PubMed · 8467116
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M Pagano. Is normal normal?. https://pubmed.ncbi.nlm.nih.gov/8467116/
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Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.
A framework--the "agreement concept"--is developed to study the use of Cohen's kappa as well as alternative measures of chance-corrected agreement in a unified manner. Focusing on intrarater consistency it is demonstrated that for 2 x 2 tables an adequate choice between different measures of chance-corrected agreement can be made only if the characteristics of the observational setting are taken into account. In particular, a naive use of Cohen's kappa may lead to strikingly overoptimistic estimates of chance-corrected agreement. Such bias can be overcome by more elaborate study designs that allow for an unrestricted estimation of the probabilities at issue. When Cohen's kappa is appropriately applied as a measure of chance-corrected agreement, its values prove to be a linear--and not a parabolic--function of true prevalence. It is further shown how the validity of ratings is influenced by lack of consistency. Depending on the design of a validity study, this may lead, on purely formal grounds, to prevalence-dependent estimates of sensitivity and specificity. Proposed formulas for "chance-corrected" validity indexes fail to adjust for this phenomenon.
We describe techniques for assessing evolutionary trees constructed by the parsimony criteria, when sequences exhibit irregular base compositions. In particular, we extend a recently described frequency-dependent significance test to handle any number of taxa and describe a modification of the Kishino-Hasegawa sites test. These modifications are useful for detecting historical signals beyond those patterns which arise purely from irregular base compositions between the compared sequences. We apply the test to extend our earlier studies on chloroplast origins using 16S rDNA sequences, where a failure to compensate for irregular base compositions between the compared sequences provides statistically significant support for unjustified phylogenetic inferences. We also describe how the techniques can be modified to determine how "tree-like" data are, given independent variation in the base frequencies.