Simultaneous sign tests for marginal homogeneity of square contingency tables

For testing the nullhypothesis of marginal homogeneity of 1x1-contingency tables it is proposed to perform I simultaneous sign tests and to combine them according to the BONFFERRONI or the ?INáK inequality.     

The equivalence of the generalized McNemar tests for marginal homogeneity in 2(3) and 3(2) tables.

The McNemar test for correlated proportions has been generalized to test for marginal homogeneity of either a 2c or an m2 contingency table. The tests for 2(3) and 3(2) tables turn out to be equivalent since each involves six non-diagonal elements; equal numbers of degrees of freedom in the associated chi square tests, m - 1 = c - 1 = 2; identical constraints on the fits to the non-diagonal elements; and identical measures of discrepancy between observed and fitted values to be optimized.     

Testing for marginal independence between two categorical variables with multiple responses

Questions that ask respondents to "choose all that apply" from a set of items occur frequently in surveys. Categorical variables that summarize this type of survey data are called both pick any/c variables and multiple-response categorical variables. It is often of interest to test for independence between two categorical variables. When both categorical variables can have multiple responses, traditional Pearson chi-square tests for independence should not be used because of the within-subject dependence among responses. An intuitively constructed version of the Pearson statistic is proposed to perform the test using bootstrap procedures to approximate its sampling distribution. First- and second-order adjustments to the proposed statistic are given in order to use a chi-square distribution approximation. A Bonferroni adjustment is proposed to perform the test when the joint set of responses for individual subjects is unavailable. Simulations show that the bootstrap procedures hold the correct size more consistently than the other procedures.     

Ascidian molecular phylogeny inferred from mtDNA data with emphasis on the Aplousobranchiata

We explored the usefulness of mtDNA data in assessing phylogenetic relationships within the Ascidiacea. Although ascidians are a crucial group in studies of deuterostome evolution and the origin of chordates, little molecular work has been done to ascertain the evolutionary relationships within the class, and in the studies performed to date the key group Aplousobranchiata has not been adequately represented. We present a phylogenetic analysis based on mitochondrial cytochrome c oxidase subunit I (COI) sequences of 37 ascidian species, mainly Aplousobranchiata (26 species). Our data retrieve the main groups of ascidians, although Phlebobranchiata appeared paraphyletic in some analyses. Aplousobranch ascidians consistently appeared as a derived group, suggesting that their simple branchial structure is not a pleisiomorphic feature. Relationships between the main groups of ascidians were not conclusively determined, the sister group of Aplousobranchiata was the Stolidobranchiata or the Phlebobranchiata, depending on the analysis. Therefore, our data could not confirm an Enterogona clade (Aplousobranchiata+Phlebobranchiata). All of the tree topologies confirmed previous ideas, based on morphological and biochemical characters, suggesting that Cionidae and Diazonidae are members of the clade Aplousobranchiata, with Cionidae occupying a basal position within them in our analyses. Within the Aplousobranchiata, we found some stable clades that provide new data on the evolutionary relationships within this large group of ascidians, and that may prompt a re-evaluation of some morphological characters.     

On the asymptotics of marginal regression splines with longitudinal data

There have been studies on how the asymptotic efficiency ofa nonparametric function estimator depends on the handling ofthe within-cluster correlation when nonparametric regressionmodels are used on longitudinal or cluster data. In particular,methods based on smoothing splines and local polynomial kernelsexhibit different behaviour. We show that the generalized estimationequations based on weighted least squares regression splinesfor the nonparametric function have an interesting property:the asymptotic bias of the estimator does not depend on theworking correlation matrix, but the asymptotic variance, andtherefore the mean squared error, is minimized when the truecorrelation structure is specified. This property of the asymptoticbias distinguishes regression splines from smoothing splines.     

Pooling of impulse sequences,with emphasis on applications to neuronal spike data

Summary A mathematical model is presented that is supposed to describe those types of neuronal discharges which show a preponderance of short intervals, as well as one or more preferred intervals of a longer duration. It is assumed that via two channels impulses impinge upon a nerve cell and that each impulse gives rise to a response. The intervals between impulses in one channel are distributed according to an exponential, or an exponential-like, function; those in the other channel are distributed according to a monomodal, or a multimodal, function.The interval distributions and the expectation density (auto-correlation) functions of the model are in particular compared with data on thalamic neuron discharge patterns reported in the literature.The properties of superimposed time series of events would seem to be of a wider interest, stretching beyond the field of theoretical neurophysiology. It is indicated how the theory is of use in the detection of hidden rhythms in records which are composed of a mixture of different signals.     

Workmen's compensation; emphasis on rehabilitation

Since the first law was enacted in 1911 major emphasis has been placed on monetary satisfaction of liability with insufficient attention to rehabilitation of the occupationally disabled. An effective workmen's compensation program must have three basic goals: (1) Rehabilitation of the occupationally disabled; (2) assured, prompt, and adequate indemnity for the occupationally disabled or their survivors; and (3) minimal costs to employers and society commensurate with the first two goals. It is suggested that the medical societies of each state provide a broadly representative committee to advise the administrative agency on medical policies and practices. This committee would prepare registers of all physicians in each locality who are willing and qualified to accept calls for service to injured employees, would mediate complaints originating with the employee, the employer, the insurance carrier or the administrative agency, and would cooperate with the administrative agency in educational programs for all concerned. It is the physician's responsibility to help the administrative agency in shifting the emphasis from indemnity to rehabilitation. The disabled employee is entitled to all services available to restore him to an earning capacity.     

