This paper shows that, contrary to what is generally believed, decreasing concavity of the agent's utility function with respect to the screening variable is not sufficient to ensure that stochastic mechanisms are suboptimal.
Recruitment using a one-stage procedure depends on the screening variable X only.
In a separate sensitivity analysis we therefore weighted the screening variable based on the simulated screening effects by age and time since screening provided by the CISNET Cancer Interventionn and Surveillance Modeling Network) Stanford simulation model.
The screening variable was set as 1, if the woman underwent breast surgery in an area offering mammography screening in the relevant period and age group, and set to 0 otherwise.
The interactions between the screened variables were then evaluated using central composite design.
A further advanced statistical approach, central composite design, found the optimum levels of the screened variables as follows (g l−1): glycerol 17.6, glutamic acid 59.6; yeast extract 2.7; K2.7O4 2.7.
The screened variables were lipid concentration (X1), surfactant concentration (X2), drug loading (X3), lecithin concentration (X4), charge-inducing agent concentration (X5), homogenization time (X6), and sonication time (X7).
Given the short time frame so far investigated, there are important outcomes whose relationships to the screened variables remain unclear.
The reflections on the screen variable consisted of three conditions: reflections across the whole screen (scenario 1), no reflections on the screen (scenario 2), and reflections on half of the screen (scenario 3).
The cognitive screening variables (MMSE or MoCA) at baseline were entered in the second block and the NIHSS scores at baseline were entered in the third block (Model A1 and B1 for MMSE and MoCA, respectively).
