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The factors affecting the survival rate were identified using Cox's proportional-hazard model.
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The cancer detection rate was identified using the administrative claims and defined as the number of women with two breast cancer claims at least 30 days apart during the 6 months following the index screening event; ancillary or laboratory claims alone were not sufficient to establish new cancer cases.
To overcome this issue, several variants with dramatically improved binding rates were identified using a semi-rational strategy, protein ligand binding complex modeling, site-saturation mutagenesis, and HTS for faster binding kinetics.
Fewer FSAs with high rates were identified using the smoothed SIRs compared to the non-smoothed SIRs; nonetheless, in the central and south-central parts of the city, both methods identified many of the same high-rate FSAs.
The wards that had the most influence on the magnitude of significant (P<0.05) rate ratios were identified using Cook's influence statistic (McCullagh and Nelder, 1989).
Significant departures in evolutionary rate post-duplication were identified using the relative rates test [ 80, 81].
For lung cancer, 105 cases were identified in the prevalent screen by a screening program (detection rate of 0.33%), and 58 cases (detection rate 0.18%) were identified using PET.
Among all primary PET studies (Table 1), 640 cancer cases were identified in the prevalent screen by a screening program (detection rate of 2%), and 363 cases (detection rate of 1.13%) were identified using PET.
Peaks indicating regions with sufficient signal above peak height threshold (false discovery rate < 0.05) were identified using the PeakSeq algorithm [ 19].
Prognostic factors associated with response rate and survival were identified using univariate and multivariate logistic and/or Cox proportional hazards analyses.
For calculating the ratio of non-synonymous (Ka) and synonymous substitution rates (KS), orthologous proteins were identified using Mauve.
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