New Incentive Approaches to
Adherence
Stephen Kimmel, MD, MSCE
Andrea Troxel, ScD
Center for Clinical Epidemiology and
Biostatistics
The Importance of Clinical Trials to
Study Adherence Interventions
• Observational studies prone to bias
– Patient selection
– Temporal trends
– Unmeasured confounders
• Effects of intervention may not be “symmetrical”
– e.g., increasing copayments for medications may not
have the same effect as reducing copayments
• Logistics
– You can learn a lot just from developing an
intervention
Adherence worsens among those facing copayment
increases…but also among patients without copays
Source: Doshi, Zhu, Kimmel, Volpp et al. Circulation
Reducing co-pays appears to have modest
effects in quasi-experimental studies
Source: Chernew ME, et al. Health Affairs 2008;27(1):103-12.
$5  $0 for generics
$25  $12.50 for preferred drugs,
$45  22.50 for non-preferred drugs
4
But, Randomized Trial of Copay
Reduction: No Impact. . .
•RCT among veterans with poorly
controlled blood pressure
•Average SBP on study entry: 160
•Copayments reduced from $8 per
month to $0
• No significant effect on blood
pressure or medication adherence in
either study
Source: Volpp, Troxel, Kimmel , Doshi et al. 2010
Novel Approaches to Adherence
• Framework: Behavioral Economics
• Most commonly used approaches
– Financial incentives
• patients
• clinicians
– Reminders
– Pre-commitment mechanisms
Adherence to Warfarin
% Population
40% of patients were
>20% non-adherent
Either missed or extra dose
Underadherence by Under-AC
Odds Ratio (CI)
P<0.0001
% Missed Pills
Reference group: No missed pills
Kimmel et al. Arch Intern Med. 2007;167:229-235.
Adherence Changes Over Time
Percent of Days Non-Adherent
30%
20%
10%
0%
1
3
6
9
Month After Beginning Warfarin Use
12
Lottery-Based Incentives: Conceptual
Framework
• Deliver frequent feedback and rewards – ideally at
the daily level, because of present-biased
preferences
• Lotteries give more bang for the buck, in part
because people overweight small probabilities
• Give frequent positive feedback plus the hope of big
payout
– Take advantage of time discounting
– Use variable reinforcement
• Utilize regret as a motivator
Lottery-based financial incentive based on daily
medication use
• Eligible for daily lottery prize if the medication monitoring
device has registered that patient took medication the
previous day
• If patient did not take medication s/he will be informed that
s/he would have won the lottery that day had s/he taken the
medication properly
• High probability small reward & low probability big reward
• Takes advantage of present-biased preferences (immediacy of
receiving coupon/gift card), overestimation of probabilities,
regret aversion, and variable reinforcement
Incentives for medication adherence
using daily lotteries
– Warfarin is an anti-stroke
medication with large
benefits but non-adherence
rates high (chronic,
asymptomatic condition)
– Designed lottery with 1 in 5
chance of winning $10 a
day, 1 in 100 chance of
winning $100 each day IF
took warfarin previous day
Volpp, Loewenstein, Troxel, Doshi & Kimmel, BMC
Health Services Research, 2008
12
The Warfarin INcentives (WIN) Pilot Trial
P<0.001
P=0.4
P-value for
interaction=0.0016
WIN2 Trial
WIN2 Trial Design
• Balanced randomization, stratified by
– Baseline INR status
– Study site
• Five comparisons of interest
– Each active arm against control (3)
– Combined arm against each single arm (2)
– Bonferroni adjustment of alpha = 0.01
WIN2 Power/Sample Size
• Binary outcome: out-of-range (OOR) INR
• Detect a 30% reduction in occurrence of OOR
INRs from 0.43 to 0.3
– Assume 10% drop-out by six months
– Assume ICC of 0.05
– Assume 7 INR assessments per subject
WIN2 Analysis Plan
• Generalized linear mixed models
– Logit link
– Fixed effects for time, treatment, other timeinvariant covariates
– Random intercepts and slopes
– Exploratory modeling of shape of time effect
– Time x treatment interactions
– Explore adjustment for frequency of assessment
Segmenting or Tailoring
• How can we improve effectiveness (and costeffectiveness) of these interventions?
• Should we (can we) only target non-adherent
patients?
• Can we tailor our interventions?
- Upfront screening of RCT patients at baseline for
reason for non-adherence with tailored
intervention
- Sequential Multiple Assignment Randomized
Trials (SMART)
Tailoring
• Evaluate subjects at baseline regarding drivers of
non-adherence (cost, distraction/forgetfulness,
knowledge/beliefs/attitudes)
• Develop strategy tailored to each driver (copay
reduction, lottery-based reminder,
education/instruction)
• Consider randomizing “tailored strategy” vs “onesize-fits-all strategy” vs “usual care”
SMART Designs
Sequential Multiple Assignment Randomized Trials
• Work by Murphy, Robins, Thall, and many others
• Optimize response by varying interventions on the
basis of time-dependent information
- In cancer therapy, randomize patients to initial therapy;
then randomize responders to maintenance therapy and
non-responders to second-line therapy
- In adherence, randomize subjects to primary strategy;
then maintain adherent subjects on same strategy and
randomize non-adherent subjects to remaining options
Summary
• Use ideas from behavioral economics to inform
interventions
• Use appropriate methodology from clinical trials to
properly assess hypotheses of interest
• Promising area for further development of adaptive
treatment and tailoring strategies
• LDI Center for Health Incentives (Kevin Volpp,
Director) is leading multiple trials in diverse areas of
behavioral health