Monday, October 19, 2015
Grand Ballroom EH (Hyatt Regency St. Louis at the Arch)
Poster Board # PS2-13

Natalia Olchanski, MS, Joshua T. Cohen, Ph.D., Peter J. Neumann, Sc.D., John B. Wong, MD and David M. Kent, MD, MSc, Tufts Medical Center, Boston, MA


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.


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.


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