Examples of using Adjusted model in English and their translations into Portuguese
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Ecclesiastic
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The first adjusted model with four latent variables,
We used a Logistic Regression Adjusted Model for risk factor analysis
The interaction terms were inserted into the adjusted model for the other potential confounding variables.
Likewise, those variables with p>0.25 in the adjusted model were excluded from the analysis of the next blocks.
The variables that presented a value of p¿0, 20 in univariate analysis were selected and in the adjusted model were kept only the ones that.
credible interval of 95%(dashed line) to log the baseline risk of the adjusted model for mortality.
Figure 2 shows the baseline risk graphs of the adjusted model in each of the cohorts.
The results of the adjusted model 1 are in Table 1 and the results of the adjusted model 2, considering the 8 items presenting DIF
The evaluation of the adjusted model used a probability of up to 0.5 as a cut-off point to classify the individuals as 0 non-self-medication,
The explanation coefficient of the adjusted model R explained 94% of the composition of the arrival time to the first health service among men Table 2,
As observed in Table 3, even with the multivariate logistic regression adjusted model, the risk of mortality has also shown to be dose-dependent on the number of allogeneic PRBCs units transfused.
Variables presenting p≤ 0.05 in the crude analysis were selected to enter the adjusted model, and they remained in the model
thereby accepting the adjusted model, in which the closer to 1.0 the p-value is, the better the quality the adjustment of the model. .
were able to elaborate adjusted model with the use of multiple logistic regression,
The non-association of anemia in the adjusted model with other variables that translate the socioeconomic status might be explained by the fact that it was represented in the adjusted model by other proxy variables, like housing condition.
The explanation coefficient of the adjusted model for the length of stay in the healthcare network also best explained the variation in the time to arrive at the referral hospitals in cardiology,
multiple linear regression of MIP and MEP in relation to gender, age, respiratory mode, and forward head posture, the best adjusted model for MIP adjusted R 60.4% included only the variables respiratory mode and forward head posture.
To obtain death prognostic factors we used the regression logistic multivariate adjusted model[16], contemplating the variables that were showed in univariate previously performed,
ordinary covariance matrix is from the covariance matrix of the adjusted model, or from the magnitude of the hiatus in discrepancy functions of both the hypothetical model
All adjusted models included sex,
