A Systematic Review On Cost-effectiveness Studies Evaluating Ovarian Cancer Early Detection And Prevention Strategies

Ovarian cancer imposes a substantial health and economic burden. We systematically reviewed current health-economic evidence for ovarian cancer early-detection or prevention strategies. Accordingly, we searched relevant databases for cost-effectiveness studies evaluating ovarian cancer early-detection or prevention strategies. Study characteristics and results including quality-adjusted life years (QALY), and incremental cost-effectiveness ratios (ICERs) were summarized in standardized evidence tables. Economic results were transformed into 2017 Euros. The included studies (N=33) evaluated ovarian cancer screening, risk-reducing interventions in women with heterogeneous cancer risks and genetic testing followed by risk-reducing interventions for mutation carriers. Multimodal screening with a risk-adjusted algorithm in postmenopausal women achieved ICERs of 9,800-81,400 Euros/QALY, depending on assumptions on mortality data extrapolation, costs, test performance and screening frequency. Cost-effectiveness of risk-reducing surgery in mutation carriers ranged from cost-saving to 59,000 Euros/QALY. Genetic testing plus risk-reducing interventions for mutation carriers ranged from cost-saving to 54,000 Euros/QALY in women at increased mutation risk. Our findings suggest that preventive surgery and genetic testing plus preventive surgery in women at high risk for ovarian cancer can be considered effective and cost-effective. In postmenopausal women from the general population, multimodal screening using a risk-adjusted algorithm may be cost-effective

Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88% of the cases, with large effect sizes for 95% of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95% of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.Comment: in IEEE Transactions on Software Engineerin

Cost effectiveness of breast cancer screening and prevention: a systematic review with a focus on risk-adapted strategies

OBJECTIVES: Benefit and cost effectiveness of breast cancer screening are still matters of controversy. Risk-adapted strategies are proposed to improve its benefit-harm and cost–benefit relations. Our objective was to perform a systematic review on economic breast cancer models evaluating primary and secondary prevention strategies in the European health care setting, with specific focus on model results, model characteristics, and risk-adapted strategies. METHODS: Literature databases were systematically searched for economic breast cancer models evaluating the cost effectiveness of breast cancer screening and prevention strategies in the European health care context. Characteristics, methodological details and results of the identified studies are reported in evidence tables. Economic model outputs are standardized to achieve comparable cost-effectiveness ratios. RESULTS: Thirty-two economic evaluations of breast cancer screening and seven evaluations of primary breast cancer prevention were included. Five screening studies and none of the prevention studies considered risk-adapted strategies. Studies differed in methodologic features. Only about half of the screening studies modeled overdiagnosis-related harms, most often indirectly and without reporting their magnitude. All models predict gains in life expectancy and/or quality-adjusted life expectancy at acceptable costs. However, risk-adapted screening was shown to be more effective and efficient than conventional screening. CONCLUSIONS: Economic models suggest that breast cancer screening and prevention are cost effective in the European setting. All screening models predict gains in life expectancy, which has not yet been confirmed by trials. European models evaluating risk-adapted screening strategies are rare, but suggest that risk-adapted screening is more effective and efficient than conventional screening

Multi-objective improvement of software using co-evolution and smart seeding

Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner