Variables found significant (p<0.05) by univariate logistic regression were tested in a multiple variable model to determine risk-adjusted association with mortality.
The Cox proportional hazards model was used in a multiple variable model to assess the risk of dislocation in association with femoral head diameter, with adjustment for sex, age, diagnosis, and type of prosthesis (SPSS, Chicago, IL, USA).
A multiple variable model adjusting for covariates estimated prevalence ratios (PR) of having good to excellent SRDH and included an interaction term for insurance and household income group.
The HR for death in the ITH group relative to the NTH group in a multiple variable model that included CCI, age, sex, and admission to the ICU as covariates was 0.34 (95% CI 0.25 0.47, P < 0.0001) (Table 3).
Reliability was analysed using kappa coefficients, while discriminative power of the best-fitting tests for back- and lower-extremity pain was assessed using a multiple-variable regression model.
Otherwise – for non-dichotomous variables and for multiple-variable models – relative risks were calculated from logistic regression odds ratios following established methods [ 11].
To quantify these trends we applied a multiple-variable, linear regression model to LIP activity over a sliding temporal window as described in Methods.
The data were then fitted by a multiple variable regression model using the maximum likelihood method.
To perform this multivariate logistic regression, a multiple variable logistic model was used.
Adjusted associations between SRDH, insurance and household income were assessed in a multiple variable regression model.
Most of them were still significant even after considering the ER status and node status using the multiple variable model (Table 3).
