Mapping the regional transition to cyclicity inClethrionomys rufocanus: Spectral densities and functional data analysis

We study the regional transitions in dynamics of the gray-sided vole,Clethrionomys rufocanus, within Hokkaido, Japan. The data-set consists of 225 time series of varying length (most from 23 to 31 years long) collected between 1962 and 1992 by the Forestry Agency of the Japanese Government. To see clearly how the periodic behavior changes geographically, we estimate the spectral density functions of the growth rates of all populations using a log-spline method. We subsequently apply functional data analysis to the estimated densities. The functional data analysis is, in this context, analogous to a principal component analysis applied to curves. We plot the results of the analysis on the map of Hokkaido, to reveal a clear transition from relatively stable populations in the southwest and west to populations undergoing 3–4 year cycles in the northeast and east. The degree of seasonality in the vegetation and the rodent demography appear to be strongest in the cyclic area. We briefly speculate that the destabilization of the rodent dynamics is linked to increased seasonalforcing on the trophic interactions in which the gray-sided voles are involved.     

A Bayesian method for safety signal detection in ongoing blinded randomised controlled trials

Sponsors have a responsibility to minimise risk to participants in clinical studies through safety monitoring. The FDA Final Rule for IND Safety Reporting requires routine aggregate safety evaluation, including in ongoing blinded studies. We are interested in estimating the probability that the true adverse event rate in the experimental arm exceeds that in the control arm. We developed a Bayesian approach that specifies an informative meta-analytic predictive prior on the event probability in the control arm and an uninformative prior on that in the experimental arm. We combined these priors with a mixture likelihood that considers each patient in the ongoing blinded study may belong to the experimental or control arm. This allowed us to estimate the quantity of interest without unblinding. We evaluated our method by simulation, pairing scenarios that differed only in whether a safety signal was present or missing, and quantifying the ability of our model to discriminate using signal detection theory. Our approach shows benefit. It detects safety signals more reliably with greater sample sizes and for common rather than rare events. Performance does not deteriorate markedly when historical studies exhibit heterogeneous hazards or non-constant hazards. Our method will allow us to monitor safety signals in ongoing blinded studies with the goal of earlier identification and risk mitigation. Our method could be adapted to use informative priors on both arms or predictive covariates where pertinent data exist. We stress that ongoing safety monitoring should involve a multi-disciplinary team where statistical methods are paired with medical judgement.