Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

AUGMENTATION OF RECONSTITUTION OF GUT ASSOCIATED LYMPHOID TISSUE IN THE SYNGENEIC MURINE BONE MARROW TRANSPLANTATION MODEL

Gut was studied as a prototypical mucosal membrane in the murine BDF-1 syngeneic bone marrow transplant model. Measures of jejunal intraepithelial lymphocytes (IELs) and crypt cells were obtained by standard techniques and a method of quantifying gut lamina propria plasma cells (PCs) was developed. The degree of ablation of gut PCs and IELs after 900 rads total body irradiation with ('60)Co, and their repopulation effected by transplantation with 2.0 x 10('5) or 1.0 x 10('6) bone marrow cells demonstrated a prolonged period of profound depression in population levels of these cells which was not reflected by the extent of damage sustained to the epithelium. Differences in the depopulation and recovery of gut PCs and IELs revealed a tendency towards initial differentiation of effector cells. A positive dose response to high bone marrow cell innocula was obtained. Subsequent studies determined that gut IEL and PC repopulation was potentiated by the addition of IELs or buffy coat cells (BCs) to the bone marrow transplant. A method of isolating 1.4 - 4.0 x 10('7) viable IELs per gram of murine small bowel was devised employing intralumenal hyaluronidase digestion of the epithelial layer and centrifugation of the resulting suspension through discontinuous Percoll gradients. Irradiated mice received 2.0 x 10('5) bone marrow cells along with an equal number of IELs or BCs. The extent and duration of depression of numbers of IELs and PCs was markedly reduced by the addition of the IEL isolate to the transplantation innocula, and to a lesser degree by the addition of BCs. The augmentation of repopuation far exceeded that expected by simple lodging of cells suggesting that the additionally transplanted cells contained a subpopulation of mucosal membrane lymphoid stem cells or helper cells. Correlation analysis of PC versus IEL levels suggests a possible feedback mechanism governing the relative size of their populations. Normal ratios of IgA, IgM, and IgG bearing PCs was maintained post transplantation with all of the regimens. ^

A multiplicative model for standardizing vital rates without using external standards

Standardization is a common method for adjusting confounding factors when comparing two or more exposure category to assess excess risk. Arbitrary choice of standard population in standardization introduces selection bias due to healthy worker effect. Small sample in specific groups also poses problems in estimating relative risk and the statistical significance is problematic. As an alternative, statistical models were proposed to overcome such limitations and find adjusted rates. In this dissertation, a multiplicative model is considered to address the issues related to standardized index namely: Standardized Mortality Ratio (SMR) and Comparative Mortality Factor (CMF). The model provides an alternative to conventional standardized technique. Maximum likelihood estimates of parameters of the model are used to construct an index similar to the SMR for estimating relative risk of exposure groups under comparison. Parametric Bootstrap resampling method is used to evaluate the goodness of fit of the model, behavior of estimated parameters and variability in relative risk on generated sample. The model provides an alternative to both direct and indirect standardization method. ^

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^