Sentence examples for count outcome from inspiring English sources

We describe our approach for continuous, binary, ordinal, and count outcome data.

The count outcome of total PSQI score was modeled using negative binomial regression.

The change in disability count outcome between follow-up and baseline was modeled using linear regression.

In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome.

(Statistically, the lower explained variance in trip frequency could be simply because it is a count outcome, which could be better modeled by a Poisson family model or an ordered logit/probit model. SEM, particularly maximum likelihood SEM, assumes the outcome to be continuous, and so it works better for composite travel measures such as travel time).

The negative binomial model assumes that the number of coinfections is a count outcome.

In such cases, marginal mean regression parameters in MZIP, MZINB, MPois-Pois and MNB-Pois models have straightforward interpretations in describing overall exposure effects on count outcomes.

Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models.

Despite the flexibility of finite mixtures for describing highly dispersed count data, parameters from standard mixture regression models are not directly applicable to making inferences about the overall effects of covariates on marginal means of count outcomes (Albert et al. 2014; Preisser et al. 2012).

Count outcomes are common in randomized studies of alcohol treatment.

The last range of applications we will mention is to panel studies of count outcomes.