Sentence examples for generalised estimation from inspiring English sources

The OR of SSI associated with the treatment arm compared with the control arm will be estimated using the generalised estimation equations accounting for potential clustering in the unit of randomisation, adjusting a priori for the joint replaced, gender, age group and presence of rheumatoid arthritis; these are all factors known to impact on the risk of infection.

Because observations are not independent in women who delivered more than once during the study period, we calculated estimates using clustered data in the generalised estimation equation method (PROC GENMOD).

Generalised Linear Models (GLM) for cluster level and Generalised Estimation Equations (GEE) and/or Multi-Level Modeling (MLM) for individual level data with adjustment for potential covariates at both cluster and individual levels will be used.

Sensitivity analysis—In addition to the conditional logistic regression, we estimated odds ratios and 95% confidence intervals using a generalised estimation equation framework.

Table 2 reports only the confidence intervals according to the standard generalised estimation equations, as we found similar results with the jack-knife estimator.

Data were analysed using generalised estimation equation (GEE) modelling.

Our approach to the analyses is guided by our own experience, 56 and recent reviews of the relative advantages of constrained full information likelihood treatment of the outcome versus inclusion of the baseline value as a covariate in ANCOVA, 57 and methods of estimation (generalised estimating equations (GEE) vs mixed random effects).

We calculated sex adjusted odds ratios for excessive drinking and crude odds ratios for relevance and frequency of alcohol counselling, with their 95% confidence intervals, with generalised estimating equations with robust variance estimation.

Since random-effect modelling has stringent data assumptions (e.g. sufficient number of groups, distributional assumptions of the random effect), robust alternatives such as generalised estimating equations (GEE) or robust variance estimation are often preferable [ 20– 20].

Dependencies in the data, due to the fact that each participant was measured repeatedly (before, during and after OMT), were handled by generalised estimating equations with unstructured working correlation and robust variance estimation.

For these measures, random effects ("multilevel") models using maximum likelihood estimation 26 for quantitative outcomes, and marginal models using generalised estimating equations 27 for dichotomous outcomes, were fitted for the regression analyses to allow for within-subject correlation.