MTPPI EPO Outcomes Research

Presented to FDA /CDER

Joint Meeting of the Cardiovascular and Renal

Drugs & Drug Safety and Risk Management

Advisory Committees

September 11, 2007

Hilton Washington

Gaithersburg, MD 1

Background and Context

Dennis CotterPresident of Medical Technology and Practice Patterns Institute (MTPPI)

4733 Bethesda Avenue #510

Bethesda, MD 20814

2

Decade-long study of EPO

• Identified Medicare and non-Medicare use of EPO

• Quantified total EPO use among dialysis patients

• Currently, PI on R01 grant focusing on the role of EPO dosing and patient outcomes

3

Hemoglobin values have increased steadily after EPO introduced

Source: USRDS 2006 Annual Data Report

4

Widespread EPO use based on 2000 DOQI findings including:

– Survival benefits

– Decreased incidence of hospitalization

– Partial regression of left ventricular hypertrophy (LVH)

– Improved quality of life

– Increased exercise capacity

5

However, survival findings might have been confounded by EPO treatment itself

6

Application of causal modeling techniques

Received R01 grant (5R01DK066011-02 Epoetin Therapy and Survival of

Hemodialysis Patients) to examine the role of EPO treatment in patient outcomes

7

Introduction to Causal Modeling

Miguel Hernán

Associate Professor of Epidemiology

Department of Epidemiology

Harvard School of Public Health

677 Huntington Avenue

Boston, MA 02115

8

Goal

To estimate the effect of EPO on hematocrit and survival among renal failure patients with anemia

A RCT would be ideal

Next best thing is an observational study that mimics an RCT

9

Problem with observational studies

Patients with worse prognosis tend to receive higher EPO doses (confounding by indication)

Not a problem in ITT analyses of RCTs

10

Actually, there are 2 problems

1. Confounding may be unmeasured

2. Confounding may be measured but inappropriately adjusted for

11

Problem 1Unmeasured confounding

THE fundamental problem

Need measurements of all important prognosis factors that are also indications for treatment but can never prove you have all confounders

12

Problem 2Inappropriately adjusting for confounding

Conventional statistical methods cannot appropriately adjust for confounding

When the prognosis factors (e.g., hematocrit) that affect treatment decisions (e.g., EPO dose) are themselves affected by prior treatment decisions

A solvable problem: just use inverse probability weighting (IPW)

13

IPW: Utility

Can be used to mimic an RCT using observational data

Under the assumption of no unmeasured confounding

Even in the presence of time-varying confounders affected by prior treatment

14

IPW: Technical details

Each subject is weighted by the inverse of the estimated probability of receiving the EPO dose that he actually received

Essentially equivalent to standardization

The corresponding weighted models estimate the parameters of marginal structural models

15

IPW: Examples of application

IPW extensively used in HIV/AIDS research

In fact, NIH required expertise on IPW when requesting applications for estimating the effects of antiretrovirals from observational data

IPW replicated estimates from RCTs in the HIV/AIDS field

16

IPW: Our application

We used IPW to estimate the survival and mean hematocrit of subjects randomly assigned to different EPO doses

We needed IPW because hematocrit is a time-dependent confounder (predicts both EPO dose and outcome) and is affected by prior EPO dose

17

Research Findings

Yi Zhang

Senior Analyst

MTPPI

18

The effect of EPO dose on hematocrit response among elderly hemodialysis patients in the U.S.

Cotter D, Zhang Y, Thamer M, Kaufman J, Hernán MA.

Kidney International 2007 [in press]

19

Mean monthly hemoglobin and mean EPO dose per week

Mean monthly hemoglobin & mean EPO dose per week

3

5

7

9

11

13

15

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Hem

og

lob

in(g

/dl)

00

02

04

06

08

10

12

14

16

18

Mean

EP

O d

ose (

in t

ho

usan

ds o

f u

nit

s)

HB

DOSE

Citation: U.S. Renal Data System, USRDS 2005 Annual Data Report: Atlas of End-Stage Renal Disease in the United States,National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2005.

