The Local Influence of Pioneer Investigators
on Technology Adoption
Evidence from New Cancer Drugs
Leila Agha1
1
David Molitor2
Boston University & NBER
2
University of Illinois
May 4, 2015
Introduction
Setting and Data
Analysis
Sorting
Do local networks matter for technology diffusion?
• How important are local“thought leaders”?
• Earlier work demonstrates that physician and hospital level technology adoption
is clustered
[Coleman et al. 1966, Baiker and Chandra 2010]
• But geographic or network clustering of technology adoption does not prove
spillovers–there could be correlated local demand!
• Recent empirical studies have focused on the role of spillover localization in the
creation of new ideas
[Jaffe et al. 1993, Azoulay et al. 2011]
• Centrality of the information injection point affects adoption levels of
microfinance in Indian villages
[Banerjee et al. 2013]
• Do these local factors matter for technology adoption in modern economies
with high human capital and easy access to information?
[Comin and Hobijm 2003]
Introduction
Setting and Data
Analysis
Sorting
Studying spillovers in adoption of new cancer drugs
• Why would “thought leaders” matter for the adoption of new cancer drugs?
• Prescribed by highly trained experts with access to clinical trial evidence
• Yet, anecdotally, oncologists report uncertainty about drug quality &
applications is a major stumbling block to adoption
• Goal:
Do physician investigators involved in clinical trials create “geographic
spillovers” for cancer drug diffusion, inducing nearby physicians to adopt more
quickly?
• Policy-relevant context for understanding forces behind technology adoption
• Technology has been a primary driver behind rising healthcare costs and large
improvements in survival
• Adoption is in the hands of expert decision makers—doctors—but little is
known about what drives adoption decisions!
• Use of medical technology varies dramatically across regions: does early
specialization lead to higher long-run use of a technology? (Roy model)
Introduction
Setting and Data
Analysis
Studying spillovers in adoption of new cancer drugs
• Study use of 21 new cancer drugs with Medicare claims data
• Empirical challenge: investigator location not randomly assigned
• Causal impact of proximity to a drug’s investigator is:
βproximity = Use of investigator’s drug by nearby physicians (observable)
− Use of same drug by nearby physicians had the
investigator been in another region (unobservable)
• Diff-in-diff implementation: Compare drug adoption in investigator’s region,
relative to
1
that drug’s adoption in other regions, and
2
“control drug” adoption in the same region
Sorting
Introduction
Setting and Data
Analysis
Sorting
Results Preview
1
New drug utilization more intensive in regions close to drug’s investigators
• Indicated patients 30% more likely to receive drug in first author’s region
• Proximity to other investigators also matters (but less)
• Initial information frictions do not spark persistent differences in use
2
First authors boost utilization both within & outside their own physician group
• Other authors boost use only within their own group
• Results robust to alternative definitions of “superstar” authors
3
Patient sorting results
• Patients appear more likely to travel to investigator regions
• IV strategy exploiting patient residence shows proximity effect on drug
utilization not driven solely by sorting
Introduction
Setting and Data
Outline
1
Setting and Data
2
Baseline empirical analysis
3
Patient Sorting and IV Analysis
Analysis
Sorting
Introduction
Setting and Data
Analysis
Why Study Cancer Drugs?
1
Cancer drugs prescribed by highly trained, narrowly specialized doctors.
• Drug side effects: kidney failure, lung damage, nausea, pain
• Chemo drugs are often prescribed as cocktails
• FDA approvals cover narrow set of indications, not necessarily population
with greatest benefit.
2
Cancer drugs are expensive
• Median monthly new drug introductory prices 2005-2009: $7,112
• Medicare Part B drug spending, dominated by cancer drugs, grew 234%
($3.4 to $10.7 billion) between 1998-2008
3
1% reduction in cancer mortality worth $500 billion
[Murphy and Topel, 2006]
Sorting
Introduction
Setting and Data
Analysis
Sorting
Data Description
Key elements needed for the empirical analysis:
1
Set of new, similar technologies: cancer drugs
• FDA archives: clinicaltrials.gov
2
The geographic location of investigators who lead the pivotal clinical trials
• Match FDA clinical trial to academic publication
3
Utilization of these new cancer drugs across regions over time
• Medicare claims data
Introduction
Setting and Data
Analysis
Sorting
Measuring Drug Utilization
• Goal: Track utilization of 21 new cancer drugs across regions from 1998-2008
• Measure fraction of indicated patients receiving drug, in each region
• Patient episodes include all chemo claims within region, year, for given patient
• Observe cancers ever diagnosed, treated over that episode
• For analysis, an observation is a patient-drug episode, if drug’s initial indication
diagnosed
• Measure whether patient is treated by a doctor who bills with the same tax ID
as a study author
• Final sample: 660,962 patient-drug episodes within 2 years following FDA
approval for drug
Introduction
Setting and Data
Analysis
Sorting
List of Drugs in Sample
Trade
name
(2)
FDA approval
date
(3)
(4)
Capecitabine
Xeloda
4/30/1998
breast cancer
Trastuzumab
Herceptin
9/25/1998
Valrubicin
Valstar
Denileukin diftitox
Ontak
Generic drug name
(1)
Temozolomide
(5)
Size of target
population
(6)
No. of authors
on pivotal trial
(7)
Dallas, TX
26,410
10
breast cancer
Chicago, IL
26,410
11
9/25/1998
bladder cancer
Chicago, IL
13,557
6
2/5/1999
cutaneous T-cell lymphoma
Durham, NC
819
26
Target disease
1st author city
Temodar
8/11/1999
brain cancer
Houston, TX
1,797
22
Epirubicin hydrochloride
Ellence
9/15/1999
breast cancer
Canada
53,762
18
Gemtuzumab ozogamicin
Mylotarg
5/17/2000
acute myeloid leukemia
Seattle, WA
2,192
17
Arsenic trioxide
Trisenox
9/25/2000
acute myeloid leukemia
New York, NY
1,079
15
Alemtuzumab
Campath
5/7/2001
chronic lymphocytic leukemia
Houston, TX
12,027
11
Zoledronic acid
Zometa
8/20/2001
hypercalcemia of malignancy
Canada
2,694
11
Ibritumomab tiuxetan1
Zevalin
2/19/2002
non-Hodgkin's lymphoma
Rochester, MN
51,042
13
Faslodex
4/25/2002
breast cancer
Houston, TX
64,045
14
Fulvestrant
Oxaliplatin
Eloxatin
8/9/2002
colon cancer
Nashville, TN
52,778
8
Bortezomib
Velcade
5/13/2003
multiple myeloma
Boston, MA
23,819
21
Tositumomab-I 131
Bexxar
6/27/2003
non-Hodgkin's lymphoma
Stanford, CA
54,275
7
Pemetrexed
Alimta
2/4/2004
lung cancer
Chicago, IL
84,918
13
Cetuximab
Erbitux
2/12/2004
colon cancer
United Kingdom
55,528
12
Bevacizumab
Avastin
2/26/2004
colon cancer
Durham, NC
55,528
15
Decitabine
Dacogen
5/2/2006
myelodysplastic syndromes
Houston, TX
15,460
16
Panitumumab
Arranon
9/27/2006
colon cancer
Belgium
59,028
12
Temsirolimus
Torisel
5/30/2007
kidney cancer
Philadelphia, PA
3,794
19
2
Introduction
Setting and Data
Analysis
Sorting
Map of Author Locations
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• First author locations in red, other author locations in green
• 17 drugs have US-based trials
• 11 unique HRRs contain at least 1 first author
• Common locations: Houston (4), Chicago (3), Durham (2), NYC (2)
Introduction
Setting and Data
Drug Utilization Summary Statistics
Analysis
Sorting
Introduction
Setting and Data
Analysis
Sorting
Drug Utilization Summary Statistics
Table&2:&Drug&use&summary&stastics
Variables:
Drug&utilization&rate
Fraction&treated&in&author's&group
Number&of&observations
Avg.&no.&of&patients&per&HRR&per&drug
No.&of&HRRMdrug&pairs
No.&of&unique&HRRs
First&author&HRR
(1)
Other&author&HRR
(2)
Author&HRR&for&
different&drug
(3)
HRR&with&no&
authors
(4)
0.156
0.534
6,985
388
18
11
0.097
0.357
29,322
236
124
54
0.092
0.000
250,330
254
986
54
0.086
0.000
372,831
75
4958
252
Notes: Regions are defined by the 306 Dartmouth Atlas Hospital Referral Regions (HRRs). For each drug,
regions are partitioned into four groups, corresponding to the four columns in the table. Statistics are then
reported for each column by aggregating over the set of drugs in our sample. Reported statistics reflect drug
utilization over the first two years following initital introduction. Data on drug utilization comes from
Medicare claims 1998-2008.
