PubMed · 8321098
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R P Lederman. Time series analysis.. https://doi.org/10.1097/00005721-199305000-00013
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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.
Population persistence in patchy environments is expected to result from an interaction between local density dependence, dispersal, and spatial heterogeneity. Using two-patch models of single species, I explore two aspects of this interaction that have hitherto received little attention. First, how is the interaction affected when local density dependence changes from negative (logistic) to positive (Allee)? Second, how does dispersal mortality influence persistence ? When local dynamics are logistic, dispersal changes the strength of negative density dependence within patches without much between-patch effect. For example, dispersal mortality reduces the population growth rate and counteracts the tendency towards complex dynamics. Density-dependent disperal amplifies the nonlinearity in the growth rate, thus opposing the stabilizing effects of dispersal mortality. Spatial heterogeneity has little or no effect on stability. In contrast to the logistic, dispersal under Allee dynamics creates between-patch effects that bring about a qualitative change. Patches that fall below the Allee threshold are rescued from extinction by immigrants from patches that are above the threshold. Density-dependent dispersal enhances this rescue effect. Persistence below the extinction threshold is contingent on spatial heterogeneity. Dispersal mortality is of little or no consequence.
A series of 15 quinoline congeners were assayed for mutagenicity and cytotoxicity in the Ames test using strain TA100 bacteria. Statistical analysis of the data allowed simultaneous determination of the mutagenicity and cytotoxicity of each quinoline. These data were used to develop three quantitative structure-activity relationships (QSAR). In all three QSAR, the strength of the relationship between hydrophobicity (as measured by log P) and biological activity was similar as h was near 1 in all three cases. For the mutagenicity of these quinolines, both hydrophobic and steric interactions appear to be important. In contrast, the cytotoxicity is mainly affected by increasing hydrophobicity and by the addition of electron withdrawing substituents to the quinoline ring. Comparison to other QSAR from our laboratory and others lends support to these findings. Both simultaneous consideration of different biological activities and the comparison of newly developed QSAR with previous data for the purpose of lateral validation should be encouraged in future QSAR studies.