Sentence examples for multiple factors adjusted from inspiring English sources

Individuals who had prior TB episodes but did not complete treatment have higher probability of discontinuing their current treatment episodes after adjusting for multiple factors (adjusted Odds Ratio (aOR): 4.77 [95% CI: 1.98 – 11.53]) (Table  2).

The normal-errors longitudinal regression model (29) assessed differences between glycemic exposure groups in the mean of the Framingham risk score and individual CVD risk factors adjusted for multiple comparisons, adjusting for baseline components (TC, HDL-C, smoking status, and SBP), demographics (sex, age at randomization, and race/ethnicity), and treatment group.

To our knowledge, this is one of the first studies to use multivariate techniques (which require a large sample size) to evaluate the relative importance of multiple factors while adjusting for possible confounding.

We compare the results from the usual equally weighted multiple window spectrogram as well as an optimal scaling factor-adjusted multiple window spectrogram.

†Cox regression models, adjusted for multiple factors of disability.

The Cox proportional hazards regression model was used to control for multiple factors simultaneously and to estimate adjusted hazard ratios (HRs) and 95% confidence intervals.

Further analysis jointly adjusted for multiple factors (Table 3) confirmed these findings with small changes in the magnitude of covariate effects except for mean LDL, which became nonsignificant.

These conservative assumptions underestimated the relative risk (toward the null) and overestimated the width of the CI (i.e., by doubling the variance to approximate a 95% CI adjusted for multiple factors).

There were no statistically significant differences between recent SJW users and non-SJW users in satisfaction with their general practice or in trust in their general practitioner (GP) when adjusted for multiple factors.

A strength of this study is the fact that we were able to study a larger primary care sample, and adjusted for multiple factors associated with a greater risk of incident diabetes.

After adjusting for multiple factors, Nunes et al. demonstrated that vegetation length was the only independent predictor of in-hospital mortality [7].