Comparison of response surface methodology and the Nelder and Mead simplex method for optimization in microsimulation models

Microsimulation models are increasingly used in the evaluation of cancer screening. Latent parameters of such models can be estimated by optimization of the goodness-of-fit. We compared the efficiency and accuracy of the Response Surface Methodology and the Nelder and Mead Simplex Method for optimization of microsimulation models. To this end, we tested several automated versions of both methods on a small microsimulation model, as well as on a standard set of test functions. With respect to accuracy, Response Surface Methodology performed better in case of optimization of the microsimulation model, whereas the results for the test functions were rather variable. The Nelder and Mead Simplex Method performed more efficiently than Response Surface Methodology, both for the microsimulation model and the test functions.health;simulation;optimization

Spatial and temporal variations of malaria epidemic risk in Ethiopia: factors involved and implications.

The aim of this study was to describe spatial and temporal variations in malaria epidemic risk in Ethiopia and to examine factors involved in relation to their implications for early warning and interpretation of geographical risk models. Forty-eight epidemic episodes were identified in various areas between September 1986 and August 1993 and factors that might have led to the events investigated using health facility records and weather data. The study showed that epidemics in specific years were associated with specific geographical areas. A major epidemic in 1988 affected the highlands whereas epidemics in 1991 and 1992 affected highland-fringe areas on the escarpments of the Rift Valley and in southern and north-western parts of the country. Malaria epidemics were significantly more often preceded by a month of abnormally high minimum temperature in the preceding 3 months than based on random chance, whereas frequency of abnormally low minimum temperature prior to epidemics was significantly lower than expected. Abnormal increases of maximum temperature and rainfall had no positive association with the epidemics. A period of low incidence during previous transmission seasons might have aggravated the events, possibly due to low level of immunity in affected populations. Epidemic risk is a dynamic phenomenon with changing geographic pattern based on temporal variations in determinant factors including weather and other eco-epidemiological characteristics of areas at risk. Epidemic early warning systems should take account of non-uniform effects of these factors by space and time and thus temporal dimensions need to be considered in spatial models of epidemic risks

Estimting parameters of a microsimulation model for breast cancer screening using the score function method

In developing decision-making models for the evaluation of medical procedures, the model parameters can be estimated by fitting the model to data observed in trial studies. For complex models that are implemented by discrete event simulation (microsimulation) of individual life histories, the Score Function (SF) method can potentially be an appropriate approach for such estimation exercises. We test this approach for a microsimulation model of screening for cancer that is fitted to data from the HIP randomized trial for early detection of breast cancer. Comparison of the parameter values estimated by the SF method and the analytical solution shows that method performs well on this simple model. The precision of the estimated parameter values depends (as expected) on the size of the simulation number of life histories), and on the number of parameters estimated. Using analytical representations for parts of the microsimulation model can increase the precision in the estimation of the remaining parameters. Compared to the Nelder and Mead Simplex method which is often used in stochastic simulation because of its ease of implementation, the SF method is clearly more efficient (ratio computer time: precision of estimates). The additional analytical investment needed to implement the method in an (existing) simulation model may well be worth the effort