Use of Marginal Structural Models in a Comparative Effectiveness Study of Intravenous Iron Formulations in End-Stage Renal Disease

Slide presentation from the AHRQ 2011 conference.

On September 19, 2011, Alan Brookhart made this presentation at the 2011 Annual Conference. Select to access the PowerPoint® presentation (303 KB). .


Slide 1

Slide 1. Use of Marginal Structural Models in a Comparative Effectiveness Study of Intravenous Iron Formulations in End-Stage Renal Disease

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

Slide 2

Slide 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.

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.

Slide 3

Slide 3. Conflict of Interest / Acknowledgments

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).

Slide 4

Slide 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.

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

Slide 5

Slide 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.

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.

Slide 6

Slide 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.

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.

Slide 7

Slide 7. Trends in EPO Dosing in US Hemodialysis Patients  Image: line graph showing the mean quarterly EPO dose from 2000Q1 to 2008Q3. There is a gradual upward trend.

Trends in EPO Dosing in US Hemodialysis Patients

Image: line graph showing the mean quarterly EPO dose from 2000Q1 to 2008Q3. There is a gradual upward trend.

Slide 8

Slide 8. Trends in Hematocrit in US Hemodialysis Patients  Image: line graph showing the mean quarterly HCT dose from 2000Q1 to 2008Q3.

Trends in Hematocrit in US Hemodialysis Patients

Image: line graph showing the mean quarterly HCT dose from 2000Q1 to 2008Q3.

Slide 9

Slide 9. Trends in Iron Dosing in US Hemodialysis Patients (By Formulation and Overall)  Image: line graph showing the mean quarterly iron dose from 2000Q1 to 2008Q3. Formulations covered are any iron, iron sucrose, ferric gluconate, and iron dextran.

Trends in Iron Dosing in US Hemodialysis Patients
(By Formulation and Overall)

Image: line graph showing the mean quarterly iron dose from 2000Q1 to 2008Q3. Formulations covered are any iron, iron sucrose, ferric gluconate, and iron dextran.

Slide 10

Slide 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.

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.

Slide 11

Slide 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

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

Slide 12

Slide 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.

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.

Slide 13

Slide 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?

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?

Slide 14

Slide 14. DaVita Data  Large dialysis provider in the US.  1,500 units and 150,000 patients.  Data from 2004-2009 on 250,000 patients:  Labs every 2 weeks to month.  Individual treatment.  Clinical data: BP, BMI, vascular access in use.

DaVita Data

  • Large dialysis provider in the US.
  • 1,500 units and 150,000 patients.
  • Data from 2004-2009 on 250,000 patients:
    • Labs every 2 weeks to month.
    • Individual treatment.
    • Clinical data: BP, BMI, vascular access in use.

Slide 15

Slide 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.

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.

Slide 16

Slide 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.

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.

Slide 17

Slide 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.

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.

Slide 18

Slide 18. Aim 1: Comparative Study of Acute Effects  Image of a timeline titled "Rapid onset iron exposure effects (Case-Crossover Analysis).  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.

Aim 1: Comparative Study of Acute Effects

Image of a timeline titled "Rapid onset iron exposure effects (Case-Crossover Analysis).

  • 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.

Slide 19

Slide 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.

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.

Slide 20

Slide 20. Aim 2: Comparative Study of Short-Term Effects  Image of a graph titled "Study Design for Intermediate-term Effects (ITT Analysis, with Propensity Score Adjustment).  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.

Aim 2: Comparative Study of Short-Term Effects

Image of a graph titled "Study Design for Intermediate-term Effects (ITT Analysis, with Propensity Score Adjustment).

  • 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.

Slide 21

Slide 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.

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.

Slide 22

Slide 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.

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.

Slide 23

Slide 23. Aim 4: Comparative Study of Chronic Effects  Image of a chart titled "Chronic Effects (Marginal Structural Model)".  Examine outcomes caused by long-term exposure.  Iron exposure is a longitudinal variable.  Compare risk of:  Continual treatment with iron sucrose versus ferric gluconate.

Aim 4: Comparative Study of Chronic Effects

Image of a chart titled "Chronic Effects (Marginal Structural Model)".

  • Examine outcomes caused by long-term exposure.
  • Iron exposure is a longitudinal variable.
  • Compare risk of:
    • Continual treatment with iron sucrose versus ferric gluconate.

Slide 24

Slide 24. Time Dependant Confounding  Image of a figure.

Time Dependant Confounding

Image of a figure.

Slide 25

Slide 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.

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.

Slide 26

Slide 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?

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?

Slide 27

Slide 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?

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?

Slide 28

Slide 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.

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.

Slide 29

Slide 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.

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.

Slide 30

Slide 30. Thank you.

Thank you.

Slide 31

Slide 31. "Black Box" Warning  On March 9th, 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."

"Black Box" Warning

  • On March 9th, 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.
Current as of March 2012
Internet Citation: Use of Marginal Structural Models in a Comparative Effectiveness Study of Intravenous Iron Formulations in End-Stage Renal Disease. March 2012. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2011/brookhart/index.html