Temporal and spatial relationships between an herbivorous lady beetleEpilachna niponica and its predator,the earwigAnechura harmandi

The relationships between egg predation of an herbivorous lady beetle Epilachna niponica (Lewis ) and its predator, the earwig Anechura harmandi (Burr ), were examined in both time and space. In spite of little annual, changes in egg densities, egg mortality due to predation varied considerably. There was no, clear relationship between the earwig density and the proportionate predation over the five years. The seasonal occurrence of earwig nymphs on thistle plants, however, was closely synchronized with that of egg predation. Predator attacks on the beetle occurred in a time-restricted manner. Thus, later cohorts mostly escape from heavy predatory pressure. No spatially density-depent egg predation was detected at the level of either thistle plants or thistle patches. Furthermore, there was no indication of aggregative behaviour of the earwig in response to local egg density. The earwig density was more likely to be associated with particular localities with sandy deposits available for its nest site.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Dynamics of savanna ecosystems

Abstract. Theories concerning the factors involved in the dynamics of savannas, particularly the tree-grass interface, are reviewed. Emphasis is put on factors related to soil moisture, soil nutrients, fire and large herbivores. The distinction between external (independent) and internal (dependent, interactive) environment is discussed and it is explained how this distinction is affected by the scale of observation.     

On the Information Matrix of Exponential Mixture Models with Long-term Survivors

The aim of this paper is to study the properties of the asymptotic variances of the maximum likelihood estimators of the parameters of the exponential mixture model with long-term survivors for randomly censored data. In addition, we study the asymptotic relative efficiency of these estimators versus those which would be obtained with complete follow-up. It is shown that fixed censoring at time T produces higher precision as well as higher asymptotic relative efficiency than those obtainable under uniform and uniform-exponential censoring distributions over (0, T). The results are useful in planning the size and duration of survival experiments with long-term survivors under random censoring schemes.     

Variable selection in joint frailty models of recurrent and terminal events

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the “minimum approximated information criterion” method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.     

Randomization inference with general interference and censoring

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is “partial interference,” in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) consider randomization-based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right-censored outcomes is challenging because standard randomization-based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676–692) for two-sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh.     

Synthesis and elaboration of N-methylpyrrolidone as an acetamide fragment substitute in bromodomain inhibition

N-Methylpyrrolidone is a solvent molecule which has been shown to compete with acetyl-lysine-containing peptides for binding to bromodomains. From crystallographic studies, it has also been shown to closely mimic the acetamide binding motif in several bromodomains, but has not yet been directly pursued as a fragment in bromodomain inhibition. In this paper, we report the elaboration of N-methylpyrrolidone as a potential lead in fragment-based drug design. Firstly, N-methylpyrrolidone was functionalised to provide points for chemical elaboration. Then, the moiety was incorporated into analogues of the reported bromodomain inhibitor, Olinone. X-ray crystallography revealed that the modified analogues showed comparable binding affinity and structural mimicry to Olinone in the bromodomain binding site.     

Synthesis of evidence from epidemiological studies with interval-censored exposure due to grouping

We describe a method for assessing dose-response effects from a series of case-control and cohort studies in which the exposure information is interval censored. The interval censoring of the exposure variable is dealt with through the use of retrospective models in which the exposure is treated as a multinomial response and disease status as a binary covariate. Polychotomous logistic regression models are adopted in which the dose-response relationship between exposure and disease may be modeled in a discrete or continuous fashion. Partial conditioning is possible to eliminate some of the nuisance parameters. The methods are applied to the motivating study of the relationship between chorionic villus sampling and the occurrence of terminal transverse limb reduction.     

Statistical inference for serial dilution assay data

Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson-Bernoulli model for such assays is appropriate for count data but not for continuous measurements that are encountered in applications involving substance concentrations. This paper presents practical inference methods based on a log-normal model and illustrates these methods using a case application involving bacterial toxins.