Since random-effect modelling has stringent data assumptions (e.g. sufficient number of groups, distributional assumptions of the random effect), robust alternatives such as generalised estimating equations (GEE) or robust variance estimation are often preferable [ 20– 20].
To evaluate the number of preclinical candidates to emerge in unit time, two simulations were run with different sized working groups under the set of assumptions described above.
One can group the assumptions of AQFT into relativistic axioms, such as locality and covariance, general physical assumptions, like isotony and spectrum condition, and finally technical assumptions which are closely related to the mathematical formulation.
Another group of studies report cautious assumptions of HRQoL improvements [18, 22, 37].
This calculation also allows a 80%% power to prove non-inferiority (non- difference) of the secondary endpoint EQ-5D with a sample of 40 patients in each group with the assumption of an EQ-5D value of 0.73 with standard deviation 0 18.
Sensitivity analyses showed that the findings on the primary outcome in the intervention group were robust to a variety of assumptions.
This article investigates the velocity-free attitude coordinated tracking control scheme for a group of spacecraft with the assumption that the angular velocities of the formation members are not available in control feedback.
Although there was a notion that ibeA might be of predictive value for the presence of B2 strains, its overall irregular occurrence in this group of strains contradicted this assumption [20], [21], [23].
A possible explanation for the evolution of this group of families involves the assumption that one ancestral homolog was present in the genome of the last Apusozoa Opisthokonta common ancestor, which underwent independent diversifications in Apusozoa and Opisthokonta.
The group made assumptions about the behavior of healthcare workers, isolation of patients, effectiveness of preexposure vaccine, vaccine efficacy when given at particular points postexposure, and the like.
To detect a difference of 15% in the proportion of patients stable at six hours with a power of 90% at a 0.05 significance level, the minimum required sample size was 390, with 195 in each group, with an assumption of 5% loss of complete monitoring.
