Sentence examples for calculation for testing from inspiring English sources

Sample size calculation for testing the primary hypothesis (alveolar collapse is reduced after PSV compared to PCV in a model of experimental pulmonary ALI in rats) was based on effect estimates obtained from pilot studies.

In addition, because almost all published trials fail to carry out a specific power analysis and sample size calculation for testing moderators, lack of power is ubiquitous, and absence of statistical significance should not be regarded as evidence of absence of an effect [ 23].

The sample size calculation for testing the primary hypothesis (the gene expression of IL-6 in lung tissue is increased with BD HVT/LPEEP compared to BD LVT/OLPEEP) was based on effect estimates obtained from pilot studies as well as on previous measurements by our group (mean value and dispersion, respectively).

Based on noncentral t-distributions, we derive some sample size calculation formulas for testing equality, testing therapeutic noninferiority/superiority, and testing therapeutic equivalence, under the popular one-sample design, two-sample parallel design, and two-sample crossover design.

Inspired by Tang and Tang [ 18], we also propose the exact unconditional, approximate unconditional and Bootstrap-resampling p-value calculation procedures for testing three-arm non-inferiority with small sample sizes.

However, formal sample size calculation for test-retest analysis or correlation analysis is rarely done and was not carried out in the present study.

Vanted offers a series of tests for calculation of statistical parameters, for testing the normal distribution of datasets (David Quicktest [ 47]) and for outlier detection (Grubbs test).

*Protein BLAST calculation (blastp algorithm, NCBI tools) for testing similarities between the three pulmonate MT isoform subfamilies, exemplified by CdMT, CuMT and Cd/CuMT from H. pomatia, C. aspersus and B. glabrata.

Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calculate displacement factor precisely and reliable precision can be obtained which meets engineering requirements.

AutoMal additionally empowers unsupervised learning, by executing various bunching calculations for tests gathering.

Additionally, this case elucidates the fact that the non-invasive nature and thus modified benefit-risk calculation for cffDNA testing may undermine the integrity of the informed consent process, even when tests are provided solely through healthcare professionals [ 55].