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Evaluate the metric on simulated data to determine whether it is informative across a large range of data values.
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As Dataset M is involved in this experiment, in addition to verifying the performance of the proposed method with a larger range of data, the above results demonstrate that a classification made by this method generally corresponds to that by manual examination, which involves a considerable amount of manual labor and time.
We consider random forest multiple imputation to be promising, but it should be tested on a larger range of data sets and in simulations to explore whether it gives unbiased estimates where there are nontrivial nonlinearities or interactions in imputation models, such that a standard parametric MICE imputation which ignores them gives biased results.
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The TLG metric appears to be more robust in either scheme of post-filtering despite a slightly larger variation in the amount of change, which may be less of a problem considering the large range of TLG data observed.
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The purpose of simulations is to estimate power and false-positive rate on a large range of simulated data sets, in order to compare the tests under simple and sometimes extreme situations.
In our study, VL hiPSC-CMs and hAdultV-CMs action potentials were simulated using the most comprehensive human models consistent with a large range of human data.
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