Inter-relationships between kindred variables are taken into account using multivariate distributions based on copulas and the marginal distributions involved.
Unlike almost all past models of this type, the inter-relationships between kindred variables are taken into account using multivariate distributions based on copulas and the marginal distributions involved.
Because in natural systems, selection is likely to act on a set of traits simultaneously, genetic correlations between traits must be accounted for using a multivariate version of the breeder's equation: R = Gβ, where G is the additive genetic covariance matrix and β is the selection gradient (Lande and Arnold 1983; Hill and Kirkpatrick 2010).
Odds ratios of CS were evaluated using multivariate logistic regression, accounting for maternal age, referral status, and private healthcare insurance.
Costs were adjusted using multivariate regression to account for demographic (age, gender, region, health plan) and clinical (VTE type, Charlson Comorbidity Index) covariates.
The evaluation of performance at the patient level was performed using multivariate Cox models accounting for the clustered structure of the data within institutions and considering the activity status of the patient as a proxy for survival.
The effect of gender, age, insurance status, and need for reoperation on postoperative mortality was examined using multivariate logistic regression accounting for the NIS stratified sampling design and using comorbidity adjustments appropriate for index hospitalization data.
Measures of effect were assessed using multivariate fixed effects logistic regression model accounting for clustering at facility level and the results are presented as adjusted odds ratios.
The association was examined using multivariate logistic regressions, taking into account women's socio-demographic characteristics.
They can be reduced by using multivariate statistics.
