Abstract
For semi-continuous data which are a mixture of true zeros and continuously distributed positive values, the use of two-part mixed models provides a convenient modelling framework. However, deriving population-averaged (marginal) effects from such models is not always straightforward. Su et al. presented a model that provided convenient estimation of marginal effects for the logistic component of the two-part model but the specification of marginal effects for the continuous part of the model presented in that paper was based on an incorrect formulation. We present a corrected formulation and additionally explore the use of the two-part model for inferences on the overall marginal mean, which may be of more practical relevance in our application and more generally.
Highlights
1 Introduction In Su et al.,[1] we described a two-part marginal model for longitudinal semi-continuous data that are a mixture of true zeros and continuously distributed positive values
The objective is to examine the association between alleles that code for human leukocyte antigen (HLA) proteins and disability level in a psoriatic arthritis (PsA) patient cohort
Interpretation of the impact of a covariate on the marginal mean given being positive cannot be made from only considering the relevant component of when the random effects are correlated and that covariate is included in the binary part
Summary
In Su et al.,[1] we described a two-part marginal model for longitudinal semi-continuous data that are a mixture of true zeros and continuously distributed positive values. When discussing the continuous part of our model, we assumed, as did Tooze et al.,[9] that integrating out the random effects was straightforward, and that the form of the relationship between covariates and the marginal mean of the response, given that it is positive, was the same as the conditional mean given a positive response and random effects. This paper rectifies this error and explores the use of the proposed model when the target of inference is the overall marginal mean, which may be of most practical relevance
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