The Duke Stroke
Policy Model (SPM)
TIA
IS
HS
DTH
ASY
MI
Bleed
Developers
 David
Matchar, MD -- principal investigator
 Greg Samsa, PhD -- project director,
statistician
 Giovanni Parmigiani, PhD -- statistician,
software developer
 Joe Lipscomb, PhD -- health economist
 Greg Hagerty, MS -- software developer
Outline
Rationale
for modeling (*)
SPM described
Applying the SPM to a
randomized trial
Extensions
Rationale for modeling
 Why
model?
 Arguments for modeling
 Arguments against modeling
 Discussion
 Conclusions
 Application to stroke
Why model? (cont’d)
“To me, decision analysis is just the
systematic articulation of common
sense: Any decent doctor reflects on
alternatives, is aware of uncertainties,
modifies judgements on the basis of
accumulated evidence, balances risks
of various kinds, . . .”
Why model? (cont’d)
“ considers the potential
consequences of his or her
diagnoses and treatments, and
synthesizes all of this in making a
reasoned decision that he or she
decrees right for the patient…”
(cont’d)
Why model?
“… All that decision analysis is asking
the doctor to do is to do this a lot
more systematically and in such a
way that others can see what is going
on and can contribute to the decision
process.” -- Howard Raiffa, 1980
Advantages of modeling
 Clarifies
decision-making
 Simplifies decision-making
 Provides comprehensive framework
 Allows best data to be applied
 Extrapolates short-term observations
into long-term
 Encourages “what if” analyses
Disadvantages of modeling
 Ignores
subjective nuances of patientlevel decision-making
 Problem may be incorrectly specified
 Inputs may be incorrect / imprecise
 Usual outputs are difficult to interpret or
irrelevant to decision-makers
Individual decisionmaking is subjective
 For
individual decision-making, primary
benefit of modeling is clarification.
 As normative process, decision-making
works better for groups.
– Most applications involve group-, rather
than individual-level, decisions (e.g., CEA,
purchasing decisions, guidelines).
An aside
 Interactive
software (possibly
including models) shows great
potential to help decision-makers
(e.g., patients, physicians, pharmacy
benefits managers) clarify and make
better decisions.
– We are developing prototype for a
“user-friendly version” of the SPM.
Some models are mis-specified
A
good model will simplify without
over-simplifying.
 Poor models exist, but this need not
imply that modeling itself is bad.
 We need more explicit standards
under which models are developed,
presented, and assessed.
An observation
 The
fundamental problem with many
of the poor models in circulation is
that they assume the answer they are
purporting to prove
(often, that a treatment which is trivially
effective or even ineffective is
nevertheless cost-effective).
 Users
are understandably wary.
Model inputs may be incorrect/
imprecise
 This
problem is often most acute for
utilities and costs, and least acute for
natural history and efficacy.
– We need more and better data on cost and
quality of life.
 The
less certain the parameter, the
greater the need for sensitivity analysis.
An aside
 In
practice, the conclusions of a
model / CEA are never stronger
than the strength of the evidence
regarding efficacy.
 If the evidence about efficacy is
weak, then modeling / CEA should
not be performed.
Usual outputs are difficult to interpret
 In
academic circles, results are
presented as ICERs using the societal
perspective.
– Present this as a base case for purposes of
publication / benchmarking.
– Also present multiple outcomes from
multiple perspectives (vary cost categories,
vary time periods, present survival, eventfree survival, QALY, …).
General conclusions
 Modeling
is of great potential benefit and
indeed is sometimes the only reasonable
way to proceed. However, models must
be held to a high standard of proof.
 Although the standard reference model
cannot be ignored, modeling should be
done flexibly, with the needs of the end
user in mind.
Application to acute stroke treatment
 RCTs
follow patients in the shortterm, but the large majority of
benefits accrue in the long-term.
 Simple heuristics will not suffice to
adequately trade off complex risks,
benefits, and costs.
 Modeling allows a large body of
evidence to be efficiently synthesized.
Outline
Rationale
for modeling
Stroke model described (*)
Applying the SPM to a
randomized trial
Extensions
SPM described
History
/ background
Types of analysis
Structure
Validation / citations
SPM history / background
SPM development (cont’d)
 First
version developed in 1993 by Stroke
PORT
 Goals of stroke PORT:
– Summarize epidemiology of stroke
– Describe best stroke prevention practices
– Describe current practices, and test
methods for improving practice
SPM development
SPM was used:
To summarize epidemiology
of stroke
 To support CEA
As a basic organizing
structure for the PORT
SPM versions
 Original
C++ code (uses waiting time
distributions, research tool, difficult to
extend)
 New S+ code (uses waiting time
distributions, highly structured code
used as development tool, inefficient)
 New C++/Decision-Maker code (uses
Markov-based cycles, intervention
language, better interface, extendable)
New C++ version
 Decision-Maker
used to specify
natural history and effect of
interventions in a decision tree
format
 Efficient C++ code used as
simulation engine
 Expandable into a web-based tool