PubMed · 1460483
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T Byrt. 1992. Problems with kappa.. https://doi.org/10.1016/0895-4356(92)90208-5
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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.
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International harmonization of guidelines for bioequivalence assessment has led to a wide acceptance of the multiplicative model for the extent and rate characteristics AUC and Cmax and--in consistency with this--of the bioequivalence range (0.80, 1.25). The effect of this change from (0.80, 1.20) on the power of the two one-sided test procedure and the sample sizes based thereon is investigated as a function of the within-subject coefficient of variation (CV) and the ratio mu T/mu R of expected medians for test and reference. The relative reduction in sample size is practically zero for mu T/mu R < or = 0.9 and then gradually increases as mu T/mu R approaches 1.2. At mu T/mu R = 1, the reduction is up to 20%. For a fixed ratio mu T/mu R this reduction increases with the coefficient of variation, reaching a plateau at a CV of about 25%.