased energy level, cognitive and physical functioning –Requires transfusions In ESRD, anemia caused by lack of erythropoietin and exacerbated by loss of iron

6 Anemia Management RCTs have shown that treatment with recombinant erythropoietin (EPO) and intravenous iron raises hematocrit in ESRD In widespread use in ESRD population Controversy: –Cost : Medicare spent over $2B on EPO in 2005 EPO has been a major source of revenue for dialysis centers –Safety: Questions about safety of EPO, appropriate hematocrit targets 2007 FDA placed a “black box” advisory on the label of ESAs (EPO) –> increased use of iron for anemia management

7 Trends in EPO Dosing in US Hemodialysis Patients

8 Trends in Hematocrit in US Hemodialysis Patients

9 Trends in Iron Dosing in US Hemodialysis Patients (by formulation and overall)

10 Potential Benefits and Risks Associated with IV Iron Use Aggressive use of iron may safely treat anemia, reduce need for EPO (DRIVE study) But may increase risk of iron overload, infections or other adverse outcomes

11 Comparative Effectiveness of Intravenous Iron in End-Stage Renal Disease 3-year project funded through AHRQ Co-investigators: Abhi Kshirsagar, MD – UNC Kidney Center Steve Cole, PhD – UNC Epidemiology Til Sturmer, MD – UNC Epidemiology Wolfgang Winkelmayer, MD — Stanford Medicine

12 Evidence Gap 1: Investigate the CER of different iron formulations Two formulations in widespread use –Ferric gluconate –Iron sucrose Different pharmacologically Little data on comparative effectiveness

13 Evidence Gap 2: Investigate the CER of iron dosing approaches Iron status measured with monthly labs –Transferrin saturation –Ferritin When should iron be administered? How should it be administered? –Maintenance dosing versus bolus dosing How much should be administered?

14 DaVita Data Large dialysis provider in the US 1,500 units and 150,000 patients Data from on 250,000 patients –Labs every 2 weeks to month –Individual treatment –Clinical data: BP, BMI, vascular access in use

15 Renal Research Institute Data Small chain of dialysis providers associated with academic medical centers in the US 15,000 prevalent patients Similar clinical data to DaVita Quality of life (SF-36), recorded every three weeks Additional labs: C-reactive protein

16 Medicare Data Hospitalization data Data from physicians, dialysis encounters outside of DaVita Date and cause of death Transplant information Linked with DaVita and RRI data

17 Outcomes Anemia management outcomes –Decreased use of ESAs –Hemoglobin control –Quality of Life Infection –Sepsis, vascular access infection, infection-related mortality Cardiovascular –AMI, stroke, CV-related mortality Hypersensitivity: Anaphylaxis, drug allergy All-cause mortality

18 X Rapid onset iron exposure effects (Case-Crossover Analysis) Event of interest: anaphylaxis, hospitalization for sepsis, AMI Hazard PeriodControl Period start of dialysis Aim 1: Comparative Study of Acute Effects Examine effects on clinical outcomes that occur within days after exposure to iron Case crossover design analyzed by conditional logistic regression Contrasts of Interest –Iron sucrose versus ferric gluconate –High dose versus low dose

19 Aim 1: Strengths and Limitations Strengths of case crossover design –Self-controlled, not confounded by time-invariant covariates Limitations –Confounded by time-varying confounders Hospitalizations Sensitivity analysis – control for hospitalization status – vary size of hazard and control windows

20 Aim 2: Comparative Study of Short-Term Effects Examine effects that occur within 6-months after exposure to iron Propensity score/IPTW to adjust for confounders Compare risk of –Iron sucrose versus ferric gluconate –High dose versus low dose –Compare bolus versus maintenance 6-month baseline period: covariates defined X O 3-month follow-up period Study Design For Intermediate-term Effects (ITT Analysis, with Propensity Score Adjustment) First TSAT lab, at least 6 months of after the start of dialysis 1-month iron exposure assessment 3-month rhEPO, TSAT, Ferritin assessment Censoring Clinical outcome

21 Aim 2: Strengths and Limitations Strengths of IPTW, propensity score analysis –Control for many confounders –Yields interpretable causal effect Limitations –Violations of non-positivity –Up-weighting of patients with rare treatment or data errors –Unmeasured confounders Sensitivity analysis –Use SMR-weighting –Vary definitions of high versus low dose

22 Aim 3: Heterogeneity of Short-Term Effects Repeat aim 2 across a range of clinically-relevant subgroups –Iron status (high ferritin, low Tsat) –Liver disease –Diabetes –Inflammation –Cause of end-stage renal disease –Age –History of infection

23 Aim 4: Comparative Study of Chronic Effects Examine outcomes caused by long-term exposure Iron exposure is a longitudinal variable Compare risk of –Continual treatment with iron sucrose versus ferric gluconate 6-month baseline period: covariates defined O Chronic Effects (Marginal Structural Model) 1-month iron exposure assessment Censoring 1-month covariate & outcome assessment 1-month iron exposure assessment Time 1Time 2 … Time n 1-month covariate & outcome assessment 1-month covariate & outcome assessment 1-month iron exposure assessment First TSAT lab, at least 6 months of after the start of dialysis

24 Time Dependant Confounding Iron Treatment at Month 1 Ferritin, TSat, Infection status Ferritin, TSat, Infection status Iron Treatment at Month 2 Outcome

25 Marginal Structural Model Analysis Marginal structural model to address time-varying confounders Causal contrasts of interest –Continual treatment with iron sucrose versus ferric gluconate –Continual treatment with high versus low dose Two-stage treatment model –for treatment versus no treatment –high dose versus low dose Assume formulation choice is exogenous

26 Issue 1: A month is a long time for a dialysis patient Dialysis patients have very dynamic health status Iron exposure during the month may be dependant on events occurring during the month Tangles up effect of events with effect of iron Possible solution: –Unit of observation is a one-week period of event the dialysis session?

27 Issue 2: Causal Contrast In this setting, MSMs returns an effect estimate that is not directly clinically relevant E.g., effect of continuous treatment with high dose versus continuous treatment with low dose Better approach would use a dynamic treatment approach, compare the effect of treat with 1g of iron when Tsat<20% versus Tsat<15%. Standard MSM could reveal risks of chronic treatment Iron is not given in this way, are data informative about such treatment effects?

28 Issue 3: Non-Positivity Iron treatment decisions driven strongly by transferrin saturation and infection status Some patients almost always treated, some almost always untreated Treatment contrary to prediction/indication can lead to lead to huge weights Data errors can also lead to very large weights We will experiment with weight truncation and trimming

29 Expected Results, Problems, Future Directions Multiple analytic methods and sensitivity analysis provides robustness to finding If methods do not agree, have to decide why –Estimating different causal effects –Assumptions hold for one method but not another Expect the study will yield important evidence about the comparative effectiveness of different iron formulations and different dosing regimens Future work with dynamic treatment models may help to identify “treatment strategies” to minimize risk and maximize clinical benefit of anemia management RCTs

30 Thank you.

31 “Black Box” Warning On March 9 th, 2007 FDA added a “black box” warning to labels of all ESA. “The new boxed warning advises physicians to monitor red blood cell levels (hemoglobin) and to adjust the ESA dose to maintain the lowest hemoglobin level needed to avoid the need for blood transfusions. Physicians and patients should carefully weigh the risks of ESAs against transfusion risks.”


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