Sentence examples for multiple covariates in from inspiring English sources

The proportional hazards model was applied to analyze the effect of single and multiple covariates in predicting whether TB developed.

The Cox proportional hazards model was applied to analyze the effect of single and multiple covariates in predicting cancer development.

Even larger n will be required in order to estimate the effect of multiple covariates in the same meta-regression model.

The HFW cohort contains of a relatively large set of white and African-American participants, a long follow-up and multiple covariates in the tumour-related, lifestyle and sociodemographic domains.

The strengths of the study include comprehensive measures of smoking status, as well as a composite measure of duration and intensity, a prospective population-based cohort design, a relatively large set of participants, a long follow-up and our ability to take into account multiple covariates in the tumor-related, lifestyle, and socio-demographic domains.

The strengths of the study include comprehensive measures of functional limitations and functional decline, a prospective population-based cohort design, a relatively large set of white and African-American participants, a long follow-up and our ability to take into account multiple covariates in the tumour-related, lifestyle and sociodemographic domains.

However, loss of precision (and hence of statistical power) through the use of ANOVA or CSA is likely to be greater with imbalance in multiple covariates than with imbalance in a single covariate, as there will normally be a greater proportion of variance in the outcome measure that is unaccounted for by either of these analyses.

Similarly, both the timing of interventions tested in current animal models and the lack of appreciation of multiple covariates (multiple hits) in clinical practice preclude the meaningful progress of translational research in acute lung injury (ALI).

If multiple covariates are included in the linear model (see Equation 1 in Theobald and Freeman, 2014), then interaction terms need to be included for each of the covariates in the model.

We used this approach for inference, instead of generalized additive models (GAM) (Hastie and Tibshirani 1990), because multiple covariates were included in the model and small effects were expected a scenario in which GAM may overestimate measures of association and underestimate standard errors (Dominici et al. 2002).

After adjustment for multiple covariates, the RRR in those with an MPR ≥ 80% versus those with an MPR <80% was 16% [ 13].