Sentence examples for multiple risk estimates from inspiring English sources

Several papers showed multiple risk estimates for the same exposure with different analytical approaches.

If an article only reported multiple risk estimates by subgroup, these estimates were input separately into our meta-analysis.

Multiple risk estimates were listed separately for each study and identified separately for differing outcomes and drugs, for example, all bisphosphonates/alendronate and category of cancer: (1) esophageal, (2) gastric or (3) UGI cancer.

If multiple risk estimates (with error measurements) were presented in a given manuscript (e.g., nested multivariable models), the estimate that most closely adjusted for only demographic characteristics (e.g., age, sex, race, socioeconomic indicators, and marital status/household composition) was selected.

16 Multiple risk estimates were extracted from most publications, because risk of CHD or CVD was provided for a range of fibre subtypes and sources as well as for total dietary fibre.

If multiple risk estimates were presented in a given manuscript, the unadjusted estimate was selected for the primary meta-analysis as some studies were adjusted for prominent confounding variables, such as family history and adiposity, while others were not, rendering a direct comparison of estimates to be questionable.

Where studies reported multiple relative risk estimates within a study, we extracted the most coronary-specific risk and 95% CI adjusted for the most number of variables.

In addition, we used RRs which were derived from recent meta-analyses [ 4– 7] evaluating multiple studies with risk estimates that were adjusted for several confounders, including lifestyle-related risk factors.

To the extent that physicians and patients in current clinical practice think about individual risk factors rather than combinations of multiple risk factors or global risk estimates, our use of a risk factor list as a comparator may underestimate the expected effects of the decision aid in actual clinical practice.

A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual.

Although MDR doesn't allow for adjustments of covariates, it does control for multiple comparisons and spurious risk estimates by using a cross validation and permutation testing scheme as a built-in feature.