A statistical analysis of vaccine-adverse event data.

BACKGROUND: Vaccination has been one of the most successful public health interventions to date, and the U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 500,000 reports for post-vaccination adverse events that occur after the administration of vaccines licensed in the United States. The VAERS dataset is huge, contains very large dimension nominal variables, and is complex due to multiple listing of vaccines and adverse symptoms in a single report. So far there has not been any statistical analysis conducted in attempting to identify the cross-board patterns on how all reported adverse symptoms are related to the vaccines. METHODS: For studies of the relationship between vaccines and reported adverse events, we consider a partial VAERS dataset which includes all reports filed over a period of 24 years between 1990-2013. We propose a neighboring method to process this dataset for dealing with the complications caused by multiple listing of vaccines and adverse symptoms in a single report. Then, the combined approaches based on our neighboring method and novel utilization of data visualization techniques are employed to analyze the large dimension dataset for characterization of the cross-board patterns of the relations between all reported vaccines and events. RESULTS: The results of our analysis indicate that those events or symptoms with overall high occurrence frequencies are positively correlated, and those most frequently occurred adverse symptoms are mostly uncorrelated or negatively correlated under different bacteria vaccines, but they are in many cases positively correlated under different virus vaccines, especially under flu vaccines. No particular patterns are shown under live vs. inactive vaccines. CONCLUSIONS: This article identifies certain cross-board patterns of the relationship between the vaccines and the reported adverse events or symptoms. This helps for better understanding the VAERS data, and provides a useful starting point for the development of statistical models and procedures to further analyze the VAERS data.

Asymptotic Behavior of Cox's Partial Likelihood and its Application to Variable Selection.

For theoretical properties of variable selection procedures for Cox's model, we study the asymptotic behavior of partial likelihood for the Cox model. We find that the partial likelihood does not behave like an ordinary likelihood, whose sample average typically tends to its expected value, a finite number, in probability. Under some mild conditions, we prove that the sample average of partial likelihood tends to infinity at the rate of the logarithm of the sample size, in probability. We apply the asymptotic results on the partial likelihood to study tuning parameter selection for penalized partial likelihood. We find that the penalized partial likelihood with the generalized cross-validation (GCV) tuning parameter proposed in Fan and Li (2002) enjoys the model selection consistency property, despite the fact that GCV, AIC and Cp , equivalent in the context of linear regression models, are not model selection consistent. Our empirical studies via Monte Carlo simulation and a data example confirm our theoretical findings.

Apoptosis-induced alkalinization by the Na+/H+ exchanger isoform 1 is mediated through phosphorylation of amino acids Ser726 and Ser729.

Apoptosis is a complex process essential for normal tissue development and cellular homeostasis. While biochemical events that occur late in the apoptotic process are better characterized, early physiological changes that initiate the progression of cell death remain poorly understood. Previously, we observed that lymphocytes, undergoing apoptosis in response to growth factor withdrawal, experienced a rapid and transient rise in cytosolic pH. We found that the protein responsible was the pH-regulating, plasma membrane protein Na(+)/H(+) exchanger isoform 1 (NHE1), and that its activity was impeded by inhibition of the stress-activated kinase, p38 MAP kinase. In the current study, we examined how NHE1 is activated during apoptosis. We identified the phosphorylation sites on NHE1 that regulate its alkalinizing activity in response to a cell death stimulus. Performing targeted mutagenesis, we observed that substitution of Ser726 and Ser729 for alanines produced a mutant form of NHE1 that did not alkalinize in response to an apoptotic stimulus, and expression of which protected cells from serum withdrawal- induced death. In contrast, substitution of Ser726 and Ser729 for glutamic acids raised the basal pH and induced susceptibility to death. Analysis of serine phosphorylation showed that phosphorylation of NHE1 during apoptosis decreased upon mutation of Ser726 and Ser729. Our findings thus confirm a necessary function for NHE1 during apoptosis and reveal the critical regulatory sites that when phosphorylated mediate the alkalinizing activity of NHE1 in the early stages of a cell death response.