A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates

Longitudinal data often contain missing observations and error-prone covariates. Extensive attention has been directed to analysis methods to adjust for the bias induced by missing observations. There is relatively little work on investigating the effects of covariate measurement error on estimation of the response parameters, especially on simultaneously accounting for the biases induced by both missing values and mismeasured covariates. It is not clear what the impact of ignoring measurement error is when analyzing longitudinal data with both missing observations and error-prone covariates. In this article, we study the effects of covariate measurement error on estimation of the response parameters for longitudinal studies. We develop an inference method that adjusts for the biases induced by measurement error as well as by missingness. The proposed method does not require the full specification of the distribution of the response vector but only requires modeling its mean and variance structures. Furthermore, the proposed method employs the so-called functional modeling strategy to handle the covariate process, with the distribution of covariates left unspecified. These features, plus the simplicity of implementation, make the proposed method very attractive. In this paper, we establish the asymptotic properties for the resulting estimators. With the proposed method, we conduct sensitivity analyses on a cohort data set arising from the Framingham Heart Study. Simulation studies are carried out to evaluate the impact of ignoring covariate measurement error and to assess the performance of the proposed method.     

Testing trend for count data with extra-Poisson variability

Trend tests for monotone trend or umbrella trend (monotone upward changing to monotone downward or vise versa) in count data are proposed when the data exhibit extra-Poisson variability. The proposed tests, which are called the GS1 test and the GS2 test, are constructed by applying an orthonormal score vector to a generalized score test under an rth-order log-linear model. These tests are compared by simulation with the Cochran-Armitage test and the quasi-likelihood test of Piegorsch and Bailer (1997, Statistics for Environmental Biology and Toxicology). It is shown that the Cochran-Armitage test should not be used under the existence of extra-Poisson variability; that, for detecting monotone trend, the GS1 test is superior to the others; and that the GS2 test has high power to detect an umbrella response.     

Testing for differentially expressed genes with microarray data

This paper compares the type I error and power of the one- and two-sample t-tests, and the one- and two-sample permutation tests for detecting differences in gene expression between two microarray samples with replicates using Monte Carlo simulations. When data are generated from a normal distribution, type I errors and powers of the one-sample parametric t-test and one-sample permutation test are very close, as are the two-sample t-test and two-sample permutation test, provided that the number of replicates is adequate. When data are generated from a t-distribution, the permutation tests outperform the corresponding parametric tests if the number of replicates is at least five. For data from a two-color dye swap experiment, the one-sample test appears to perform better than the two-sample test since expression measurements for control and treatment samples from the same spot are correlated. For data from independent samples, such as the one-channel array or two-channel array experiment using reference design, the two-sample t-tests appear more powerful than the one-sample t-tests.     

Detecting hypermutations in viral sequences with an emphasis on G --> A hypermutation

SUMMARY: This program compares sequence sets to a reference sequence, tallies G --> A hypermutations, and presents the results in various tables and graphs, which include dinucleotide context, summaries of all observed nucleotide changes, and stop codons introduced by hypermutation. AVAILABILITY: www.hiv.lanl.gov/HYPERMUT/hypermut.html     

A general method for constructing increment-decrement life tables that agree with the data

A general method for making increment-decrement life tables is presented. The method involves the finding of probabilities of transition between states, graduated to small intervals of time and age, that are consistent with (i.e., can reproduce) the data, whether the data consist of central age-state specific rates, or some other feature, such as state distributions of a real cohort. The method is then illustrated with a fetal loss life table.     

Incorporating marginal covariate information in a nonparametric regression model for a sample of R x C tables

SUMMARY: Nonparametric regression models are proposed in the framework of ecological inference for exploratory modeling of disease prevalence rates adjusted for variables, such as age, ethnicity/race, and socio-economic status. Ecological inference is needed when a response variable and covariate are not available at the subject level because only summary statistics are available for the reporting unit, for example, in the form of R x C tables. In this article, only the marginal counts are assumed available in the sample of R x C contingency tables for modeling the joint distribution of counts. A general form for the ecological regression model is proposed, whereby certain covariates are included as a varying coefficient regression model, whereas others are included as a functional linear model. The nonparametric regression curves are modeled as splines fit by penalized weighted least squares. A data-driven selection of the smoothing parameter is proposed using the pointwise maximum squared bias computed from averaging kernels (explained by O'Sullivan, 1986, Statistical Science 1, 502-517). Analytic expressions for bias and variance are provided that could be used to study the rates of convergence of the estimators. Instead, this article focuses on demonstrating the utility of the estimators in a study of disparity in health outcomes by ethnicity/race.