Assessing the Performance of a Computer-Based Policy Model of HIV and AIDS
Cotich, Kara L.
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CitationRydzak, Chara E., Kara L. Cotich, Paul E. Sax, Heather E. Hsu, Bingxia Wang, Elena Losina, Kenneth A. Freedberg, Milton C. Weinstein, and Sue J. Goldie. 2010. Assessing the Performance of a Computer-Based Policy Model of HIV and AIDS. PLoS ONE 5(9): e12647.
AbstractBackground: Model-based analyses, conducted within a decision analytic framework, provide a systematic way to combine
information about the natural history of disease and effectiveness of clinical management strategies with demographic and
epidemiological characteristics of the population. Among the challenges with disease-specific modeling include the need to
identify influential assumptions and to assess the face validity and internal consistency of the model.
Methods and Findings: We describe a series of exercises involved in adapting a computer-based simulation model of HIV
disease to the Women’s Interagency HIV Study (WIHS) cohort and assess model performance as we re-parameterized the
model to address policy questions in the U.S. relevant to HIV-infected women using data from the WIHS. Empiric calibration
targets included 24-month survival curves stratified by treatment status and CD4 cell count. The most influential
assumptions in untreated women included chronic HIV-associated mortality following an opportunistic infection, and in
treated women, the ‘clinical effectiveness’ of HAART and the ability of HAART to prevent HIV complications independent of
virologic suppression. Good-fitting parameter sets required reductions in the clinical effectiveness of 1st and 2nd line HAART
and improvements in 3rd and 4th line regimens. Projected rates of treatment regimen switching using the calibrated cohortspecific
model closely approximated independent analyses published using data from the WIHS.
Conclusions: The model demonstrated good internal consistency and face validity, and supported cohort heterogeneities
that have been reported in the literature. Iterative assessment of model performance can provide information about the
relative influence of uncertain assumptions and provide insight into heterogeneities within and between cohorts.
Description of calibration exercises can enhance the transparency of disease-specific models.
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