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Evaluating the Impact of Treatment Effectiveness and Sideeffects on Prescription Decisions: The Role

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Title: Evaluating the Impact of Treatment Effectiveness and Sideeffects on Prescription Decisions: The Role


1
Evaluating the Impact of Treatment Effectiveness
and Side-effects on Prescription Decisions The
Role of Learning and Patient Heterogeneity
  • Tat Chan
  • Chakravarthi Narasimhan
  • Ying Xie
  • Washington University in Saint Louis
  • May 11, 2007
  • Yale Conference on Consumer Insights

2
Medicines are never harmless!
  • In September 2004, Merck Co. (MRK ) pulled its
    blockbuster painkiller Vioxx from the market
    because a study linked it to heart attacks and
    strokes.
  • All drugs have risk does the benefit exceed the
    risks?
  • How would physician and patient trade off the
    benefits (effectiveness of a treatment) and the
    risks (side effects) of a drug?

3
Conceptual Framework
effectiveness
switch/stay
detailing
Physician Patient
switching reasons
Side effects
Prescription choice
Patient feedback
First prescription
cost
4
Identifying Effectiveness and Side Effects
  • Two data sources
  • Market outcomes treatment choice for each
    physician-patient pair
  • Self-reported reasons for switching treatments
    ineffective or side effects
  • Manski (2004), BLP (2004)
  • (1) infers the overall evaluation of treatments
  • (2) identifies the treatment effectiveness and
    side effects across treatments as well as the
    heterogeneity in their impacts
  • Model Match of market outcomes and self-reported
    switching reasons to identify the impacts of
    effectiveness and side effects on prescription
    choices

5
Data
  • Prescription and Promotion Data available for a
    physician panel for the Erectile Dysfunction
    Category from Sept. 2003 to Oct. 2004
  • Two new drugs launched during this period
    (Levitra in August 2003, and Cialis in November
    2003)
  • 828 physicians, 13619 prescriptions
  • 7324 prescriptions to revisiting patients, with
    1652 switched treatment
  • Reported reasons for switching prescriptions, and
    to which other drugs these patients switched
  • Observed promotional activities
  • 26509 detailing visits 4648 detailing visit with
    a meal
  • Observed patient characteristics severity,
    insurance status, ethnicity, age

6
Switching Reasons
  • Among 1652 visits where the revisiting patients
    switched the drug treatments
  • 929 ineffectiveness
  • 161 side effects
  • 365 patient request
  • 198 other reasons such as marketing
  • Assumptions
  • Effectiveness and side effects of previous drug
    known by patients and physicians
  • Model the probability of switching due to
    ineffectiveness vs. side effects
  • Patient request or other reasons either due
    to ineffectiveness or side effects

7
The Model
  • Physician i will choose the alternative drug j
    that provides the highest expected utility for
    patient h at occasion t conditional on the
    physicians information ,

Past detailing, Past patient feedback
  • New patient
  • Ongoing patient who used j before same drug
    prescribed if
  • Otherwise switch treatment

8
The Model
  • Physician i will choose the alternative drug j
    that provides the highest expected utility for
    patient h at occasion t conditional on the
    physicians information ,

effectiveness
  • New patient
  • Ongoing patient who used j before same drug
    prescribed if
  • Otherwise switch treatment

9
The Model
  • Physician i will choose the alternative drug j
    that provides the highest expected utility for
    patient h at occasion t conditional on the
    physicians information ,

Side effects
  • New patient
  • Ongoing patient who used j before same drug
    prescribed if
  • Otherwise switch treatment

10
The Model
  • Physician i will choose the alternative drug j
    that provides the highest expected utility for
    patient h at occasion t conditional on the
    physicians information ,

Patient characteristics detailing (persuasive)
  • New patient
  • Ongoing patient who used j before same drug
    prescribed if
  • Otherwise switch treatment

11
The Model
  • Physician i will choose the alternative drug j
    that provides the highest expected utility for
    patient h at occasion t conditional on the
    physicians information ,

  • New patient
  • Ongoing patient who used j before same drug
    prescribed if
  • Otherwise switch treatment

Switching cost
12
Model Specification
  • Model Assumption
  • Heterogeneity of drug effectiveness and side
    effects across patients
  • Effectiveness and side effects may be correlated
    across drugs

13
Model Specification switching reasons
  • Suppose a revisiting patient h switches from drug
    j to drug k on occasion t, and states side
    effects as the switching reason, two additional
    condition hold in this case

i.
ii.
We then estimate the joint probability of i, ii,
and iii
14
Model Specification Learning
  • As in Erdem and Keane (1996)
  • Prior Belief
  • Learning from two sources
  • Detailing
  • Feedback from Ongoing Patients
  • Bayesian Learning

15
Preliminary Results effectiveness and side
effects
Switching cost 2.6687
16
Preliminary Results persuasive detailing
17
Preliminary Results Patient characteristics
  • Cialis is more likely to be prescribed to older
    patients
  • Cialis is more likely to be prescribed to white
  • Levitra is less likely to be prescribed to
    African American, and more likely to white
  • Levitra is more likely to be prescribed to
    patients with Medicare

18
Summary
  • Tease out the effect of treatment effectiveness
    and side effects on prescription choice
  • The next step to identify the role of patient
    feedback and detailing in driving physicians
    learning of treatment effectiveness and side
    effects

19
Policy Experiments
  • Competitive relationship across drugs
  • Own- and cross-elasticities of prescription share
    due to change of detailing effort
  • How much due to patient-physician evaluation of
    effectiveness vs. side effects?
  • How much due to heterogeneity in effectiveness
    and side effects as well as their correlations
    across drugs?
  • Time-varying informative role of detailing
  • Through both detailing and own prescription
    experience, physicians learn more about
    effectiveness and side effects of new drugs
  • Informative role of detailing may decline
    overtime
  • Can that explain the declining detailing efforts
    observed in data?
  • What is the optimal detailing strategy at the
    physician level overtime?

20
  • Backup slides

21
Prescription Choice
Detailing
Prescription Choice
Physician Influence
Learning Effectiveness Side effects
Effectiveness
Patient Influence
Side effects
Out of pocket cost
22
Research Objective
  • The Role of Treatment Effectiveness and Side
    Effects
  • How does the heterogeneity of treatment
    effectiveness and side effects across patients
    affects treatment choices?
  • The Role of Detailing
  • Persuasive function detailing, detailing with
    meals
  • Informative function
  • Uncertainty of effectiveness and side effects of
    new drugs
  • detailing, detailing with meals
  • Narayanan et al(2006)
  • How does detailing perform the persuasive and
    informative functions?
  • How does detailing affect competitive
    relationships overtime?

23
Prescription Trend
24
Detailing Trend
25
Model Specification
  • Mean Effectiveness (by severity) and Side
    Effects
  • Model Assumption
  • Heterogeneity of drug effectiveness and side
    effects across patients
  • Effectiveness and side effects may be correlated
    across drugs

26
Model Specification switching reasons
  • Suppose a revisiting patient h switches from drug
    j to drug k on occasion t, and states side
    effects as the switching reason, two additional
    condition hold in this case

i.
ii.
Therefore, we estimate the following joint
probability
which can be expressed as conditional probability
as follows
27
Preliminary Results effectiveness and side
effects
28
Preliminary Results without learning
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