Introduction
Setting and Data
Analysis
Sorting
Empirical Strategy
• Central idea: exploit variation in the geographic location of lead study authors
across multiple new cancer drugs
• Empirical framework analogous to a diff-in-diffs setting
• Compare drug utilization in first author and other regions
• Control for baseline differences in region’s propensity to use new cancer drugs
• Baseline specification:
(drug)ijtd = {HRR × disease-group FEs}ijd + {drug × year FEs}dt
+ βf 1(First author HRR)jd + βo 1(Other author HRR)jd + εijtd (1)
– Patient i treated in region j, t years after drug d approved by FDA
– Disease groups: hematologic cancers, urologic cancers, other carcinomas
Evolution of Investigator Proximity Effect
Introduction
Setting and Data
Analysis
Sorting
Baseline Investigator Proximity Effects
Table&3:&Author&Proximity&Effect&on&Drug&Utilization
Dependent'variable:'(drug)_id'in'{0,1},'indicates'receipt'of'new'cancer'drug'd'by'patient'i
Proximity&Measures
First&author&HRR
Other&author&HRR
Number&of&observations
Panel&A:&&All&
HRRs
(1)
Panel&B:&&Author&
HRRs&only
(3)
Panel&C:&&New&
Cancer&Patients
(5)
0.0404***
0.0383***
(0.0131)
(0.0122)
0.0399***
(0.0154)
0.0069
0.0068
0.0059
(0.0048)
(0.0051)
(0.0053)
659,468
286,637
393,618
(drug)ijtd = {HRR × disease-group FEs}ijd + {drug × year FEs}dt
+ βf 1(First author HRR)jd + βo 1(Other author HRR)jd + εijtd
Introduction
Setting and Data
Analysis
Sorting
Baseline Investigator Proximity Effects: Author Roles
• Patients treated in the first author’s region are 35% more likely to receive the
new drug
• We find a smaller (insignificant) effect of proximity to other authors on the
clinical trial relative to first effect
• Studies average 14 authors per paper (range: 6-26)
• Authors are typically associated with a clinical trial center; we omit research
scientists with the sponsoring drug company from the analysis
Introduction
Setting and Data
Analysis
Sorting
Baseline Investigator Proximity Effects:
Why are there frictions in diffusion?
• Conversations with oncologists suggest that prescribing a new drug can be
risky and uncertain
• Trial participants are typically the “healthiest cancer patients”: tolerating the
drug is a major concern
• Awareness of heterogeneous treatment effects may make individual success
stories more compelling than median benefits
• Uncertainty about optimal dosing regimes may lead to worse initial results
• According to doctors from the Mayo Clinic and Dana Farber, trastuzumab first
introduced, median survival with treatment was 25 months; now with longer
regimens and better management, the same drug achieves 41 months median
survival (NYT 2014).
Introduction
Setting and Data
Analysis
Sorting
Is Investigator Influence Limited to Own Practice Group?
