Sentence examples for multivariate models resulted from inspiring English sources
Removing age adjustment from the main multivariate models resulted in substantially larger estimates, very similar to the crude associations.
Table 4 shows five separate multivariate models resulting from forward stepwise regression.
Patients with missing information for variables were excluded from the multivariate models, resulting in models smaller than the total population.
Variables with a p-value <0.15 were retained in the multivariate models, resulting in an internal and external model.
The final multivariate model resulted in 3 factors independently associated with AME (Table 3).
Adding MR-proANP to the multivariate model resulted in smaller increases in the Harrell C values and in lower IDI values.
Replacing COPD with FEV1 % predicted in the multivariate model resulted in the decreasing level of FEV1 being a significant risk factor for death, while heart disease was not a significant risk factor for death in any of the models.
In general, confounding was defined when inclusion of a variable in the multivariate model resulted in a change of more than 15percentt in odds ratios of factors already present in the model.
It was also an independent prognostic factor: addition of LHR insLQ carriership to the multivariate model resulted in an increase of χ2 from 36.28 to 42.70 (Δχ2 = 6.42 (df = 1), P = 0.01) for DFS.
In these premenopausal patients the LHR insLQ genotype was an independent prognostic factor: addition of LHR insLQ carriership to the multivariate model resulted in an increase of χ2 from 44.06 to 52.23 (Δχ2 = 8.17 (df = 1), P = 0.004) for DFS.
Confounders were identified as Table 2 characteristics that met each of the following conditions: They preceded the exposure window, differed by > 10% between exposed and unexposed participants, and was an independent predictor of the outcome of interest (< 0.05), and inclusion in a multivariate model resulted in a > 10% change in the effect estimate for PCE exposure.
