Evaluation of statistical methods for safety signal detection: a simulation study

Proactive evaluation of drug safety with systematic screening and detection is critical to protect patients' safety and important in regulatory approval of new drug indications and postmarketing communications and label renewals. In recent years, quite a few statistical methodologies have been developed to better evaluate drug safety through the life cycle of the product development. The statistical methods for flagging safety signals have been developed in two major areas – one for data collected from spontaneous reporting system, mostly in the postmarketing area, and the other for data from clinical trials. To our knowledge, the methods developed for one area have not been applied to the other one so far. In this article, we propose to utilize all such methods for flagging safety signals in both areas regardless of which specific area they were originally developed for. Therefore, we selected eight typical methods, that is, proportional reporting ratios, reporting odds ratios, the maximum likelihood ratio test, Bayesian confidence propagation neural network method, chi‐square test for rates comparison, Benjamini and Hochberg procedure, new double false discovery rate control procedure, and Bayesian hierarchical mixture model for systematic comparison through simulations. The Benjamini and Hochberg procedure and new double false discovery rate control procedure perform best overall in terms of sensitivity and false discovery rate. The likelihood ratio test also performs well when the sample sizes are large. Copyright © 2014 John Wiley & Sons, Ltd.     

Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India

Urban Ecosystems - Urbanization is a major threat to native biodiversity. Invasion of tramp species is a major consequence of urbanization, which might threaten native species. Ants are established...     

Linking Food Security with Household’s Adaptive Capacity and Drought Risk: Implications for Sustainable Rural Development

In spite of green revolution and rapid economic growth, India’s vast population still suffers from hunger and poverty, especially in the rural areas. Moreover, drought adversely affects India’s economy by declining agricultural production and purchasing power. It also escalates rural unemployment which ultimately affects household food security. Our study investigated the food security of drought prone rural households in a broader context by linking the dimensions of food security with dimensions of climate change vulnerability. We used the primary data of 157 drought prone rural households of Odisha state in India for analysis. This study employed polychoric principal component analysis to construct an aggregate food security index. An ordered probit model was used to estimate the determinants of food security. The FSI showed that three-fourth of the respondents were facing food security issues with varying degrees. The estimates of ordered probit model indicated that joint family, education, migration and health insurance are key variables that determine food security, whereas drought adversely affected food security of rural households. Overarching strategies are required to effectively address food security issues in the wake of increased drought risk. This study provides an insight for policy makers in India and in similar south Asian countries who must consider food security in the light of drought.