Interval-specific event related potentials to omitted stimuli in the electrosensory pathway in elasmobranchs: an elementary form of expectation

Multiunit activity and slow local field potentials show Omitted Stimulus Potentials (OSP) in the electrosensory system in rays (Platyrhinoidis triseriata, Urolophus halleri) after a missing stimulus in a 3 to >20 Hz train of V pulses in the bath, at levels from the primary medullary nucleus to the telencephalon. A precursor can be seen in the afferent nerve. The OSP follows the due-time of the first omitted stimulus with a, usually, constant main peak latency, 30–50 ms in medullary dorsal nucleus, 60–100 ms in midbrain, 120–190 ms in telencephalon — as though the brain has an expectation specific to the interstimulus interval (ISI). The latency, form and components vary between nerve, medulla, mid-brain and forebrain. They include early fast waves, later slow waves and labile induced rhythms. Responsive loci are quite local. Besides ISI, which exerts a strong influence, many factors affect the OSP slightly, including train parameters and intensity, duration and polarity of the single stimulus pulses. Jitter of ISI does not reduce the OSP substantially, if the last interval equals the mean; the mean and the last interval have the main effect on both amplitude and latency.Taken together with our recent findings on visually evoked OSPs, we conclude that OSPs do not require higher brain levels or even the complexities of the retina. They appear in primary sensory nuclei and are then modified at midbrain and telencephalic levels. We propose that the initial processes are partly in the receptors and partly in the first central relay including a rapid increase of some depressing influence contributed by each stimulus. This influence comes to an ISI-specific equilibrium with the excitatory influence; withholding a stimulus and hence its depressing influence causes a rebound excitation with a specific latency.Abbreviations DN dorsal nucleus of medullary lateral line lobe - EEG electroencephalogram - EP evoked potential - ERP event related potential - IR induced rhythm - ISI interstimulus interval - OSP omitted stimulus potential - MLN mesencephalic lateral nucleus - P75 positive peak at 75 ms     

A matching procedure for sequential experiments that iteratively learns which covariates improve power

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package “SeqExpMatch ” for use by practitioners is available on CRAN .     

Identifiability of causal effects for binary variables with baseline data missing due to death

We discuss identifiability and estimation of causal effects of a treatment in subgroups defined by a covariate that is sometimes missing due to death, which is different from a problem with outcomes censored by death. Frangakis et al. (2007, Biometrics 63, 641-662) proposed an approach for estimating the causal effects under a strong monotonicity (SM) assumption. In this article, we focus on identifiability of the joint distribution of the covariate, treatment and potential outcomes, show sufficient conditions for identifiability, and relax the SM assumption to monotonicity (M) and no-interaction (NI) assumptions. We derive expectation-maximization algorithms for finding the maximum likelihood estimates of parameters of the joint distribution under different assumptions. Further we remove the M and NI assumptions, and prove that signs of the causal effects of a treatment in the subgroups are identifiable, which means that their bounds do not cover zero. We perform simulations and a sensitivity analysis to evaluate our approaches. Finally, we apply the approaches to the National Study on the Costs and Outcomes of Trauma Centers data, which are also analyzed by Frangakis et al. (2007) and Xie and Murphy (2007, Biometrics 63, 655-658).     

Influence of trial duration on the bias of the estimated treatment effect in clinical trials when individual heterogeneity is ignored

In randomized clinical trials where the times to event of two treatment groups are compared under a proportional hazards assumption, it has been established that omitting prognostic factors from the model entails an underestimation of the hazards ratio. Heterogeneity due to unobserved covariates in cancer patient populations is a concern since genomic investigations have revealed molecular and clinical heterogeneity in these populations. In HIV prevention trials, heterogeneity is unavoidable and has been shown to decrease the treatment effect over time. This article assesses the influence of trial duration on the bias of the estimated hazards ratio resulting from omitting covariates from the Cox analysis. The true model is defined by including an unobserved random frailty term in the individual hazard that reflects the omitted covariate. Three frailty distributions are investigated: gamma, log‐normal, and binary, and the asymptotic bias of the hazards ratio estimator is calculated. We show that the attenuation of the treatment effect resulting from unobserved heterogeneity strongly increases with trial duration, especially for continuous frailties that are likely to reflect omitted covariates, as they are often encountered in practice. The possibility of interpreting the long‐term decrease in treatment effects as a bias induced by heterogeneity and trial duration is illustrated by a trial in oncology where adjuvant chemotherapy in stage 1B NSCLC was investigated.     

Estimating functions for inhomogeneous spatial point processes with incomplete covariate data

The R package spatstat provides a very flexible and useful frameworkfor analysing spatial point patterns. A fundamental featureis a procedure for fitting spatial point process models dependingon covariates. However, in practice one often faces incompleteobservation of the covariates and this leads to parameter estimationerror which is difficult to quantify. In this paper, we introducea Monte Carlo version of the estimating function used in spatstatfor fitting inhomogeneous Poisson processes and certain inhomogeneouscluster processes. For this modified estimating function, itis feasible to obtain the asymptotic distribution of the parameterestimators in the case of incomplete covariate information.This allows a study of the loss of efficiency due to the missingcovariate data.     

Inference of haplotype effects in case-control studies using unphased genotype and environmental data

A retrospective likelihood-based approach was proposed to test and estimate the effect of haplotype on disease risk using unphased genotype data with adjustment for environmental covariates. The proposed method was also extended to handle the data in which the haplotype and environmental covariates are not independent. Likelihood ratio tests were constructed to test the effects of haplotype and gene-environment interaction. The model parameters such as haplotype effect size was estimated using an Expectation Conditional-Maximization (ECM) algorithm developed by Meng and Rubin (1993). Model-based variance estimates were derived using the observed information matrix. Simulation studies were conducted for three different genetic effect models, including dominant effect, recessive effect, and additive effect. The results showed that the proposed method generated unbiased parameter estimates, proper type I error, and true beta coverage probabilities. The model performed well with small or large sample sizes, as well as short or long haplotypes.     

Additive hazard regression with auxiliary covariates

We consider the additive hazard model when some of the truecovariates are measured only on a randomly selected validationset whereas auxiliary covariates are observed for all studysubjects. An updated pseudoscore estimation approach is proposedfor the parameters of the additive hazard model. It allows oneto fit the model with auxiliary covariates, while leaving thebaseline hazard unspecified. Asymptotic properties of the proposedestimators are established, and consistent standard error estimatorsare developed. Simulations demonstrate that the asymptotic approximationsof the proposed estimates are adequate for practical use. Areal example is used to illustrate the performance of the proposedmethod.     

Modelling the effects of partially observed covariates on Poisson process intensity

We propose an estimating function for parameters in a modelfor Poisson process intensity when time- or space-varying covariatesare observed for both the events of the process and at sampletimes or locations selected from a probability-based samplingdesign. We investigate the large-sample properties of the proposedestimator under increasing domain asymptotics, demonstratingthat it is consistent and asymptotically normally distributed.We illustrate our approach using data from an ecological momentaryassessment of smoking.