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But understanding survival data is not straightforward.
However, although this statistical interpretation of the previous three years' survival data is published every year by the National Congenital Heart Disease Audit, these reports are hard to find, and not necessarily accessible or understandable for everyone.
The success/survival data and further subgroup analysis for implant survival data is presented below.
Their application in survival data is narrowed to univariate models, which partition the feature space with axis-parallel hyperplanes, meaning that each internal node involves a single feature.
The algorithm is proposed in Section 3 and tested on synthetic data in Section 4. The application of the algorithm to real microbial survival data is the subject of Section 5 along with conclusions.
Once the GLD fits to survival data is attained, other summary statistics such as hazards can be easily obtained and will be consistent with the same underlying survival distribution and the distributional shape of survival times can be easily gleaned by plotting the fitted GLD.
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Survival data are censored at one year.
Clinicopathological and survival data were correlated with the staining results.
In parallel, growth and survival data were collected.
Survival data was analyzed with Kaplan Meier survival analysis.
Fish infection survival data were analyzed using a Logrank Test.
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