Period prevalent dialysis patients with EPO claims; monthly hemoglobin includes all claims with a hematocrit value between 10 & 50; weekly EPO dose includes all claims for patients with an average number of administrations per month of =20. EPO doses adjusted for inpatient days through December, 2003; doses in January–June, 2004, are unadjusted.

20

Prior research

• Dose response relationship has not been examined since Phase II trials

– Stringent patient eligibility criteria

– Limited dose

• Observational studies have shown an inverse relationship between EPO dose and hematocrit

– Confounding by indication

21

Research goals

To mimic an RCT in which subjects are randomly assigned to different arms, each receiving a different EPO dose

To compare the achieved hematocrit in each arm

22

Data source• United States Renal Data System (USRDS)

– administrative database on ESRD patients whose

care is covered by Medicare

– include extensive baseline and follow-up

demographic and clinical data

– outpatient EPO claims include monthly total EPO

dose and hematocrit values

– most recent USRDS data available for researchers

23

Patient population• Retrospective cohort study.

• 14,001 patients who started EPO and dialysis in

2003. – >=65 years of age– had first claim with 90 days of their first ESRD service

date– had not used EPO before– did not have a kidney transplant, HIV or cancer before

starting dialysis.– were not censored during the first complete dialysis

month

24

Study variables

• Censoring events

– change of dialysis modality, transplantation, 30

days after change of dialysis provider, gap in

outpatient dialysis services, or death

• Exposure: Average EPO dose in the first 3

months of dialysis

• Outcome: HCT at month 4

25

Statistical methods• Estimated inverse probability weights to adjust for

measured confounders, and then fit a weighted regression model

• Constructed a dose-response curve• Each hematocrit-EPO dose point in the curve

shows the estimated average hematocrit if subjects had been randomly assigned to that EPO dose

• 95% CI were based on bootstrap techniques

26

Distribution of patients by initial EPO doses

>200 u/kg 13.1%150-200 u/kg 16.0%100-150 u/kg 31.7%50-100 u/kg 29.8%< 50 u/kg 9.4%

27

Distribution of patients by hematocrit group

<30% 30-<33% 33-<36% 36-<39% >=39% 0

5

10

15

20

25

30

35

Hematocrit group

Per

cent

of

patie

nts

23,400

21,000

21,100

21,500

26,000

Average EPO dose (U/week)

28

4

Dose response curve and 95% confidence intervals based on MSM

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 5032

33

34

35

36

37

38

39

40

Note: Red dots indicate FDA-recommended starting doses

Hem

atoc

rit a

t mon

th 4

(%

)

Average epoetin dose in months 1-3 (1,000 U/week)29

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 5232

33

34

35

36

37

38

39

40

Average epoetin dose in month 1-3 (1,000 U/week)

Hem

atoc

rit

at m

onth

4 (

%)

Dose response curve based on standard adjustment

30

Study limitations

• Potential for unmeasured confounding

• Monthly HCT and EPO dose• Unobserved clinical factors (iron level, blood

pressure, nutritional status...)• EPO use in the hospital, route of EPO

administration

• Did not consider dynamic EPO dosing regimes

• Restriction of study period and population

31

Conclusions

• Dose-response curve is S-shaped• HCT plateaus at 38.5% for average EPO

doses greater than 20,000 units/week• Normal HCT target might not be achievable

for dialysis population• Starting doses recommended by FDA are

appropriate and are in the linear portion of the curve

32

The relationship between EPO dose and survival among hemodialysis patients

Zhang Y, Thamer, Cotter D, Kaufman J, Hernán MA

Joint Statistical Meetings 2007 [Abstract]

33

Research goals

To mimic an RCT in which subjects are randomly assigned to different arms, each receiving a different average dose of EPO

To compare the survival in each arm

34

Previous research

• A plethora of observational studies have shown that higher hematocrit is associated with better survival for dialysis patients

• However, results of clinical trials demonstrated that patients targeted to higher hematocrit levels did not show survival benefits– led to a recent FDA black box warning

