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Methods of item and person parameter estimation which incorporate missing data in the item responses have long been developed and have been proved to be suitable (Lord [1974], [1983]; Mislevy et al. [1992]).
We have developed a Bayesian method for Linkage analysis of Ordinal and Categorical traits (LOCate) that can analyze complex genealogical structure for family groups and incorporate missing data.
Further work is required to explicitly incorporate missing data, as this is likely to have a strong influence on the estimates of both the generation time and the reproductive number.
However, these methods are limited primarily by their lack of ability to incorporate missing data.
Since the mixed regression model has the ability to incorporate missing data, the six drop-outs were included in the analysis.
Moreover, distance matrices can more easily incorporate missing data: each sufficiently conserved gene, ubiquitous or not, can contribute to the pairwise distances between the organisms containing that gene.
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The propensity score is useful to identify heterogeneity and also incorporates missing data into the analysis in a fashion different from the multiple imputations used with the primary Cox method.
Second, by virtue of its probabilistic nature, uncertain observations can be incorporated, missing data can be managed, and flexible queries to probe and explain predictions can be entertained [ 20, 21].
Having established that non-detects in qPCR data represent data missing not at random, we now propose a method that incorporates the missing data mechanism into subsequent statistical analyses.
This method estimates the number and outcomes of missing studies, and adjusts the meta-analysis to incorporate the imputed missing data [ 16].
Logistic regression models which incorporate techniques for missing data estimation are used for risk-adjustment; the change in two-year health status is determined as: better than expected, same as expected, or worse than expected [ 10].
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