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In addition to the alternate genotype codings and covariates listed above, tests of heterogeneity between datasets are expected to lead to secondary meta-analyses of results from more homogeneous datasets.
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Using multiple datasets was expected to remedy this effect.
The richer information contained in the multilevel datasets is expected to improve the multiple imputations.
Further analyses on the complete 2-year dataset are expected to confirm these findings.
As data will be collected on three separate occasions for each patient, a robust dataset is expected to identify any significant associations between predictors and patient outcomes.
σPooled, which averages the variances of datasets, also has the risk of measuring the variability between conditions, but in a lesser degree because the heterogeneity of a dataset is expected to be smaller than that of whole GEO database.
Thus, the PSPGO649 dataset is expected to contain many MTPs, but is not completely a membrane-transporter dataset.
Therefore the number of founders for this dataset is expected to be three.
Such datasets can be generated by combining multiple datasets that are expected to be subject to the same bias.
Many more are expected as more datasets are meta-analyzed.
For larger datasets, the differences in computational time are expected to be even greater.
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