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The vaccination coverage in a girls catch-up programme below which the boys vaccination strategy (assuming maximum expected benefit from boys vaccination) is modelled to lead to more CC prevented than the addition of a catch-up programme was estimated for different vaccine efficacy and vaccine duration of protection.
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Within EVSI, the costs of the trial are compared with the benefits achieved to find the maximum expected net benefit, which would correspond with the recommended trial size.
The difference between the expected net benefit obtained using perfect information and the expected net benefit obtained in the presence of uncertainty (that is, the maximum expected net benefit obtained with less than perfect information) is known as the expected value of perfect information (EVPI).
The maximum expected monetary benefit or minimum expected cost rule are often adopted to assess the optimum structural design levels under infrequent large earthquakes.
Proposition 1 If the subsidy period T is long enough, for the government's any subsidy policy δ 1 / δ 2, there is only one optimal decision η ∗ ∈ ( 0, 1 ), which makes the manufacturer's expected benefit E maximum.
The above analysis indicates that, given the government's subsidy policy δ 1 / δ 2, if the subsidy period T is long enough, there must exist only one optimal decision η ∗ ∈ ( 0, 1 ) which makes the manufacturer's expected benefit E maximum.
Modeling the problem as a maximum matching on a random graph, we first establish that the expected benefit from pooling scales as the square-root of the number of pairs in each hospital.
So even the expected benefit is overstated.
Another expected benefit is in the enhanced logic functionality.
In addition, the maximum expected net monetary benefit (NMB) was calculated for each ceiling ratio value (the value society would be WTP for a QALY gained).
These expected benefits induce households to continue using various adaptation strategies that provide them with a maximum level of unobserved utility (Barberies 2013).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com