Cross-Validation for Nonlinear Mixed Effects Models

Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires "out-of-sample" predictions of the outcome variable, which cannot be easily calculated when random effects are present. We describe two novel variants of cross-validation that can be applied to nonlinear mixed effects models. One variant, where out-of-sample predictions are based on post hoc estimates of the random effects, can be used to select the overall structural model. Another variant, where cross-validation seeks to minimize the estimated random effects rather than the estimated residuals, can be used to select covariates to include in the model. We show that these methods produce accurate results in a variety of simulated data sets and apply them to two publicly available population pharmacokinetic data sets.Comment: 38 pages, 15 figures To be published in the Journal of Pharmacokinetics and Pharmacodynamic

"Pre-conditioning" for feature selection and regression in high-dimensional problems

We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function, yielding a "pre-conditioned" response variable. The primary method used for this initial regression is supervised principal components. Then we apply a standard procedure such as forward stepwise selection or the LASSO to the pre-conditioned response variable. In a number of simulated and real data examples, this two-step procedure outperforms forward stepwise selection or the usual LASSO (applied directly to the raw outcome). We also show that under a certain Gaussian latent variable model, application of the LASSO to the pre-conditioned response variable is consistent as the number of predictors and observations increases. Moreover, when the observational noise is rather large, the suggested procedure can give a more accurate estimate than LASSO. We illustrate our method on some real problems, including survival analysis with microarray data

A radiopaque polymer hydrogel used as a fiducial marker in gynecologic-cancer patients receiving brachytherapy

We assessed a novel Food and Drug Administration–approved hydrogel, synthesized as absorbable iodinated particles, in gynecologic-cancer patients undergoing computed tomography (CT) or magnetic resonance (MR) based brachytherapy after external beam radiation

Viral MicroRNAs Identified in Human Dental Pulp

MicroRNAs (miRs) are a family of non-coding RNAs that regulate gene expression. They are ubiquitous among multicellular eukaryotes and are also encoded by some viruses. Upon infection, viral miRs (vmiRs) can potentially target gene expression in the host and alter the immune response. While prior studies have reported viral infections in human pulps, the role of vmiRs in pulpal disease is yet to be explored. The purpose of this study was to examine the expression of vmiRs in normal and diseased pulps and to identify potential target genes

Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data

An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer

An Analysis of Patient Characteristics and Clinical Outcomes in Primary Pulmonary Sarcoma

INTRODUCTION: Literature concerning primary pulmonary sarcomas (PPS) is limited to small case series. This study examines, in a large cohort, the clinical characteristics and therapeutic strategies of PPS and their impact on overall survival (OS). METHODS: This was a retrospective analysis from the Surveillance, Epidemiology, and End Results database (1988-2008). Eligible patients had primary PPS and underwent local therapy. Survival estimates were obtained using the Kaplan-Meier method and the Cox regression model. OS of PPS patients were compared with a cohort of 10,909 patients with extremity soft-tissue sarcomas. RESULTS: The cohort included 365 PPS patients with a median follow-up of 21 months. Fifty-five percent of the patients had large tumors (>5 cm), 76% were high-grade, and 16% had node-positive disease. Seventy-five percent of the cohort underwent surgery alone, 14% underwent surgery and radiation therapy, and 11% underwent radiation therapy alone. Multivariate analysis showed reduced OS for patients with tumors more than 5 cm (hazard ratio [HR] 1.6, 95% confidence interval [CI] 1.25-2.19), high tumor grade (HR 3.1, 95% CI 1.26-3.62), and unresectable disease (HR 2.6, 95% CI 1.76-3.88. The 5-year OS for the cohort of pulmonary sarcomas versus sarcomas of the extremities was 35% versus 71% (p < 0.0001). CONCLUSION: This large study examining PPS patients reveals a high rate of nodal involvement and a markedly worse OS than patients with extremity soft-tissue sarcomas. Thus, given the poor overall prognosis, it is recommended that PPS patients undergo a thorough mediastinal nodal evaluation to rule out locoregional metastasis and proceed with aggressive treatment

Universal properties of correlation transfer in integrate-and-fire neurons

One of the fundamental characteristics of a nonlinear system is how it transfers correlations in its inputs to correlations in its outputs. This is particularly important in the nervous system, where correlations between spiking neurons are prominent. Using linear response and asymptotic methods for pairs of unconnected integrate-and-fire (IF) neurons receiving white noise inputs, we show that this correlation transfer depends on the output spike firing rate in a strong, stereotyped manner, and is, surprisingly, almost independent of the interspike variance. For cells receiving heterogeneous inputs, we further show that correlation increases with the geometric mean spiking rate in the same stereotyped manner, greatly extending the generality of this relationship. We present an immediate consequence of this relationship for population coding via tuning curves