Evaluation of mass screening for cancer : a model-based approach

The main goal in evaluation of screening for cancer is to assist in decision making about a screening program: Should it be initiated at all? What screening policies can be recommended: what age groups, what frequency of screening. Should special attention be paid to high risk groups? If a screening program is already running, should screening be continued in view of the results? Should the present policy be changed? In this chapter, I will describe the complexities involved in answering these questions. These difficulties lead to the conclusion that models are indispensable in the interpretation of observed results of screening and in the prediction of effects and costs of different screening policies

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

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

The reproductive lifespan of Onchocerca volvulus in West African savanna

Abstract The epidemiological model ONCHOSIM — a model and computer simulation program for the transmission and control of onchocerciasis — has been used to determine the range of plausible values for the reproductive lifespan of Onchocerca volvulus. Model predictions based on different lifespan quantifications were compared with the results of longitudinal skin-snip surveys undertaken in 4 reference villages during 13 to 14 years of successful vector control in the Onchocerciasis Control Programme in West Africa. Good fits between predicted and observed trends in skin microfilarial loads could be obtained for all villages. It is concluded that the reproductive lifespan of the savanna strain of O. volvulus lies between 9 and 11 years, and that 95% of the parasites reach the end of reproduction before the age of 13 to 14 years

A framework for response surface methodology for simulation optimization

We develop a framework for automated optimization of stochastic simulation models using Response Surface Methodology. The framework is especially intended for simulation models where the calculation of the corresponding stochastic response function is very expensive or time-consuming. Response Surface Methodology is frequently used for the optimization of stochastic simulation models in a non-automated fashion. In scientific applications there is a clear need for a standardized algorithm based on Response Surface Methodology. In addition, an automated algorithm is less time-consuming, since there is no need to interfere in the optimization process. In our framework for automated optimization we describe all choices that have to be made in constructing such an algorithm

Adaptive extensions of the Nelder and Mead Simplex Method for optimization of stochastic simulation models

We consider the Nelder and Mead Simplex Method for the optimization of stochastic simulation models. Existing and new adaptive extensions of the Nelder and Mead simplex method designed to improve the accuracy and consistency of the observed best point are studied. We comparethe performance of the extensions on a small microsimulation model, as well as on five test functions. We found that gradually decreasing the noise during an optimization run is the most preferred approach for stochastic objective functions. The amount of computation effort needed for successful optimization is very sensitive to the timing of noise reduction and to the rate of decrease of the noise. Restarting the algorithm during the optimization run, in the sense that the algorithm applies a fresh simplex at certain iterations during an optimization run, has adverse effects in our tests for the microsimulation model and for most test functions.simulation;health care;programming;Nelder and Mead Simplex Method

Adaptive extensions of the Nelder and Mead Simplex Method for optimization of stochastic simulation models

We consider the Nelder and Mead Simplex Method for the optimization of stochastic simulation models. Existing and new adaptive extensions of the Nelder and Mead simplex method designed to improve the accuracy and consistency of the observed best point are studied. We compare the performance of the extensions on a small microsimulation model, as well as on five test functions. We found that gradually decreasing the noise during an optimization run is the most preferred approach for stochastic objective functions. The amount of computation effort needed for successful optimization is very sensitive to the timing of noise reduction and to the rate of decrease of the noise. Restarting the algorithm during the optimization run, in the sense that the algorithm applies a fresh simplex at certain iterations during an optimization run, has adverse effects in our tests for the microsimulation model and for most test functions

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