Tuesday, October 25, 2011: 10:15 AM
Columbus Hall C-F (Hyatt Regency Chicago)
(MET) Quantitative Methods and Theoretical Developments

Candidate for the Lee B. Lusted Student Prize Competition

Ankur Pandya, MPH, Harvard University, Boston, MA, Thomas Gaziano, MD, MSc, Harvard Medical School, Boston, MA and Milton C. Weinstein, PhD, Harvard School of Public Health, Boston, MA

Purpose: In cost-effectiveness analyses (CEAs) involving continuous decision variables (such as screening rates or treatment thresholds), the strategies being evaluated are generally pre-specified using arbitrary thresholds or round numbers.  The objective of this study was to evaluate the potential gains in welfare, defined by average net monetary benefit (NMB), from direct-search optimization of continuous decision variables (cardiovascular disease [CVD] screening/treatment thresholds) compared to solely focusing on pre-specified strategies.

Method: We used a CVD micro-simulation model to estimate the lifetime health benefits (quality-adjusted life years [QALYs]) and screening, treatment, and event costs under various multi-staged screening/treatment strategies for a representative cohort of 10,000 adults (aged 25-74 years) in the U.S. without history of CVD.  Screening/treatment strategies were defined by the numbers of individuals receiving non-laboratory-based or cholesterol-based risk assessment, and by the proportions of individuals ultimately receiving lipid-lowering and/or blood pressure treatment.  In total, 36 age- and sex-specific continuous decision variables collectively defined any screening/treatment strategy.  Fifty pre-specified strategies were determined based on commonly-used treatment thresholds and/or plausible screening/treatment cutoffs that spanned a considerable range of the decision variable space.   These strategies were compared to an optimized set of decision variables that was determined using the Nelder-Mead algorithm, a direct-search method that aimed to maximize average NMB (discounted at 3%, using a willingness-to-pay [WTP] value of $100,000/QALY).   Common random numbers were employed to produce stable results across model runs.

Result: Among the pre-specified strategies, the optimal option under conventional incremental CEA rules yielded discounted per-person averages of 20.422 QALYs, costs of $12,734, and average NMB of $2.0295 million.  The corresponding results from the direct-search optimization were 20.419 QALYs, costs of $11,456, and average NMB of $2.0305 million.  Extrapolated to the relevant U.S. population eligible for primary CVD prevention (~136 million adults), the total difference in average NMB between these approaches would be >$130 billion.

Conclusion: We found that direct-search optimization of multistage CVD screening/treatment thresholds resulted in meaningful gains in welfare (average NMB) compared to a traditional CEA of pre-specified strategies.  Future CEA studies involving many (>10) continuous decision variables might also benefit from employing direct-search or other optimization algorithms, although the gains in NMB should be weighed against potential losses from increased complexity of model results and subsequent clinical guidance (i.e., nuanced screening/treatment guidelines).