Recently, Sung et al. [1] obtained an exponential inequality for identically distributed and acceptable random variables, and their result improved the corresponding ones of Kim and Kim [2], Nooghabi and Azarnoosh [3], Sung [4], Xing [5], Xing et al. [6], and Xing and Yang [7].
To deliver experiment-sharing capabilities (an "experiment" is defined as a set of connected raw data variables, analyses and their results), the platform implements a team-based sharing mechanism.
However, longitudinal studies of these variables are rare, and their results are somewhat conflicting.
Most of the variables were non-normally distributed, and their results are shown as median (+ SEM) in Fig. 2 and median with interquartile range in Tables 1 and 2. For normally distributed variables, mean (+ SEM) is used in Fig. 2 and mean ± SD is used in the tables.
All of these variables and their interplay result in the observation that ISB is often seemingly "irrational" to the outside observer.
Thus the additional moments can be reconstructed using the variables and their first moments, resulting in a very substantial savings in computer memory.
There was no matching of the patients according to age and duration of illness to control for demographic and clinical variables and their impact on the results.
These patient characteristics were examined in separate blocks as they were considered to be theoretically highly linked to the dependent variables, and their inclusion together may result in a reduction of the significance of the other demographic factors which we wished to assess.
Finally, in interpreting observational studies that exhibit significant differences between participants and non-participants, clinicians and researchers should be aware of differences in important prognostic variables and their possible impact on study results.
The authors have implemented a new software package--called SPRIT--consisting of a heuristic as well as an exact algorithm, applied it to several data sets of variable size, and compared their results and running times with those obtained from other algorithms that have previously been developed to analyze reticulate evolution.
How might they predict that the different variables might alter their results?
