Monday, October 21, 2013
Key Ballroom Foyer (Hilton Baltimore)
Poster Board # P2-42
Quantitative Methods and Theoretical Developments (MET)
Candidate for the Lee B. Lusted Student Prize Competition
Hawre Jalal, MD, MSc1, Jeremy D. Goldhaber-Fiebert, PhD2 and Karen M. Kuntz, ScD1, (1)University of Minnesota, Minneapolis, MN, (2)Stanford University, Stanford, CA
�� Purpose: Regression metamodeling (RM) is
a useful technique for efficiently revealing parameter sensitivities in a model
in terms of marginal effects of each parameter on policy-relevant outcomes
using the output from probabilistic sensitivity analysis (PSA). The present
study examined the performance of RM when model parameters are correlated.�
�� Methods: �A metamodel is a statistical model
that can summarize model parameter sensitivities from PSA by regressing model
outcomes on the model input parameters.� Coefficients from RM can be
interpreted as changes in the model outcome due to a change in the
corresponding input.� Decision models with two or more parameters that are
highly correlated may present an important limitation of RM, as collinear
variables do in multivariate regression. �Increased correlation in RM parameters
may widen the confidence intervals of the coefficient estimates. We used a
previously published model of treating herpes simplex encephalopathy, where the
outcome is the expected utility of undergoing a brain biopsy.� We incorporated a
correlation between two of the model parameters: the probability of dying
because of biopsy (pDieBiopsy) and the probability of developing severe complications
following biopsy (pSevereBiopsy).� We ran 10,000 PSA simulations for each hypothetical
correlation level between these two parameters, varying the correlation value (rho)
from 0 to 1.� We then examined the precision of the estimated RM coefficients
at each value of rho.�
�� Results: �The figure shows the RM
coefficients of pDieBiopsy and pSevereBiopsy (solid line), and their confidence
intervals (gray region) for various correlation coefficient values.� The
negative coefficients indicate that an increase in the value of either parameter
results in a reduction in the expected utility of biopsy.� The confidence intervals
maintains the same width at various correlation levels except for near exact correlation
(i.e., when rho = 1).� We found similar results with other correlated
parameters.�
�� Conclusion: We found RM to accurately
predict parameter sensitivities except when these parameters are in near
perfect correlation.� In these situations, including correlated parameters in
the model may be unnecessary because one of the correlated parameters can be
expressed as a function of the other one. Using standard regression diagnostics
(e.g., the condition index) to identify situations of high multicollinearity
may be appropriate when performing RM. ��
