Sentence examples for model for adjustments from inspiring English sources

Patient age, CIN grade, at the time of entry, HPV risk category (HPV16/18/31/33/35/45/52/58, other carcinogenic types, or carcinogenic HPV negative), smoking status, parity, OC use, number of lifetime sexual partners, and age at first sexual intercourse were included in the multivariate model for adjustments.

This novel application of a penalized Cox model for adjustment of a trial estimate of treatment efficacy incorporates evidence-based estimates for other therapies, preserves comparisons between the randomized groups, and is applicable to other long-term trials.

3, 4, 9 We considered all these variables when we analyzed the characteristics of the compared groups, using a logistic regression model for adjustment when significant differences were detected in order for the results to be as accurate as possible.

It can be noted that sex was of no prognostic value by univariate analysis, and when this factor was added into the Cox model for adjustment on baseline characteristics, the results remained strictly similar.

Included in our model for adjustment were the NTLP zone where the patient was enrolled, patient category (new or previously treated), age group, and participant residence (rural/urban) (Table  3).

In principle, the "binomial pricing model" allows for adjustments for some of these additional complications.

Semi-parametric Poisson's regression with generalized additive models for adjustment of over-dispersion using SAS version 8.02 was used to model the daily counts of asthma admissions.

We considered two models for adjustment; in both models, maternal age, smoking and alcohol intake during pregnancy, annual household income, parity, and the blood sampling period (categorical in model 1; continuous in model 2) were used as confounding factors.

The covariates were included in multivariable models for adjustment if they modified effect estimates for associations between the exposure variables (CO breath and tube) and the outcome variables (respiratory symptoms and lung function) by at least 5%.

For studies using a logistic regression model for risk adjustment, we will extract adjusted OR and relevant 95% CI for analysis.

To ensure those commands are always being fine-tuned, at the same time, the data gets sent from the server to a model for further adjustment in a lovely virtuous cycle.