Biomedical Engineering Reference
In-Depth Information
from the same physician panel. ImpactRx data are not projected to national level.
These data are ideal for measuring treatment responses to various promotional tools
at the individual physician level.
ImpactRx collects sample usage data as a part of physician treatment activities.
The data reports the type of sample treatment prescribed by physicians (i.e., whether
it is new prescription treatment with samples, renewal prescription treatment with
samples, or sample only treatment), as well as the amount of samples dispensed on
each prescription occasion measured by sample counts and sample days of therapy.
Each sample or prescription treatment has a unique diagnosis code (ICD-9) so that
a treatment can be identifi ed for a specifi c medical condition.
As a measure of sales reps' detailing activities, ImpactRx data recorded sample
signature data starting in October of 2010. Physicians reported sample signature
signed as well as vouchers and coupons distributed to each doctor during each
product detailing.
17.4
Literature Review of Academic Research
on Pharmaceutical Sampling
In this section we review existing academic studies on prescription sampling from
both the marketing and medical literature. We fi rst summarize prior literature on the
effects of free drug samples on physicians' prescription choices and prescription
drug sales. We then discuss previous studies that examine the drivers of physicians'
free drug sample dispensing behavior and explore the roles free samples play in
physician's prescription decisions.
17.4.1
The Effects of Free Drug Samples on Prescription
Choice
The vast majority of the existing marketing studies on the effects of free drug sam-
ples have focused on evaluating the impact of free samples on prescription drug
sales or physician's prescription choice.
Gönül et al. (
2001
) studied the effects of detailing and sampling on an individual
physician's prescription decision. They found that sampling had a positive effect on
physician's prescription decisions, but this effect had diminishing returns. Using a
large pooled time series of cross-sectional data involving three drugs and 74,075
individual physicians, Mizik and Jacobson (
2004
) also fi nd that detailing and free
drug samples have statistically signifi cant positive effects on the number of monthly
new prescriptions issued by a physician. However, the magnitudes of these effects
are modest compared to the fi ndings in previous studies.
Using individual level panel data, Manchanda et al. (
2004
) analyzed physician's
prescription decisions with a hierarchical Bayesian framework. They suggest there
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