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Within a ZIP likelihood framework, Long et al. (2014) proposed marginalized zero-inflated Poisson (MZIP) regression, which specifies a two-part model for counts with a set of regression coefficients for the marginal mean and, to complete model specification, a second set of regression coefficients for the latent parameter defining membership in the 'excess-zero' class.
An advantage of this method is that one need not to specify the distribution of the outcome variable, just the relationships between the marginal mean and variance, and between the marginal mean and covariates.
The τ is determined by the Beta-binomial method with defined marginal mean and selected intraclass correlation.
Using post-imputation rounding introduced some bias in the estimate of the marginal mean and this increased as the number of values imputed below the minimum value increased.
Suppose that s and t follow a joint probability distribution with correlation coefficient r st, and with marginal mean and variance: E s), E t), var(s), and var(t).
The estimate from the sensitivity analysis under MNAR is 0.022 for the marginal mean and 0.518 for the measure of association, which are quite close to the values from the full dataset.
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Estimated marginal means and standard errors are depicted.
Results are given as estimated marginal means and standard error.
Estimated marginal means and significance values are shown in Table 3.
Again, effect sizes 'd' were calculated employing the estimated marginal means and the pooled standard deviation.
Effect sizes are reported as estimated marginal means and between-group differences.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com