Sample size determination for adaptive crossover trial in detecting gene-drug interactions.

Parallel design and crossover design are two of the most frequently used designs for studying drug-gene interactions. Due to the concerns of statistical power and ethics, it is often more prudent to use the crossover design while allowing the patients to have choices of not switching the treatment if the first stage treatment is effective. This complicates the calculation of the required sample size to achieve pre-specified statistical power.

Testing generalized linear models with high-dimensional nuisance parameter.

Generalized linear models often have a high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional sub-vector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and thus are computationally expensive.

Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures.

Exposures to environmental pollutants are often composed of mixtures of chemicals that can be highly correlated because of similar sources and/or chemical structures. The effect of an individual chemical on a health outcome can be weak and difficult to detect because of the relatively low level of exposures to many environmental pollutants. To tackle the challenging problem of assessing the health risk of exposure to a mixture of environmental pollutants, we propose a statistical approach to assessing the proportion of the variation of an outcome explained by a mixture of pollutants.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations.

Dexmedetomidine enhances hypoxia-induced cancer cell progression.

Dexmedetomidine (DEX) is widely used in perioperative settings for analgesia and sedation; however, little is known about its effects on the hypoxia-induced progression of tumor cells. In the present study, the effects of DEX on hypoxia-induced growth and metastasis of lung cancer cells and colorectal cancer cells was examined. A549 cells and HCT116 cells were treated with normoxia, hypoxia, co-treatment of hypoxia and DEX, and atipamezole (an α adrenoceptor antagonist) for 4 h.

Metallic air pollutants and breast cancer heterogeneity.

Background: Emerging evidence suggests airborne metals may be associated with breast cancer risk. However, breast cancer is heterogenous and associations with heavy metals vary by subtype. Heavy metals possess both carcinogenic and xenoestrogenic properties which may be related to different tumor etiologies.

Candidate gene DNA methylation associations with breast cancer characteristics and tumor progression.

Aim: We examined methylation patterns with aggressive tumor phenotypes and investigated demographic, socioeconomic and reproductive predictors of gene methylation.

Materials & Methods: Pyrosequencing quantified methylation of BRCA1, EGFR, GSTM2, RASSF1, TFF1 and Sat 2. We used quantile regression models to calculate adjusted median methylation values by estrogen and progesterone receptor (ER/PR) status.

Common vitamin D pathway gene variants reveal contrasting effects on serum vitamin D levels in African Americans and European Americans.

Vitamin D deficiency is more common among African Americans (AAs) than among European Americans (EAs), and epidemiologic evidence links vitamin D status to many health outcomes. Two genome-wide association studies (GWAS) in European populations identified vitamin D pathway gene single-nucleotide polymorphisms (SNPs) associated with serum vitamin D [25(OH)D] levels, but a few of these SNPs have been replicated in AAs. Here, we investigated the associations of 39 SNPs in vitamin D pathway genes, including 19 GWAS-identified SNPs, with serum 25(OH)D concentrations in 652 AAs and 405 EAs.

Semiparametric odds ratio model for case-control and matched case-control designs.

We propose a semiparametric odds ratio model that extends Umbach and Weinberg's approach to exploiting gene-environment association model for efficiency gains in case-control designs to both discrete and continuous data. We directly model the gene-environment association in the control population to avoid estimating the intercept in the disease risk model, which is inherently difficult because of the scarcity of information on the parameter with the sampling designs. We propose a novel permutation-based approach to eliminate the high-dimensional nuisance parameters in the matched case-control design.

Bias correction to secondary trait analysis with case-control design.

In genetic association studies with densely typed genetic markers, it is often of substantial interest to examine not only the primary phenotype but also the secondary traits for their association with the genetic markers. For more efficient sample ascertainment of the primary phenotype, a case-control design or its variants, such as the extreme-value sampling design for a quantitative trait, are often adopted. The secondary trait analysis without correcting for the sample ascertainment may yield a biased association estimator.

Improving power and robustness for detecting genetic association with extreme-value sampling design.

Extreme-value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as "cases" and "controls" and analyzed using logistic regression. Such a case-control analysis ignores the potential dose-response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association.

On information coded in gene-environment independence in case-control studies.

For analysis of case-control genetic association studies, it has recently been shown that gene-environment independence in the population can be leveraged to increase efficiency for estimating gene-environment interaction effects in comparison with the standard prospective analysis. However, for the special case in which data on the binary phenotype and genetic and environmental risk factors can be summarized in a 2 × 2 × 2 table, the authors show here that there is no efficiency gain for estimating interaction effects, nor is there an efficiency gain for estimating the genetic and environmental main effects. This contrasts with the well-known result assuming that rare phenotype prevalence and gene-environment independence in the control population for the same data can lead to efficiency gain.

Representations of efficient score for coarse data problems based on Neumann series expansion.

We derive new representations of the efficient score for coarse data problems based on Neumann series expansion. The representations can be applied to both ignorable and nonignorable coarse data. An approximation to the new representation may be used for computing locally efficient scores in such problems.

