The Duke Stroke Policy Model (SPM) - PowerPoint PPT Presentation

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The Duke Stroke Policy Model (SPM)

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Greg Samsa, PhD -- project director, statistician. Giovanni Parmigiani, PhD -- statistician, software developer ... 'To me, decision analysis is just the ... – PowerPoint PPT presentation

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Title: The Duke Stroke Policy Model (SPM)


1
The Duke Stroke Policy Model (SPM)
2
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

3
Outline
  • Rationale for modeling ()
  • SPM described
  • Applying the SPM to a randomized trial
  • Extensions

4
Rationale for modeling
  • Why model?
  • Arguments for modeling
  • Arguments against modeling
  • Discussion
  • Conclusions
  • Application to stroke

5
Why model? (contd)
  • 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, . . .

6
Why model? (contd)
  • 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
  • (contd)

7
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

8
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

9
Disadvantages of modeling
  • Ignores subjective nuances of patient-level
    decision-making
  • Problem may be incorrectly specified
  • Inputs may be incorrect / imprecise
  • Usual outputs are difficult to interpret or
    irrelevant to decision-makers

10
Individual decision-making 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).

11
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.

12
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.

13
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.

14
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.

15
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.

16
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, event-free survival,
    QALY, ).

17
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.

18
Application to acute stroke treatment
  • RCTs follow patients in the short-term, 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.

19
Outline
  • Rationale for modeling
  • Stroke model described ()
  • Applying the SPM to a randomized trial
  • Extensions

20
SPM described
  • History / background
  • Types of analysis
  • Structure
  • Validation / citations

21
SPM history / background
22
SPM development (contd)
  • 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

23
SPM development
  • SPM was used
  • To summarize epidemiology of stroke
  • To support CEA
  • As a basic organizing structure for the PORT

24
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)

25
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
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