Approaches to the multivariate random variables associated with stochastic processes

Stochastic compartment models are widely used in modeling processes for biological populations. The residence time has been especially useful in describing the system dynamics in the models. The direct calculation of the distribution for the residence time of stochastic multi-compartment models is very complicated even with a relatively simple model and often impossible to calculate directly. This dissertation presents an analytical method to obtain the moment generating function for stochastic multi-compartment models and describe the distribution of the residence times, especially systems with nonexponential lifetime distributions. A common method for obtaining moments of the residence time is using the coefficient matrix, however it has a limitation in obtaining high order moments and moments for combined compartments in a system. In this dissertation, we first derive the bivariate moment generating function of the residence time distribution for stochastic two-compartment models with general lifetimes. It provides any order of moments and also enables us to approximate the density of the residence time using the saddlepoint approximation. The approximation method is applied to various situations including the approximation of the bivariate distribution of residence times in two-compartment models or approximations based on the truncated moment generating function. Special attention is given to the distribution of the residence time for multi-compartment semi-Markov models. The cofactor rule and the analytic approach to the two-compartment model facilitate the derivation of the moment generating function. The properties from the embedded Markov chain are also used to extend the application of the approach. This approach provides a complete specification of the residence time distribution based on the moment generating function and thus provides an easier calculation of high-order moments than the approach using the coefficient matrix. Applications to drug kinetics demonstrate the simplicity and usefulness of this approach

Two-sample density-based empirical likelihood ratio tests based on paired data, with application to a treatment study of Attention- Deficit/Hyperactivity Disorder and Severe Mood Dysregulation

Abstract It is a common practice to conduct medical trials in order to compare a new therapy with a standard-ofcare based on paired data consisted of pre-and post-treatment measurements. In such cases, a great interest often lies in identifying treatment effects within each therapy group as well as detecting a between-group difference. In this article, we propose exact nonparametric tests for composite hypotheses related to treatment effects to provide efficient tools that compare study groups utilizing paired data. When correctly specified, parametric likelihood ratios can be applied, in an optimal manner, to detect a difference in distributions of two samples based on paired data. The recent statistical literature introduces density-based empirical likelihood methods to derive efficient nonparametric tests that approximate most powerful Neyman-Pearson decision rules. We adapt and extend these methods to deal with various testing scenarios involved in the two-sample comparisons based on paired data. We show the proposed procedures outperform classical approaches. An extensive Monte Carlo study confirms that the proposed approach is powerful and can be easily applied to a variety of testing problems in practice. The proposed technique is applied for comparing two therapy strategies to treat children's attention deficit/hyperactivity disorder and severe mood dysregulation

Household-level risk factors for secondary influenza-like illness in a rural area of Bangladesh

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Objective To describe household‐level risk factors for secondary influenza‐like illness (ILI), an important public health concern in the low‐income population of Bangladesh. Methods Secondary analysis of control participants in a randomised controlled trial evaluating the effect of handwashing to prevent household ILI transmission. We recruited index‐case patients with ILI – fever (<5 years); fever, cough or sore throat (≥5 years) – from health facilities, collected information on household factors and conducted syndromic surveillance among household contacts for 10 days after resolution of index‐case patients’ symptoms. We evaluated the associations between household factors at baseline and secondary ILI among household contacts using negative binomial regression, accounting for clustering by household. Results Our sample was 1491 household contacts of 184 index‐case patients. Seventy‐one percentage reported that smoking occurred in their home, 27% shared a latrine with one other household and 36% shared a latrine with >1 other household. A total of 114 household contacts (7.6%) had symptoms of ILI during follow‐up. Smoking in the home (RRadj 1.9, 95% CI: 1.2, 3.0) and sharing a latrine with one household (RRadj 2.1, 95% CI: 1.2, 3.6) or >1 household (RRadj 3.1, 95% CI: 1.8–5.2) were independently associated with increased risk of secondary ILI. Conclusion Tobacco use in homes could increase respiratory illness in Bangladesh. The mechanism between use of shared latrines and household ILI transmission is not clear. It is possible that respiratory pathogens could be transmitted through faecal contact or contaminated fomites in shared latrines

Transformation of human bronchial epithelial cells alters responsiveness to inflammatory cytokines