Multiplex immunoassay of lower genital tract mucosal fluid from women attending an urban STD clinic shows broadly increased IL1ß and lactoferrin.

Background: More than one million new cases of sexually transmitted diseases (STDs) occur each day. The immune responses and inflammation induced by STDs and other frequent non-STD microbial colonizations (i.e.

Multiple imputation for missing values through conditional Semiparametric odds ratio models.

Multiple imputation is a practically useful approach to handling incompletely observed data in statistical analysis. Parameter estimation and inference based on imputed full data have been made easy by Rubin's rule for result combination. However, creating proper imputation that accommodates flexible models for statistical analysis in practice can be very challenging.

Compatibility of conditionally specified models.

A conditionally specified joint model is convenient to use in fields such as spatial data modeling, Gibbs sampling, and missing data imputation. One potential problem with such an approach is that the conditionally specified models may be incompatible, which can lead to serious problems in applications. We propose an odds ratio representation of a joint density to study the issue and derive conditions under which conditionally specified distributions are compatible and yield a joint distribution.

On L convergence of Neumann series approximation in missing data problems.

The inverse of the nonparametric information operator is key to finding doubly robust estimators and the semiparametric efficient estimator in missing data problems. It is known that no closed-form expression for the inverse of the nonparametric information operator exists when missing data form nonmonotone patterns. Neumann series is usually applied to approximate the inverse.

Estimation and inference based on Neumann series approximation to locally efficient score in missing data problems.

Theory on semiparametric efficient estimation in missing data problems has been systematically developed by Robins and his coauthors. Except in relatively simple problems, semiparametric efficient scores cannot be expressed in closed forms. Instead, the efficient scores are often expressed as solutions to integral equations.

Estimation of average treatment effect with incompletely observed longitudinal data: application to a smoking cessation study.

We study the problem of estimation and inference on the average treatment effect in a smoking cessation trial where an outcome and some auxiliary information were measured longitudinally, and both were subject to missing values. Dynamic generalized linear mixed effects models linking the outcome, the auxiliary information, and the covariates are proposed. The maximum likelihood approach is applied to the estimation and inference on the model parameters.

Positive association between HIV RNA and IL-6 in the genital tract of Rwandan women.

Infections and inflammation in the genital tract can influence HIV expression or HIV susceptibility. The goal of this study was to determine if significant relationships exist between cytokines and HIV in genital tract secretions from 57 HIV-seropositive Rwandan women. Genital tract secretions were obtained by cervicovaginal lavage (CVL).

Mucosal HIV-binding antibody and neutralizing activity in high-risk HIV-uninfected female participants in a trial of HIV-vaccine efficacy.

Background: This study investigated gp120-binding antibody and neutralizing activity, at the gingival- and cervical-mucosal levels, in response to a bivalent gp120 candidate vaccine.

Methods: Women who met the study's inclusion criteria for documented high-risk behaviors participated in a nested substudy of the multicenter phase 3 trial of human immunodeficiency virus (HIV)-vaccine efficacy, VAX004. Gingival, cervicovaginal lavage, and plasma specimens were collected at 6-month intervals for 3 years.

One-year follow-up to a randomized controlled trial of an energy conservation course for persons with multiple sclerosis.

In this randomized controlled trial, 169 persons with multiple sclerosis were randomly assigned to an immediate intervention group or a delayed control group using a crossover design. The outcome measures (Fatigue Impact Scale and SF-36 Health Survey) were measured four times before and after courses. This study investigated whether the immediate benefits of a 6-week, community-based, energy conservation course for persons with multiple sclerosis were maintained at 1-year follow-up.

Cervicovaginal levels of lactoferrin, secretory leukocyte protease inhibitor, and RANTES and the effects of coexisting vaginoses in human immunodeficiency virus (HIV)-seronegative women with a high risk of heterosexual acquisition of HIV infection.

Innate immune factors in mucosal secretions may influence human immunodeficiency virus type 1 (HIV-1) transmission. This study examined the levels of three such factors, genital tract lactoferrin [Lf], secretory leukocyte protease inhibitor [SLPI], and RANTES, in women at risk for acquiring HIV infection, as well as cofactors that may be associated with their presence. Women at high risk for HIV infection meeting established criteria (n = 62) and low-risk controls (n = 33) underwent cervicovaginal lavage (CVL), and the CVL fluid samples were assayed for Lf and SLPI.

Relationship of U1 cell HIV-stimulatory activity to bacterial vaginosis and HIV genital tract virus load.

Bacterial vaginosis (BV) has been associated with HIV sexual transmission and increased levels of genital tract HIV RNA. We postulated that BV induces the appearance of substances in the genital tract that stimulate HIV expression locally. To test this, we measured HIV RNA levels in genital mucosal fluid from women with or without BV (defined by Nugent score) and compared them with the ability of those fluids to stimulate HIV expression in the chronically HIV-infected monocytic line U1.