Sentence examples for models for subjects from inspiring English sources
The observed interactions of adiponectin with HbA1c and MIF with smoking could especially add predictive value to cardiovascular risk models for subjects with diabetes that contain these conventional cardiovascular risk factors.
We used logistic regression to examine the independent contributions of current age, age at first visit to the provider site, and sex to 1) ability to match a registry record and 2) among matched records, discrepancy in whether the subject was up-to-date for pertussis-related immunization, constructing separate models for subjects from current user sites and from future direct-EMR-transfer sites.
To assess the impact of family relationships on the prediction accuracy derived from models incorporating pedigree or markers, we calculated the AUC of each of the models for subjects that did not have data from relatives in the training data sets (n = 871) and for those that had at least one relative in the training data set (n= 4248) (see Table 3).
The two-level models for subject i at Time t are as follows: Level 1 (within-subject) DEPRESSION ti = β 0 i + β 1 i STRESS ti + e ti Level 2 (between-subject) β 0 i = γ 00 + γ 01 Time 1 CES − D i + γ 02 5 − HTTLPR i + u 0 i β 1 i = γ 10 + γ 11 5 − HTTLPR i + u 1 i.
For this purpose, we calculated exactly the same statistical ANOVA model for subjects' discrimination abilities within the subsample having answered the tinnitus questionnaire (i.e. all subjects experiencing tinnitus from the electromagnetic hypersensitive and the control group).
However, the values obtained with the 2-CM model for subjects in group 1 differed slightly from the reference data.
Figure 2 shows the adjustment of log (HOMA-IR) according to our proposed parametric model for subjects with levels of Lp(a) >46 and for subjects with levels of Lp(a) ≤46 mg/dl.
The probability model was used in a mixture model (for subjects for whom sex information was missing) together with the observed weights to provide the probability of being male for each individual (NONMEM code in Appendix 1).
The recalibration procedure seemed to work reasonably well for the SAHS model for subjects in the lowest three quintiles; however, the SAHS model still overestimated the number of cases in the two highest quintiles even after recalibration.
To allow for the correlation between repeat LOS values from the same patients a multilevel approach was employed, equivalent to fitting a random-effects model for subjects in addition to the fixed effects model.
The best-fit ISI model for subjects with type 1 diabetes comprises waist circumference, daily insulin dose per kilogram body weight, triglyceride levels, and DBP and explained 63% of the variance in GDR.