• The EPO dose-survival relationship has not been empirically determined 35

Study design

• 20,580 incident hemodialysis patients

• Eligibility criteria – Age 65 and older

– First ESRD service in 2003 – Attend freestanding facilities– Complete baseline (first 3 months of dialysis) data

• Exposure: cumulative average EPO dose

• Outcome: death during months 4-12

• Censored if change of provider/modality, or loss to follow-up 36

Methods• Estimated inverse probability weights to adjust for

measured confounders, and then fit a weighted Cox model

• Constructed survival curves for each EPO dose• Each curve shows the survival if subjects had

been randomly assigned to that EPO dose

• 95% CI were based on bootstrap techniques

37

Mortality hazard ratios by EPO dose (quartiles)

Table 2. Hazard ratios and 95% confidence intervals of death disaggregated by epoetin dose categories.

Cumulative epoetin dose (units/month)MSM Standard models

Unadjusted Adjusted controlling for HCT Adjusted not controlling for HCT

1.00L U1.00L U1.00LU 1.00LU0.640.58 0.691.080.97 1.201.080.971.20 1.030.921.150.760.69 0.831.321.19 1.461.241.11 1.38 1.201.071.341.050.96 1.151.781.61 1.961.491.331.67 1.501.341.69

1st quartile (<21,500)2nd quartile (21,500-36,500)3rd quartile (36,500-58,700)4th quartile (>=58,700)

Dose Quartiles (U/Mo) IPW Standard Adjustment1.00 (ref) 1.00 (ref)0.64 (0.58, 0.69) 1.08 (0.97, 1.20)0.76 (0.69, 0.83) 1.24 (1.11, 1.38)1.05 (0.96, 1.15) 1.49 (1.33, 1.67)

1st (<21,500)

2nd (21,500-36,500)3rd (36,500-58,700)4th (>=58,700)

38

Mortality rate by EPO dose

Table 2. Hazard ratios and 95% confidence intervals of death disaggregated by epoetin dose categories.

Cumulative epoetin dose (units/month)MSM Standard models

Unadjusted Adjusted controlling for HCT Adjusted not controlling for HCT

1.00L U1.00L U1.00LU 1.00LU0.640.58 0.691.080.97 1.201.080.971.20 1.030.921.150.760.69 0.831.321.19 1.461.241.11 1.38 1.201.071.341.050.96 1.151.781.61 1.961.491.331.67 1.501.341.69

1st quartile (<21,500)2nd quartile (21,500-36,500)3rd quartile (36,500-58,700)4th quartile (>=58,700)

0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 402.5

2.7

2.9

3.1

3.3

3.5

3.7

3.9

4.1

Note: Confidence intervals are forthcoming and will be shown at the FDA presentation

Haz

ard

of d

eath

(%

)

Cumulative average epoetin dose (1000 units/week)

39

Survival for EPO doses based on 3 different doses

2 4 6 8 10 12 140

0.2

0.4

0.6

0.8

1

1.2

5,000U/wk

12,500 U/wk

25,000 U/wk

Month

Su

rviv

al F

un

ctio

n

40

Study limitations

• Potential for unmeasured confounding as always

• Did not consider dynamic EPO dosing regimes

• One-year survival only

41

Conclusions

• Lowest mortality found for average EPO doses of 8,500-15,000 units per week

• Treating all patients with higher EPO doses (>15,000 U/wk) might decrease average survival

42

Relevance of research findings to FDA labeling decisions

INITIAL DOSEIn our study cohort, 61% of all incident elderly dialysis patients receivedan initial EPO dose higher than the FDA-approved 50-100 U/kg range

DOSE-RESPONSEBased on our dose-response model, a population average EPO dose higher than 12,000 U/week would result in exceeding the FDA-approved HCT target of 36%

RISKBased on our dose-survival model, a population average EPO dose higher than 15,000 U/week would result in progressively higher mortality risks

HYPORESPONSIVE PATIENTSThe risk of increased mortality is greatest among hyporesponsive patientswho receive the largest EPO doses

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