BACKGROUND: Inflammation is commonly associated with lung tumors. Since inflammatory mediators, including members of the interleukin-6 (IL-6) cytokine family, suppress proliferation of normal epithelial cells, we hypothesized that epithelial cells must develop mechanisms to evade this inhibition during the tumorigenesis. This study compared the cytokine responses of normal epithelial cells to that of premalignant cells. METHODS: Short-term primary cultures of epithelial cells were established from bronchial brushings. Paired sets of brushings were obtained from areas of normal bronchial epithelium and from areas of metaplastic or dysplastic epithelium, or areas of frank endobronchial carcinoma. In 43 paired cultures, the signalling through the signal transducer and activator of transcription (STAT) and extracellular regulated kinase (ERK) pathways and growth regulation by IL-6, leukemia inhibitory factor (LIF), oncostatin M (OSM), interferon-γ (IFNγ) or epidermal growth factor (EGF) were determined. Inducible expression and function of the leukemia inhibitory factor receptor was assessed by treatment with the histone deacetylase inhibitor depsipeptide. RESULTS: Normal epithelial cells respond strongly to OSM, IFNγ and EGF, and respond moderately to IL-6, and do not exhibit a detectable response to LIF. In preneoplastic cells, the aberrant signaling that was detected most frequently was an elevated activation of ERK, a reduced or increased IL-6 and EGF response, and an increased LIF response. Some of these changes in preneoplastic cell signaling approach those observed in established lung cancer cell lines. Epigenetic control of LIF receptor expression by histone acetylation can account for the gain of LIF responsiveness. OSM and macrophage-derived cytokines suppressed proliferation of normal epithelial cells, but reduced inhibition or even stimulated proliferation was noted for preneoplastic cells. These alterations likely contribute to the supporting effects that inflammation has on lung tumor progression. CONCLUSION: This study indicates that during the earliest stage of premalignant transformation, a modified response to cytokines and EGF is evident. Some of the altered cytokine responses in primary premalignant cells are comparable to those seen in established lung cancer cell lines

CD24 Expression is an Independent Prognostic Marker in Cholangiocarcinoma

CD24 has been described as an adverse prognostic marker in several malignancies. This study evaluates CD24 expression in cholangiocarcinoma and correlates the findings with clinicopathologic data and patient survival. Between 1996 and 2002, 22 consecutive patients with cholangiocarcinoma were treated at our institution. Demographic data, SEER stage, pathologic data, treatment, expression of CD24, mitogen-activated protein kinase (MAPK), phosphorylated MAPK, and survival were analyzed. The majority of the tumors demonstrated CD24 (81.8%) and p-MAPK (87%) expression. A negative association was noted between the expression of CD24 and p-MAPK. Median survival for patients with low expression of CD24 was 36 months and high expression was 8 months. Median survival for patients who received chemotherapy with low CD24 expression was 163 months, and for seven patients with high CD24 expression, it was 17 months (p = 0.04). With the addition of radiation therapy, median survival for patients with low expression of CD24 was 52 months and high expression was 17 months (p = 0.08). On multivariate analysis, the use of chemotherapy (p = 0.0014, hazard ratio 0.069) and the CD24 overexpression (p = 0.02, hazard ratio 7.528) were predictive of survival. CD24 is commonly expressed in cholangiocarcinoma, and overexpression is predictive of poor survival and possibly of lack of response to chemotherapy and radiation therapy. These findings may improve selection of patients for the appropriate treatment modality and the development of CD24-targeted therapy

On the maximum total sample size of a group sequential test about bivariate binomial proportions

For testing "univariate" binomial proportions, it has been proven that, under mild conditions, there exist group sequential designs which satisfy the pre-specified Type I error and power of the single-stage design while the sample size is bounded above by that of the single-stage design (Kepner and Chang, 2003). In this article, we extend this result and prove the existence of such group sequential designs for various decision rules in the space of bivariate binomial variables. We also demonstrate how to obtain the actual group sequential designs for detecting changes in bivariate binomial variables.Bivariate binomial distribution Cancer clinical trials Cytostatic treatment Decision rule Toxicity evaluation

Likelihood testing populations modeled by autoregressive process subject to the limit of detection in applications to longitudinal biomedical data

Dependent and often incomplete outcomes are commonly found in longitudinal biomedical studies. We develop a likelihood function, which implements the autoregressive process of outcomes, incorporating the limit of detection problem and the probability of drop-out. The proposed approach incorporates the characteristics of the longitudinal data in biomedical research allowing us to carry out powerful tests to detect a difference between study populations in terms of the growth rate and drop-out rate. The formal notation of the likelihood function is developed, making it possible to adapt the proposed method easily for various different scenarios in terms of the number of groups to compare and a variety of growth trend patterns. Useful inferential properties for the proposed method are established, which take advantage of many well-developed theorems regarding the likelihood approach. A broad Monte-Carlo study confirms both the asymptotic results and illustrates good power properties of the proposed method. We apply the proposed method to three data sets obtained from mouse tumor experiments.