Summary Tools for suppressing synaptic transmission gain power when able to target highly selective neuron subtypes, thereby sharpening attainable links between neuron type, behavior, and disease; and when able to silence most any neuron subtype, thereby offering broad applicability. Here we present such a tool, RC::PFtox, that harnesses breadth in scope along with high cell-type selection via combinatorial gene expression to deliver tetanus toxin light chain (tox), an inhibitor of vesicular neurotransmission. When applied in mice, we observed cell-type specific disruption of vesicle exocytosis accompanied by loss of excitatory postsynaptic currents and commensurately perturbed behaviors. Among various test populations, we applied RC::PFtox to silence serotonergic neurons, en masse or a subset defined combinatorially. Of the behavioral phenotypes observed upon en masse serotonergic silencing, only one mapped to the combinatorially defined subset. These findings provide evidence for separability by genetic lineage of serotonin-modulated behaviors; collectively, these findings demonstrate broad utility of RC::PFtox for dissecting neuron functions.
The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data.
Longitudinal data often encounter missingness with monotone and/or intermittent missing patterns. Multiple imputation (MI) has been popularly employed for analysis of missing longitudinal data. In particular, the MI-GEE method has been proposed for inference of generalized estimating equations (GEE) when missing data are imputed via MI. However, little is known about how to perform model selection with multiply imputed longitudinal data. In this work, we extend the existing GEE model selection criteria, including the "quasi-likelihood under the independence model criterion" (QIC) and the "missing longitudinal information criterion" (MLIC), to accommodate multiple imputed datasets for selection of the MI-GEE mean model. According to real data analyses from a schizophrenia study and an AIDS study, as well as simulations under nonmonotone missingness with moderate proportion of missing observations, we conclude that: (i) more than a few imputed datasets are required for stable and reliable model selection in MI-GEE analysis; (ii) the MI-based GEE model selection methods with a suitable number of imputations generally perform well, while the naive application of existing model selection methods by simply ignoring missing observations may lead to very poor performance; (iii) the model selection criteria based on improper (frequentist) multiple imputation generally performs better than their analogies based on proper (Bayesian) multiple imputation.
The aim of this article is to provide asymptotically valid likelihood inferences about regression parameters for correlated ordinal response variables. The legitimacy of this novel approach requires no knowledge of the underlying joint distributions so long as their second moments exist. The efficacy of the proposed parametric approach is demonstrated via simulations and the analyses of two real data sets.
We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly.
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