Dependent'variable:'(drug)_id'in'{0,1},'indicates'receipt'of'new'cancer'drug'd'by'patient'i
Proximity)Measures
First)author)HRR)&)in)author)group
Panel)A:))All)HRRs
(1)
Panel)B:))Author)
HRRs)only
(2)
Panel)C:))New)
Cancer)Patients
(3)
0.0421***
0.0417**
(0.0125)
(0.0124)
(0.0162)
First)author)HRR)&)nonIauthor)group
0.0416**
0.0392**
0.0409**
(0.0211)
(0.0195)
(0.0203)
Other)author)HRR)&)in)author)group
0.0276***
0.0286***
0.0271***
(0.0074)
(0.0073)
(0.0082)
Other)author)HRR)&)nonIauthor)group
,0.0031
,0.0033
,0.0036
(0.0054)
(0.0058)
(0.0057)
659,468
286,637
393,618
Number)of)observations
0.0421***
Introduction
Setting and Data
Analysis
Baseline Investigator Proximity Effects: Author Roles
• Higher use in first author region–even outside his group!
• Suggestive that local opinion leader matters
• In other author regions, higher use inside the author’s physician group
• No evidence of spillovers outside of the other author’s group
• Less prominent authors don’t have the broad scope of influence
Sorting
Introduction
Setting and Data
Analysis
Baseline Proximity Effects: Possible Mechanisms
• Do drug companies play a role?
• 9/21 drugs in our sample report financial disclosures
• For these drugs, 52% of all clinical authors report financial ties to drug co,
compared to 67% of first authors
• Drug companies are 1.3x more likely to have tie to first author, but proximity
impact to author is 5.5x larger in first author’s region
• Drug co investment may be complementary to the superior information or
professional stature that the first author already offers.
• Oncologists also may share ideas through:
• internal drug treatment protocols and “tumor board” meetings
• invited “grand rounds” seminars
• local and regional professional society meetings
• contact through shared patients and patient referrals
• casual interpersonal networks
Sorting
Introduction
Setting and Data
Analysis
Sorting
Effect of Proximity to Superstar Investigators
Dependent variable: (drug)_id in {0,1}, indicates receipt of new cancer drug d by patient i
Independent variables:
First author HRR
(1)
0.0300**
(0.0124)
Top 50% cited author HRR
(2)
0.0227**
(0.0090)
Top 10% cited author HRR
Number of observations
(3)
(4)
0.0246**
(0.0119)
0.0154*
(0.0081)
0.0232**
(0.0122)
659,468
659,468
659,468
659,468
(5)
0.0242**
(0.0111)
0.0100
(0.0108)
(6)
0.0230**
(0.0110)
0.0144
(0.0090)
0.0034
(0.0117)
659,468
659,468
Notes: These regressions test whether "superstar" authors are more influential than other study authors for the same
drug;
to that end,
the baseline regression
is augmented
include a vectorwith
of (drug)*(any
author HRR) fixed
• These
regressions
includespecification
drug fixed
effectstointeracted
”any author”
effects. Reported coefficients describe whether regions with authors of the noted type have higher new drug use
indicator (equals 1 if region contains any position trial author)
compared to the rest of the author regions for the same drug. Top 50% and top 10% authors are defined as the most
prominent
academic
authors for each
drug,us
as measured
by citation
counts
to publications
produced
over the
10
• Reported
coefficient
tells
how much
more
theaccruing
superstar
author’s
region
uses
years leading up to FDA drug approval in the relevant field. All regressions include drug year fixed effects; HRR cancer
to other
regions
withurologic,
non-superstar
authors
typethe
fixeddrug
effects compared
defined using three
categories
of cancer drugs:
hematologic, and
other (including breast,
colon,
lung, and brain);
indicators for
patient age,
race,
sex,new
and new
cancer more
treatment
episode.
See notes
to Table 3
• Regions
with and
superstar
authors
use
the
drugs
than
regions
with
for further details. *: p<0.10; **: p<0.05; ***: p<0.01.
non-superstar authors
Introduction
Setting and Data
Analysis
Sorting
Do local leaders matter more in slow-adopting regions?
