Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models

In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.     

Semiparametric regression splines in matched case-control studies

We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: 1) an approximate cross-validation scheme to estimate the smoothing parameter inherent in regression splines, as well as 2) Monte Carlo expectation maximization (MCEM) and 3) Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM, and Bayesian approaches using simulation, showing that they appear approximately equally efficient; the approximate cross-validation method is computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.     

A graph-theoretic approach to the partition of individuals into full-sib families

We present an algorithm to partition a single generation of individuals into full-sib families using single-locus co-dominant marker data. Pairwise likelihood ratios are used to create a graph that represents the full-sib relationships within the data set. Connected-component and minimum-cut algorithms from the graph theory are then employed to find the full-sib families within the graph. The results of a large-scale simulation study show that the algorithm is able to produce accurate partitions when applied to data sets with eight or more loci. Although the algorithm performs best when the distribution of allele frequencies and family sizes in a data set is uniform, the inclusion of more loci or alleles per locus allows accurate partitions to be created from data sets in which these distributions are highly skewed.     

Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach

The function of a protein is closely correlated with its subcellular location. With the success of human genome project and the rapid increase in the number of newly found protein sequences entering into data banks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will no doubt expedite the functionality determination of newly found proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Based on the concept of pseudo amino acid composition originally proposed by K. C. Chou (Proteins: Struct. Funct. Genet. 43: 246–255, 2001), the digital signal processing approach has been introduced to partially incorporate the sequence order effect. One of the remarkable merits by doing so is that many existing tools in mathematics and engineering can be straightforwardly used in predicting protein subcellular location. The results thus obtained are quite encouraging. It is anticipated that the digital signal processing may serve as a useful vehicle for many other protein science areas as well.     

Top-down anticipatory control in prefrontal cortex

Summary The prefrontal cortex has been implicated in a wide variety of executive functions, many involving some form of anticipatory attention. Anticipatory attention involves the pre-selection of specific sensory circuits to allow fast and efficient stimulus processing and a subsequently fast and accurate response. It is generally agreed that the prefrontal cortex plays a critical role in anticipatory attention by exerting a facilitatory “top-down” bias on sensory pathways. In this paper we review recent results indicating that synchronized activity in prefrontal cortex, during anticipation of visual stimulus, can predict features of early visual stimulus processing and behavioral response. Although the mechanisms involved in anticipatory attention are still largely unknown, we argue that the synchronized oscillation in prefrontal cortex is a plausible candidate during sustained visual anticipation. We further propose a learning hypothesis that explains how this top-down anticipatory control in prefrontal cortex is learned based on accumulated prior experience by adopting a Temporal Difference learning algorithm.     

Negative binomial additive models

The generalized additive model is extended to handle negative binomial responses. The extension is complicated by the fact that the negative binomial distribution has two parameters and is not in the exponential family. The methodology is applied to data involving DNA adduct counts and smoking variables among ex-smokers with lung cancer. A more detailed investigation is made of the parametric relationship between the number of adducts and years since quitting while retaining a smooth relationship between adducts and the other covariates.     

Common EF-hand motifs in cholinesterases and neuroligins suggest a role for Ca2+ binding in cell surface associations

Comparisons of protein sequence via cyclic training of Hidden Markov Models (HMMs) in conjunction with alignments of three-dimensional structure, using the Combinatorial Extension (CE) algorithm, reveal two putative EF-hand metal binding domains in acetylcholinesterase. Based on sequence similarity, putative EF-hands are also predicted for the neuroligin family of cell surface proteins. These predictions are supported by experimental evidence. In the acetylcholinesterase crystal structure from Torpedo californica, the first putative EF-hand region binds the Zn2+ found in the heavy metal replacement structure. Further, the interaction of neuroligin 1 with its cognate receptor neurexin depends on Ca2+. Thus, members of the alpha,beta hydrolase fold family of proteins contain potential Ca2+ binding sites, which in some family members may be critical for heterologous cell associations.     

A transitional model for longitudinal binary data subject to nonignorable missing data

Binary longitudinal data are often collected in clinical trials when interest is on assessing the effect of a treatment over time. Our application is a recent study of opiate addiction that examined the effect of a new treatment on repeated urine tests to assess opiate use over an extended follow-up. Drug addiction is episodic, and a new treatment may affect various features of the opiate-use process such as the proportion of positive urine tests over follow-up and the time to the first occurrence of a positive test. Complications in this trial were the large amounts of dropout and intermittent missing data and the large number of observations on each subject. We develop a transitional model for longitudinal binary data subject to nonignorable missing data and propose an EM algorithm for parameter estimation. We use the transitional model to derive summary measures of the opiate-use process that can be compared across treatment groups to assess treatment effect. Through analyses and simulations, we show the importance of properly accounting for the missing data mechanism when assessing the treatment effect in our example.     

Standard errors for EM estimates in generalized linear models with random effects

A procedure is derived for computing standard errors of EM estimates in generalized linear models with random effects. Quadrature formulas are used to approximate the integrals in the EM algorithm, where two different approaches are pursued, i.e., Gauss-Hermite quadrature in the case of Gaussian random effects and nonparametric maximum likelihood estimation for an unspecified random effect distribution. An approximation of the expected Fisher information matrix is derived from an expansion of the EM estimating equations. This allows for inferential arguments based on EM estimates, as demonstrated by an example and simulations.