PubMed · 7648775
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L B Sheiner, D Verotta. 1995. Further notes on physiologic indirect response models.. https://doi.org/10.1016/0009-9236(95)90203-1
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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.
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.
The interaction of temperature (10, 14, 25, 31, and 37 degrees C), pH (pH 5, 5.6, 6.5, 7.4, and 8), and NaCl (0, 2, 5, 8, and 10%) in a laboratory medium affects the specific growth of Staphylococcus aureus. From growth curves obtained by the turbidimetric technique, a nonlinear model in which the specific growth rate (mu) is fitted directly, without data transformation and with the residual error variations taken into account, is proposed. This model correctly fits experimental data and gives more biological information than the quadratic polynomial model. Moreover, the comparison of five strains of S. aureus was performed by a principal-component analysis in which the specific growth rate was the identifying characteristic for S. aureus strains. The results obtained from model coefficient comparison among the five strains and from multivariate data analysis allow the same classification of strains to be performed. Two of them have similar behaviors during food spoilage, two others could be distinguished by their capacity to grow at a low temperature, whereas the last one was markedly different from the others.