Dependent'variable:'(drug)_id'in'{0,1},'indicates'receipt'of'new'cancer'drug'd'by'patient'i
Independent'variables:
First*author*HRR
Regional*technology*intensity
(1)
(2)
0.0409***
0.0385***
(0.0112)
(0.0105)
First*author*HRR***Fast*adoption*index
)0.0215**
)0.0197***
(0.0084)
(0.0077)
Other*author*HRR
0.0064
0.0063
(0.0047)
(0.0050)
Other*author*HRR***Fast*adoption*index
)0.0042
)0.0035
(0.0052)
(0.0053)
No
659,468
Yes
286,637
Sample
Restricted*sample?
Number*of*observations
• Being treated in a first author HRR increases probability of new drug receipt by
6.2 p.p. for regions that are typically 1 SD slower to adopt than average
• Being treated in a first author HRR increases probability of new drug receipt by
1.9 p.p. for regions that are typically 1 SD faster to adopt than average
Introduction
Setting and Data
Analysis
Sorting
Do study authors influence practice in neighboring regions?
Independent'variables:
First+author+HRR
Neighbor+region+drug+adoption
(3)
(4)
Of
(5
0.0402***
0.0402***
0.0006
(0.0131)
(0.0130)
(0.000
Neighbor+of+first+author+HRR
0.0017
0.0014
(0.0066)
(0.0066)
Other+author+HRR
0.0066
0.0067
0.0002
(0.0048)
(0.0049)
(0.000
)0.0031
)0.0030
(0.0030)
(0.0031)
No
659,468
Yes
547,256
Neighbor+of+other+author+HRR
Sample
Restricted+sample?
Number+of+observations
No
7,712,
• No evidence of impact on neighboring HRRs
• 95% CI bounds effect on neighbor of first author HRR at 1.4 p.p., or a third as
large as the within-region effect
Introduction
Setting and Data
Analysis
Do study authors increase off label drug use?
Independent'variables:
First*author*HRR
Other*author*HRR
OffIlabel*drug*use
(5)
(6)
0.0006
0.0007
(0.0005)
(0.0005)
0.0002
0.0003
(0.0004)
(0.0004)
No
7,712,248
Yes
3,063,237
Sample
Restricted*sample?
Number*of*observations
• No evidence of impact on off-label applications (imprecise)
• Mean utilization is 0.37 p.p. among off-label patients
• 95% CI bounds the effect as no larger than 0.16 p.p.
Sorting
Introduction
Setting and Data
Analysis
Proximity Channels: Patient Travel vs. Adoption
• For our baseline results, patients matched to provider regions where care
delivered
• First author effect on a region could be driven by two separate channels
(1) Increased propensity to treat a fixed population of patients with new drug
(2) Sorting effect, where different types of patients seek out treatment in first
author region
Sorting
Introduction
Setting and Data
Analysis
Sorting
Patient Travel and Proximity Effects
Dependent variables:
Independent variables:
First author HRR
Travel
(1)
(2)
0.0329* 0.0309*
(0.0192) (0.0197)
Traveler to first author HRR
New drug use
(3)
(4)
0.0327*** 0.0311***
(0.0124) (0.0116)
0.0224
0.0226
(0.0140) (0.0138)
Other author HRR
0.0295*** 0.0285*** 0.0066
(0.0094) (0.0089)
(0.0052)
Traveler to other author HRR
0.0012
(0.0061)
Sample
Author HRRs only?
Number of observations
No
659,468
Yes
286,637
No
659,468
0.0064
(0.0056)
0.0011
(0.0064)
Yes
286,637
Notes: Columns 1 and 2 report results from regressions where the dependent • First author’s region treats 3 p.p. more patients traveling from outside the HRR
variable indicates whether patient received care outside the patient's HRR of • Patients who do travel into the first author’s region slightly more likely to
residence. Columns 3 and 4 report results from regressions where the receive drug than residents (not significant)
Introduction
Setting and Data
Analysis
IV Estimates of Proximity Effect on Drug Utilization
Use IV strategy to test whether new drug is prescribed more in author
regions, holding the patient population fixed.
