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Because of this modest concordance between observed and predicted events, the Gail model is currently of limited practical usefulness for obtaining risk estimates for any given individual.
In studies which require data linkage, some vulnerable and high risk populations are more likely to be excluded due to lack of personal identifiers or difficulty in obtaining risk factor information.
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Modified Poisson regression with adjustment for matching and clustering were then performed to obtain risk ratios.
A mixed method uses both quantitative and qualitative analysis, to obtain risk values.
For performing FMEA, the proposed approach handles limitations of traditional FMEA procedure to obtain risk priority number (RPN).
After the individual risk distribution is obtained, risk zones are divided according to corresponding individual risk value of HSE, and land-use planning suggestions are proposed.
Data analysis was performed using Stata 8. Binomial regression was used to obtain risk differences between treatments and 95% confidence intervals.
Controlling for cigarette smoking by using pack-years did not result in substantive changes in obtained risk estimates.
We used conditional logistic regression to calculate odds ratios and obtained risk differences with population averaged general estimating equations.
We calculated and obtained risk enzymes from the above expression profile of foam cells and macrophages, separately.
As stated in the results, the obtained total risk score is almost equal to the risk probability in percent, in our view making interpretation of the obtained risk score relatively straightforward even without plotting values on the curve.
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