Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning

Efeito da conexidade de dados sobre a acurácia dos testes de progênie e performance Data connectedness effect on the accuracy of progeny and performance tests

Dados simulados foram utilizados para verificar o efeito da conexidade sobre a acurácia dos testes de progênie e performance. O genoma simulado foi constituído de uma característica quantitativa governada por 500 locos. Simulou-se o efeito de rebanho (9 rebanhos) significativo a 5% de probabilidade pelo teste F. Foram simulados arranjos de dados com 0, 15, 30, 60, 90 e 100% de conexidade para herdabilidades 0,10; 0,30; e 0,60 e para diferentes tamanhos de progênie (9, 54 e 90 progênies/reprodutor). A cada geração foram selecionados nove machos e o número de fêmeas selecionadas variou de acordo com o número de progênies considerado em cada arranjo de dados. A seleção foi efetuada em dez gerações, sendo este processo repetido por 30 vezes. O uso de dados com baixa conexidade reduziu a acurácia dos testes considerados, e a maior importância da conexidade ocorreu para baixa herdabilidade e reduzido tamanho da progênie. Apesar de ter sido observado efeito da conexidade, a herdabilidade e o tamanho da progênie tiveram maior influência na avaliação genética.<br>Simulated data were used to verify the effect of the connectedness on the accuracy of progeny and performance tests. The simulated genome was formed from a quantitative trait governed by 500 loci. Significance herd effect (9 herds) by 5% probability F test was simulated. Data sets with 0, 15, 30, 60, 90 and 100% of connectedness for heritabilities 0.10, 0.30 and 0.60 and for different progeny sizes (9, 54 and 90 progenies/sire) were simulated. In each generation nine males were selected and the number of selected females shifted according to the number of progenies considering in each data arrange. The selection was accomplished in ten generations, where the process was repeated 30 times. Data with smaller connectedness reduced the accuracy of both tests and the connectedness was more important for small heritability and small progeny size. The heritability and the progeny size had larger influence in the genetic evaluation than the effect of connectedness