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A Quality-Adjusted Price Index for Colorectal Cancer Drugs Claudio Lucarelli Sean Nicholson Cornell University June 2, 2007. New (Expensive) Technology Is the Engine Driving Increases in Medical Spending Causes of Growth in Medical Spending, 1950-1987. Aging of population. 15%.

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slide1

A Quality-Adjusted Price Index for

Colorectal Cancer Drugs

Claudio Lucarelli

Sean Nicholson

Cornell University

June 2, 2007

slide2

New (Expensive) Technology Is the Engine Driving

Increases in Medical Spending

Causes of Growth in Medical Spending, 1950-1987

Aging of population

15%

Technological change:

new drugs, procedures,

devices, and increased

“intensity” of care.

More generous insurance

coverage (lower price to

patient at point of care)

12%

63-68%

5-10%

Increase in personal

income

Source: Newhouse, “Medical Care Costs: How Much Welfare Loss?”,

Journal of Economic Perspectives, Summer 1992.

slide3

Research Questions

  • Does the value of new colorectal cancer chemotherapy
  • drugs exceed their cost?
  • 2) Did colorectal cancer drug prices rise or fall between
  • 1993 and 2005 once one correctly controls for the
  • changing attributes/quality of the drugs?
  • Policy implication: should the government (and private
  • insurers) increase or reduce the incentives for
  • pharmaceutical and biotech firms to invest in new
  • medical technologies?
slide4

Little Existing Evidence on These Issues Due

to Empirical Challenges

Traditional price index: P1Q0

P0Q0

P1/P0: price increase holding quantity fixed at Year 0 level.

Challenges in health care:

1) Often do not observe the transaction price (P).

2) Due to insurance and physician’s role as agent, consumer

purchases do not necessarily reveal marginal valuation.

3) Quantities (Q) change over time as physicians/patients

adopt new medical technologies.

4) Patient outcomes (quality) change over time.

slide5

Evidence on Quality-Adjusted Health Care Prices

1) Heart attack treatment (Cutler, McClellan, Newhouse, Remler, 1998)

- average annual real price increase of 0.7% per year.

- prices fell 1.1% per year when account for improved mortality

(assume a year of life is worth $25,000).

2) Depression (Berndt, Bir, Busch, Frank, Normand, 2002)

- expert panel estimated probability of full remission associated

with each observed treatment method.

- estimate incremental spending per full remission case relative

to the expected effect of a placebo.

- incremental quality-adjusted prices fell about 2% per year

between 1991-1996 among privately-insured patients.

slide6

Why Colorectal Cancer?

  • 112,000 new cases diagnosed per year in the United States.

52,000 deaths per year in the U.S.

  • 5 new drugs launched in the U.S. between 1996 and 2004.

Drug prices and spending increasing substantially.

  • Consistent information on efficacy and side effects of each

drug based on phase 3 clinical trials.

  • Most chemotherapy drugs are infused into a patient in a

physician’s office (59%) or hospital clinic (28%).

  • Physicians take ownership of oncology drugs; we observe

the price they pay wholesalers.

slide7

Schematic of Colon Cancer Treatment

Patient

Diagnosed

Cancer is stage

1, 2, or 3

Surgeon removes

section of colon

Cancer is stage 4

1st Line

metastatic

chemo

Cancer

metastasizes

Adjuvant

chemo (30%)

70%

of

chemo

patients

2nd Line

metastatic

chemo

No recurrence

slide8

Data

  • 1) Drug Prices from IMS, 1993-2005
  • Invoice/transaction amount actually paid by a customer type (e.g., MD

offices) and quantity of drugs purchased for each quarter.

  • We calculate average (across NDC codes) price per mg of active ingredient.

2) Regimen (combination of drugs) Market Shares

  • SEER: claims-based market share by regimen by quarter for Medicare

patients for 1993-2001.

  • IntrinsiQ: market share by regimen by quarter based on physician

surveys for 2002-2005.

3) Regimen-Specific Dose Per Drug Per Patient

  • National Comprehensive Cancer Network: typical quantity of each drug

used in each regimen for a representative patient.

4) Drug Attributes

  • Overall survival rates, time to progression of cancer, tumor response rates,

and side effects experienced by Phase 3 trial patients, as reported in

package inserts, published articles, and conference abstracts.

slide9

Calculating the Price of a Regimen

  • Assume every patient receives 24 weeks of chemotherapy.
  • Use mg dosage levels recommended by National

Comprehensive Cancer Network (see last pages of

handout for details)

  • Assume representative patient has a surface area of 1.7m2

and weighs 80kg.

