Sentence examples for marginal model which from inspiring English sources

A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it).

Estimation and inference in classical linear mixed models are based on the marginal model, which for equation (1) is the bivariate normal model with variance-covariance δ i + Ω. Assume for the sake of simplicity that the within-study covariance matrix δ i = δ for all studies.

In order to analyze the DAS44 responses over time for both groups (PBO and MTX), we use a marginal longitudinal model, which consists of modeling parametrically the marginal mean of the responses as well as the correlations between individuals.

To account for this collider bias we implemented the marginal structural model, which rather surprisingly did not minimize HWE bias (Tables 4, 5 and 6 - Model 4).

Lin et al. provided a rigorous formalization of the marginal rate model, which relaxes the assumption that the event history, F i (t), can be completely described by time-dependent covariates, x i (t), that is, [ 17, 30] E[ dN i (t )| F i (t )] = E[ dN i (t )| x i (t )].

For the analysis of the effects of the HLA - A -B and DRB1 haplotypes, we use the corresponding "marginal" model in which the effect is always randomly attributed to one of the two haplotypes <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0006836.e011.PNG" class= inline-graphic"/> of a considered individual.

While this feature poses no problem for marginal models, in which the mean and correlation functions are modeled directly and separately, the issue is more complicated in, for example, generalized linear mixed models.

We are utilizing discrete time proportional hazards models to study the association of time-varying exposures and correlates with outcomes as well as marginal structural models, which provide an opportunity to account for time dependent influences of confounders on iron therapy.

We are utilizing discrete time proportional hazards models to study the association of time-varying exposures and correlates with outcomes as well as marginal structural models, which provide an opportunity to account for time dependent influences of confounders on anti-hypertension treatment choice through inverse probability weighting techniques.

The state of the art for addressing this challenge is considered by many to be methods employing inverse probability weighting to develop analytic cohorts whose characteristics and prior states are balanced across treatment groups, such as inversely-weighted propensity scoring [ 33, 34] and marginal structural modeling, which we will employ in primary analyses [ 35].

This is commonly calculated based on the F value of each model, which is the negative marginal log-likelihood or negative log-evidence: F = −ln p y | m).