The two endogenous variables are indicators for:
• treated in the first author HRR
• treated in other author HRR
The two instrumental variables are indicators for:
• residence in the first author HRR
• residence in the other author HRR
Exclusion restriction: where patient lives is uncorrelated with his
suitability or demand for treatment with a new chemotherapy drug, after
conditioning on included FEs.
Sorting
Introduction
Setting and Data
Analysis
Sorting
IV Estimates of Proximity Effect on Drug Utilization
Table&6:&IV&Estimates&of&Proximity&Effect&on&Drug&Utilization
Outcome:&new&drug&use
(1)
(2)
(3)
A.#Reduced#form:#drug#receipt#effect
&&&&&Residence&in&first&author&HRR
&&&&&Residence&in&other&author&HRR
0.0228**
(0.0099)
0.0038
(0.0044)
0.0217**
(0.0099)
0.0044
(0.0050)
0.0228**
(0.0099)
0.0046
(0.0061)
0.0035
(0.0045)
,0.0028
(0.0028)
0.0293**
(0.0127)
0.0056
(0.0058)
0.0259**
(0.0114)
0.0060
(0.0061)
0.0263**
(0.0115)
0.0065
(0.0061)
&&&&&Residence&in&first&author's&neighbor&HRR
&&&&Residence&in&other&author's&neighbor&HRR
B.#Two#Stage#Least#Squares#
&&&&&Provider&in&first&author&HRR
&&&&&Provider&in&other&author&HRR
Sample
&&&&Author&HRR&only?
&&&&Number&of&observations
No
659,468
Yes
286,637
No
659,468
Notes:&Reduced&form&results&report&coefficients&from&3&regressions&where&the&outcome&
variable&is&new&drug&use&and&the&key&explanatory&variables&are&indicators&for&whether&a&
given&patient&resides&in&the&same&region&as&the&study&author&(or&in&column&3,&in&a&
neighboring&region).&In&Panel&B,&the&&Two&Stage&Least&Squares&results&use&patient&residence&
variables&as&instrumental&variables&for&whether&the&patient&is&treated&in&author&region.&All&
Introduction
Setting and Data
Analysis
Sorting
How big is this effect?
• About 2500 Medicare FFS patients would be treated with each new drug if all
regions match the first author’s region’s utilization patterns
• This amounts to 53,000 Medicare FFS patients in total who were not treated
with new drugs, for the 21 drugs we study
• Dr Edith Perez (Mayo Clinic): “Usually we see two months of [median
survival] improvement” from a new cancer drug (NYT 2014)
• Using estimates of efficacy reported by the UK’s NICE and national SEER
incidence statistics:
• Trastuzumab (for breast cancer): utilization would increase by 12,500 patients
in 2 years, with median survival increasing by 4 months for treated patients
relative to prior best practice
• Bortezomib (for multiple myeloma): utilization would increase by 1200, with
median survival increasing by 6 months for treated patients relative to prior best
practice
• Cetuximab (for colorectal cancer): utilization would increase by 7700 patients in
2 years, with median survival increasing by 4 months
Introduction
Setting and Data
Analysis
Conclusion
1
New drug utilization more intensive in regions close to drug’s investigators
• Patients 30% more likely to receive drug if treated in first author’s HRR
• First authors are quite influential even outside their physician group
• Other authors only influence adoption within their group
2
Proximity effect diminishes over time
• No evidence of investigator proximity effect after 4 years
• Theories of geographic variations differ in convergence predictions
– Initial information frictions don’t spark long-run differences in specialization
3
Patient sorting results
• Patients appear more likely to travel to investigator regions
• IV strategy exploiting patient residence shows proximity effect on drug
utilization not primarily due to patient sorting
Sorting
PI HRR for given drug
Other PI HRRs
temozolomide
valrubicin
pemetrexed
bortezomib
ibritumomab
trastuzumab
bevacizumab
arsenic
capecitabine
gemtuzumab
fulvestrant
tositumomab
oxaliplatin
alemtuzumab
decitabine
denileukin
temsirolimus
−.1
.1
.2
Propensity to use drug
0
.3
.4
Drug-Specific Investigator Proximity Effect