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TRA-2-1
PS2-13
VALUE OF INDIVIDUALIZED INFORMATION IN COST-EFFECTIVENESS ANALYSIS: WHEN IS AN OUTCOME PREDICTION MODEL WORTH USING?

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** Purpose: **
Because risk often varies across patients enrolled in randomized trials,
average population trial and cost-effectiveness analysis (CEA) results do not
apply to many patients. Risk prediction models make it possible to incorporate
individual risk and clinical effectiveness information to compute
individualized CEA and identify patients for whom therapy is most appropriate.
The expected value of individualized care (EVIC) refers to the incremental monetized
value of customizing care in this manner, compared to a uniform recommendation
(treat all or none) based on the population average incremental
cost-effectiveness ratio (ICER). We explore factors influencing EVIC.

** Methods: ** We developed a general framework to calculate individualized
ICERs as a function of individual outcome risk. For a case study (tPA vs.
streptokinase to treat possible myocardial infarction), we used simulation to
explore how EVIC is influenced by risk model discriminatory power (c-statistic),
population outcome prevalence, model calibration and willingness-to-pay (WTP)
thresholds. We characterized individual risk using beta distributions, the
parameters for which we estimated from population prevalence and the risk model
c-statistic. We derived this relationship empirically from a database of 25
large randomized clinical trials. We assumed that treatment effect
(relative risk reduction) and life expectancy do not vary according to patient
risk.

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** Results: **For unbiased models (figure), EVIC is
always non-negative and improvements in discrimination better targets
treatment, increasing EVIC. In our example, lower population outcome
prevalence also increases EVIC. EVIC also depends on the WTP threshold; when
the WTP and population average ICER are close, EVIC increases because
individualized information more often changes treatment decisions. In contrast
to unbiased prediction models, miscalibrated models can introduce mistakes and
hence have a negative EVIC. When the population average ICER is near the WTP
threshold, decision making is more sensitive to bias and these “mistakes” are
more common (making EVIC more negative). In simulated examples, EVIC decreased
for model c-statistic values of 0.6-0.7 and increased for c-statistic values of
0.8-0.9.

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** Conclusions: ** In
general, higher predictive model c-statistic values produce higher EVIC
values. This benefit is greatest when the population ICER is near the WTP
threshold. When models are miscalibrated, greater discriminating power can
paradoxically reduce the EVIC under some circumstances.

Figure: EVIC as a function of c-statistic at several levels of outcome prevalence and WTP thresholds