Sentence examples for multivariable models adjusting from inspiring English sources

The relationship between body mass index, coronary disease treatment, and survival was assessed via Cox multivariable models adjusting for baseline demographic, clinical, and angiographic characteristics.

We estimated the adjusted relative risk of each outcome (head injury, facial injury, traumatic brain injury, and moderate to severe head or facial injury) separately with multivariable models adjusting for state, year, age, type of crash (single vs. multi-vehicle), intersection, time of day, surface conditions, and helmet law.

We fit multivariable models adjusting for various potential confounders.

These associations, however, became non-significant in multivariable models (adjusting for age, diabetes duration and HbA1c).

These results persisted in multivariable models adjusting for covariables (Table  4).

Multivariable models adjusting for age, sex, and co-morbidity were examined to assess the prognostic significance of each criterion.

Multivariable models adjusted for age and illness severity.

Multivariable models adjusted for age, mean IOP, central corneal thickness, vertical cup-to-disc ratio, and pattern SD.Forty eyes of 31 subjects developed glaucoma during follow-up.

For multivariable models, adjusted odds ratios with 95%% confidence intervals were estimated to assess the strength of the association between each risk factor and calf diarrhoea or shedding status.

In multivariable models adjusted for current risk factors, early-life supplementation was not positively associated with BMD (Table 2).

Multivariable models adjusted for age group, payer, rural vs. urban hospital location, multiple gestation, and high-risk pregnancy.