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Present study focuses an efficient stochastic finite element method for random response sensitivity analysis.
We present a new method for random testing of binary executables inspired by biology.
Ep-PCR is a fast and cheap molecular biology method for random mutation in a particular piece of DNA.
In this paper, we propose a novel method for random software testing based on the theory of optimal foraging.
Assuming appropriate density probability functions for each random variable and adopting the Latin Hypercube Sampling (LHS) method for random sampling, the input data set has been defined.
This study adopts the acceptance-rejection sampling method, instead of the direct sampling method, for random variable sampling to reduce computing complexity.
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All models were conducted using the DerSimonian and Laird method for random-effects [ 34].
For dichotomous outcomes, pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated based on the Mantel–Haenszel method for random-effects models.
Continuous outcomes were presented as standardized mean differences (SMDs) together with 95% CIs using the inverse variance method for random-effects models.
We assessed post hoc if using a different method for random-effects (Biggerstaff-Tweedie) for the main outcome would produce different results.
For frequentist ITC, we used inverse variance weights to pool results of multiple trials in meta-analysis, and used the DerSimonian-Laird method for random-effects meta-analyses [ 22].
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