  • Apply IMS prices per mg of active ingredient (separately

for hospitals and physicians).

slide10

Price of Drug Regimens Weighted by Market Shares

1993-2005

$36,305

2,600%

increase

$13,039

$1,839

$126

1993 1995 1997 1999 2001 2003 2005

Note: prices are for a 24-week treatment cycle.

slide11

Market Shares of 12 Largest Regimens: 1993-2005

5-FU/LV

SEER data IntrinsiQ data

Oxaliplatin + 5-FU/LV

Bevacizumab +

oxaliplatin +

5-FU/LV

Irinotecan + 5-FU/LV

Outside option

Irinotecan

1993 1995 1997 1999 2001 2003 2005

slide12

Efficacy of Drugs From Phase 3 Clinical Trials:

1st line Therapy for Metastatic Colorectal Cancer

(Median, months)

(Median, months)

Note: capecitabine is a tablet.

slide13

Percentage of Phase 3 Trial Patients w/ a Grade 3 or

Grade 4 Side Effect: Metastatic First-Line Therapies

40

30

20

10

0

Note: capecitabine is a tablet.

slide14

Hedonic Price Index

t=2005:3

ln(pjt) = βXjt + Σγtdt + εjt

t=1993:2

  • pjt: price of regimen j in quarter t.
  • X: attributes of each regimen (efficacy and side effects).
  • β: “implicit prices” of attributes (to physicians).
  • γt: yield price index, controlling for changing attributes.
slide15

Hedonic Regression Coefficient Estimates

VariableCoefficientStandard Error

Efficacy

Survival (months) 0.496** 0.0049

Response rate (%) 0.422** 0.0027

Response rate * 2nd line -0.141** 0.0013

Time to progression (months) -2.58** 0.019

Side effects

Diarrhea 0.124** 0.0011

Nausea 0.059* 0.0012

Abdominal pain 0.336** 0.0028

Vomiting -0.019** 0.0009

Neutropenia -0.045** 0.0005

2nd line therapy 10.2** 0.050

Tablet -0.101** 0.0086

Observations 492

R2 0.99

slide16

Hedonic Price Index, 1993-2005

Bevacizumab (2004:1)

Oxaliplatin (2002:3)

Indexed

to 1.0

in 1st

quarter

of 1993

0.88

Cetuximab (2004:1)

Irinotecan

(1996:3)

Capecitabine

(1998:2)

1993 1995 1997 1999 2001 2003 2005

Note: (year : quarter) of entry.

slide17

Quality-Adjusted Price Index

  • In each quarter (t), physician (i) chooses the drug (j) that

maximizes his/her utility:

Uijt = αPjt + βXjt + ξj + Δξjt+ εijt

  • α: price sensitivity of a physician.
  • β: value of each attribute from a physician’s perspective.
  • ξj: mean of unobserved attributes for regimen j.
  • Δξjt: time-specific deviation from ξj.
  • 1st estimation approach (BLP next):

ln(sjt)– ln(s0t) = αPjt + βXjt + ξj + Δξjt+ εijt

  • sjt: market share of regimen j in quarter t.
  • s0t: market share of outside option in quarter t.
  • instrument for P w/ # of drugs in the market and the sum

of the observed attributes of the competitor regimens.

slide18

Quality-Adjusted Regression Coefficient Estimates

VariableCoefficientStandard Error

Price (instrumented) -0.706** 0.079

Efficacy

Survival (months) 0.248** 0.0085

Response rate (%) 0.208** 0.051

Response rate * 2nd line 0.118** 0.031

Time to progression (months) -0.945** 0.338

Side effects

Diarrhea -0.0015 0.021

Nausea 0.129** 0.0047

Abdominal pain 0.188** 0.0088

Vomiting -0.010* 0.057

Neutropenia -0.043** 0.012

Tablet -2.26** 0.313

Observations 216

R2 0.75

slide19

Constructing a Quality-Adjusted Price Index

1) Quantify the Equivalent Variation (EV):

How much income could be taken away from (or given to)

an individual to leave him indifferent between facing the

old choice set -- attributes and prices -- and the new improved

(inferior) choice set.

Ut – Ut-1

2) Translate EV into a Quality-Adjusted Price Index: find the

amount by which prices would have to change in order to

produce the same welfare effect as expressed bythe EV.

slide21

Conclusions

  • Important to control for product attributes: naïve price
  • index greatly overstates the “true” price change.
  • 2) New colon cancer drugs provide welfare increases that
  • justify their higher prices.
  • 3) Pharmaceutical/biotech companies capture most of the
  • welfare increases associated with the new drugs, which
  • provides strong incentives for innovation.
  • 4) When these drugs lose patent protection, presumably
  • consumers would capture more of the welfare gains.
  • 5) Hedonic index fails to capture value of “extending the
  • range” type of innovation, whereas quality-adjusted does.