Exemplos de uso de Cox regression model em Inglês e suas traduções para o Português
{-}
-
Medicine
-
Colloquial
-
Official
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
A Determined from Cox regression model.
The Cox regression model is usually used to analyze time-to-event data.
Results for endpoint survival using the Cox regression model.
The Cox regression model was used to identify predictors of restenosis.
Table 5 shows the results of the Cox regression model applied to the MET-AMI variable.
The gross andadjusted relative risk of death was estimated by the Cox regression model.
The Cox regression model was used to analyze breastfeeding duration, adjusted for covariates.
Table 2: Results for endpoint survival using the Cox regression model.
Cox regression model was applied to the variables associated with survival on bivariate analysis p< 0.20.
Results of univariate and multivariate analysis by Cox regression model are given in Table 3.
P-value from Cox regression model, stratifying for site of disease and prior anti-adjuvant therapy at screening.
The impact of each treatment on survival was calculated using a Cox regression model.
The Cox regression model and the Kaplan-Meier method with log-rank test were used to compare survival between groups.
The predictors of death at one-year follow up were determined by Cox regression model.
The second step was to use the Cox regression model for determining which variables were independently associated with mortality.
For late death, in the long-term after surgery,multivariate were assessed by Cox regression model.
Bivariate analysis was carried out through Cox regression model, with 95% confidence interval.
The cox regression model was used to assess the effect of each variable after adjustment to the same levels as the other variables.
We evaluated the effect of each group on the survival at 28 days using the Cox regression model, both unadjusted and adjusted.
The Cox regression model was used for checking the effect of each variable after each was adjusted to the same level Hosmer& Lemeshow, 1999.
To identify surveyed covariates that exerted influence on the time from the monitoring to the outcome,we used the Cox regression model.
In a Cox regression model, baseline sST2 values added significant information regarding first morbid event, death, but no HF hospitalization.
There were no interactions between LVEDP andLVEF regarding this endpoint, as in a Cox regression model that included only those two variables, they both remained significant data not shown.
A cox regression model was used for the hazard ratios, and the kaplan-meier method in order to determine survival estimates. the resulting database had 1800 cases notified in 2007.
To assess the association of hypokalemia with the survival curve, a Cox regression model was adjusted, which included variables presenting a p-value< 0.05 in the univariate assessment Log-rank test.
Cox regression model for the incidence of cumulative ST revealed that, among other parameters, the first generation of DES was an independent predictor in univariate analysis HR 4.61, CI 1.88-11.31, p< 0.001 Table 4.
The survival time between the icu stay andthe occurrence of dai was analyzed using the nonparametric method kaplan- meier and cox regression model to estimate the risk factors of effect on survival time.
Table 4 shows the Cox regression model including all significant variables, excepted binary variables that represent the type of credit union Mutual Credit, Free Admission, or Rural.
The goodness of fit of the logistical regression model was determinedby the Hosmer-Lemeshow test, whereas the assumption of proportional hazards over time in the Cox regression model were estimated by analysis of Schoenfeld residuals.
According to the Cox regression model, only the final TNM stage had influenced survival time, whereas patients with stage III or IV showed the risk of death 2.81 times higher than those in stage I or II p=0.001.