covariate

The choice of marker covariates has not been solved, however.

For example, in a multiple regression analysis we may include several covariates of potential interest.

The problem is that time-varying covariates may change their value over the time line of the study.

This model still can be generalized to include covariates with random effects.

The nested case control study can be analyzed using methods for missing covariates.

It reflects the phenomenon that a covariate is not necessarily constant through the whole study.

These covariates are then used in the standard regression models for model fitting and prediction.

These effects remained even when other clinically relevant covariates were accounted for.

Indeed, units in both groups have identical distributions of covariates and potential outcomes.

The threshold state, parameters of the process, and even time scale may depend on corresponding covariates.

The proposed methods hinges on first selecting the appropriate baseline covariates.

We chose the time-series model because it accounts for the variations of covariates within the study patients with time.

Including covariates would remove some of this bias.

However, even with the use of covariates, there are no statistical techniques that can equate unequal groups.

The area of residency could then be introduced in the statistical model as a time-varying covariate.

Mixture analysis of multivariate categorical data with covariates and missing entries.

Proportional hazards analysis was used to estimate relative risks adjusted for several covariates.

One may wish however to account for a number of covariates when analyzing neural data, not just a neuron's own history.

"A hazard model analysis of the covariates of marriage dissolution in Canada."

The effect of covariates estimated by any proportional hazards model can thus be reported as hazard ratios.

A multivariate analysis was performed to examine the influence of this polymorphism and other covariates (Table 3).

However, after further adjustment for body mass index, cigarette smoking and other covariates, no association with red meat consumption was found.

Propensity scores are used to reduce selection bias by equating groups based on these covariates.