Sentence examples for estimates for binary from inspiring English sources

Figure 2 shows corresponding estimates for binary HbA1c>8% using GLMM.

The effect estimates for binary outcome variables were obtained from logistic regression models and are presented as odds ratios with 95% confidence intervals.

The pooled effects estimates for binary variables were expressed as odds ratios with 95% confidence interval (CI), whereas continuous variables were expressed as mean differences with 95% CI.

50 More recently, a meta-epidemiological study 11 of 48 meta-analyses showed significantly larger intervention effect estimates for binary outcomes in single centre trials than in multicentre trials, which persisted after adjustment for the number of patients randomly allocated and risk of bias.

Data on the association of statin treatment with continuous outcome variables are presented in Table 2. Effect estimates for binary outcomes are presented in Fig 2. Three cohort studies reported decreased all-cause mortality in patients treated with statins, whereas no difference in all-cause mortality between statin users and non-statin users was found in one cohort study.

The ICCs for continuous and ordinal variables were calculated using multilevel package and ICC estimates for binary variables and boostrap confidence intervals were calculated using aod package in R. ICCs and 95% confidence intervals (CIs) were calculated for 429 heart failure patients (CP, n = 214 and UC, n = 215).

Odds ratios and 95% confidence intervals are estimated for binary health outcomes using logistic regression to compare exposed and unexposed populations while controlling for age, gender, education and race/ethnicity.

These include the first reported estimates for the binary interaction energies of MA with TMPC and with PC.

Heritability estimates for any binary trait can be used by our method.

Heritability estimates for the binary life history classifications in addition to the quantitative traits were conducted using a Bayesian MCMC statistical framework, which provides a more robust technique for analyzing non-Gaussian traits (Hadfield 2010).

Additional file 1 shows the ROC area under the curve (AUC) estimates for predicting binary outcomes capturing depression onset over 12 months from all RDQ, MFQ and RCMAS items.