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In the present retrospective study, the prognostic impact was evaluated in terms of progression-free survival which, by being an earlier end point than overall survival, was considered more appropriate when modelling the prognostic significance of drug transporters which are expected to influence treatment outcome and to control drug efficacy.
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We present and discuss some statistical issues relevant to the paper by Taylor and colleagues, who conducted studies to model the prognostic potential of gene expression changes that occur after endocrine treatment.
For this aim, longitudinal studies would be of high interest to model the prognostic power of different levels of identity development on subscale level as well as possible changes over time.
Subgroups for analysis will be modelled by the prognostic factors mentioned.
In all models, the prognostic score was always included and the additional utility of UAPI assessed.
In both the Cox and Fine and Gray models the prognostic factors were tested by two-sided Wald tests.
Despite the increasing number of trauma registries and the benchmarking models, the prognostic ability of these models hardly addresses individual patients in the clinical setting.
In multivariate models, the prognostic significance of a variable depends on the prognostic significance of MVD values using Mab to CD105 and vice versa.
In the Cox regression model, the prognostic factors for OS in all of the patients were AJCC stage, breast cancer subtype, adjuvant chemotherapy, and CD151 overexpression.
As demonstrated in the multivariate Cox models, the prognostic capability of both caspase-3 forms was independent on established prognostic factors – Gleason score and pathological T stage – indicating that they may help to identify patients at high risk of progression.
In the final model, the prognostic factors that showed statistically significant effects were analyzed according to subgroups to determine the effect of each stratum on survival time for breast cancer patients.
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