The cost models and the underlying study data are critical steps in the overarching aim of identifying optimally cost-efficient studies of biomechanical exposure.
Testing with five users is thought to be optimally cost-effective 25 as the method quickly identifies areas in which navigation or page layout need improving.
Then, the general model is simplified, and procedures are derived for identifying optimally cost-efficient exposure assessment strategies, depending on the shapes of cost functions.
The determination of the boldface strategy in section a was illustrated in figure 4. As illustrated in table 2, the optimally cost-efficient strategy in many scenarios is to obtain data on one occasion from as many subjects as possible.
A few studies take on the more challenging task of determining the optimally cost-efficient strategy at a certain budget, on the basis of specified costs for collecting data at different stages, and specified sizes of the corresponding variance components.
Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components.
Determination of variance components requires, as a minimum, duplicate samples at each stage of the measurement model [ 5], and this may often not be an optimally cost-efficient strategy if the objective is to get a precise group mean (cf. table 2a-c; cases with n d = 1).
Since both bias and precision can, under a number of assumptions, be expressed as mathematical functions of variance components and the number of measurements [ 18], it might be possible to develop closed-form solutions to the problem of finding optimally cost-efficient measurement strategies, but this has not so-far been pursued.
Obviously, both for regression metrics, exceedance, and ICCs, optimally cost-efficient exposure assessment strategies may deviate from those driven by the objective of obtaining precise exposure means, as illustrated by two studies on optimal measurement allocation in reliability studies [ 69, 70].
For pre-emptive vaccination the optimally cost-effective duration of social distancing depended on the pandemic severity: for severe pandemics sustained social distancing was the most cost effective, while for moderate pandemics 8 weeks of social distancing was more cost effective than sustained social distancing.
With this information the user can then make a more informed decision about how to carry out their pooling experiment to optimally balance cost with loss of power.
