Cost-effectiveness of universal prophylaxis in pregnancy with prior group B streptococci colonization.

OBJECTIVE: To estimate the costs and outcomes of rescreening for group B streptococci (GBS) compared to universal treatment of term women with history of GBS colonization in a previous pregnancy. STUDY DESIGN: A decision analysis model was used to compare costs and outcomes. Total cost included the costs of screening, intrapartum antibiotic prophylaxis (IAP), treatment for maternal anaphylaxis and death, evaluation of well infants whose mothers received IAP, and total costs for treatment of term neonatal early onset GBS sepsis. RESULTS: When compared to screening and treating, universal treatment results in more women treated per GBS case prevented (155 versus 67) and prevents more cases of early onset GBS (1732 versus 1700) and neonatal deaths (52 versus 51) at a lower cost per case prevented ($8,805 versus $12,710). CONCLUSION: Universal treatment of term pregnancies with a history of previous GBS colonization is more cost-effective than the strategy of screening and treating based on positive culture results.

Prior history of atopy or autoimmunity increases risk for alopecia areata

Background. The association between a prior history of atopy or other autoimmune diseases and risk of alopecia areata is not well established. ^ Objective. Purpose of this study was to use the National Alopecia Areata Registry database to further investigate the association between history of atopy or other autoimmune diseases and risk of alopecia areata. ^ Methods. A total of 2,613 self-registered sporadic cases (n = 2,055) and controls (n = 558) were included in the present analysis. ^ Results. Possessing a history of any atopy (OR = 2.00; 95% CI 1.50-2.54) or autoimmune disease (OR = 1.73; 95% CI 1.10-2.72) was associated with an increased risk of alopecia areata. There was no trend for possessing a history of more than one atopy or autoimmune disease and increasing risk of alopecia areata. ^ Limitations. Recall, reporting, and recruiting bias are potential sources of limitations in this analysis. ^ Conclusion. This analysis revealed that a prior history of atopy and autoimmune disease was associated with an increased risk of alopecia areata and that the results were consistent for both the severe subtype of alopecia areata (i.e., alopecia totalis and alopecia universalis) and the localized subtype (i.e., alopecia areata persistent).^

A systematic review of selected birth outcomes in diabetic women who received preconception care and/or used multivitamins prior to pregnancy

Objective. To conduct a systematic review of published literature on preconception care in pre-existing diabetic women looking at the effect of glycemic control and multivitamin usage on the frequency of spontaneous abortion and birth defects.^ Methods. Articles were retrieved from Medline (1950–Dec 2007), Cochrane Library (1800–Dec 2007), Academic Search Complete (Ebsco) (Jan 1800–Dec 2007) and Maternal and Child Health Library (1965–Dec 2007). Studies included women with pre-existing, non-gestational diabetes and a comparison group. Participants must have either received preconception care and/or consumed a multivitamin as part of the study.^ Results. Overall, seven studies met the study criteria and applicability to the study objectives. Four of these reported the frequency of spontaneous abortion. Only one found a statistically significant increased risk of spontaneous abortion among pregnant women who did not receive preconception care compared with those who did receive care, odds ratio 4.32; 95% CI 1.34 to 13.9. Of the seven studies, six reported the frequency of birth defects. Five of these six studies found a significantly increased rate of birth defects among pregnant women who did not receive preconception care compared with those who did receive care, with odds ratios ranging from 1.53 to 10.16. All seven studies based their preconception care intervention on glycemic control. One study also used multivitamins as part of the preconception care.^ Conclusion. Glycemic control was shown to be useful in reducing the prevalence of birth defects, but not as useful in reducing the prevalence of spontaneous abortion. Insulin regimen options vary widely for the diabetic woman. No author excluded or controlled for women who may have been taking a multivitamin on their own. Due to the small amount of literature available, it is still not known which preconception care option, glucose control and/or multivitamin usage, provides better protection from birth defects and spontaneous abortion for the diabetic woman. An area for future investigation would be glycemic control and the use of folic acid started before pregnancy and the effects on birth defects and spontaneous abortion.^

Genetic epidemiology of gallbladder disease in Mexican Americans and cholesterol 7$\alpha$-hydroxylase gene sequence variation

Among Mexican Americans, the second largest minority group in the United States, the prevalence of gallbladder disease is markedly elevated. Previous data from both genetic admixture and family studies indicate that there is a genetic component to the occurrence of gallbladder disease in Mexican Americans. However, prior to this thesis no formal genetic analysis of gallbladder disease had been carried out nor had any contributing genes been identified.^ The results of complex segregation analysis in a sample of 232 Mexican American pedigrees documented the existence of a major gene having two alleles with age- and gender-specific effects influencing the occurrence of gallbladder disease. The estimated frequency of the allele increasing susceptibility was 0.39. The lifetime probabilities that an individual will be affected by gallbladder disease were 1.0, 0.54, and 0.00 for females of genotypes "AA", "Aa", and "aa", respectively, and 0.68, 0.30, and 0.00 for males, respectively. This analysis provided the first conclusive evidence for the existence of a common single gene having a large effect on the occurrence of gallbladder disease.^ Human cholesterol 7$\alpha$-hydroxylase is the rate-limiting enzyme in bile acid synthesis. The results of an association study in both a random sample and a matched case/control sample showed that there is a significant association between cholesterol 7$\alpha$-hydroxylase gene variation and the occurrence of gallbladder disease in Mexican Americans males but not in females. These data have implicated a specific gene, 7$\alpha$-hydroxylase, in the etiology of gallbladder disease in this population.^ Finally, I asked whether the inferred major gene from complex segregation analysis is genetically linked to the cholesterol 7$\alpha$-hydroxylase gene. Three pedigrees predicted to be informative for linkage analysis by virtue of supporting the major gene hypothesis and having parents with informative genotypes and multiple offspring were selected for this linkage analysis. In each of these pedigrees, the recombination fractions maximized at 0 with a positive, albeit low, LOD score. The results of this linkage analysis provide preliminary and suggestive evidence that the cholesterol 7$\alpha$-hydroxylase gene and the inferred gallbladder disease susceptibility gene are genetically linked. ^

Does prior receipt of a rifamycin antibiotic increase the risk of acquiring an infection due to a rifamycin-resistant strain of Clostridium difficile?

Objectives. To examine the association between prior rifamycin exposure and later development of C. difficile infection (CDI) caused by a rifamycin-resistant strain of C. difficile , and to compare patient characteristics between rifamycin-resistant strains of C. difficile infection and rifamycin-susceptible strains of C. difficile infection. ^ Methods. A case-control study was performed in a large university-affiliated hospital in Houston, Texas. Study subjects were patients with C. difficile infection acquired at the hospital with culture-positive isolates of C. difficile with which in vitro rifaximin and rifampin susceptibility has been tested. Prior use of rifamycin, demographic and clinical characteristics was compared between case and control groups using univariate statistics. ^ Results. A total of 49 C. difficile strains met the study inclusion criteria for rifamycin-resistant case isolates, and a total of 98 rifamycin-susceptible C. difficile strains were matched to case isolates. Of 49 case isolates, 12 (4%) were resistant to rifampin alone, 12 (4%) were resistant to rifaximin alone, and 25 (9%) were resistant to both rifampin and rifaximin. There was no significant association between prior rifamycin use and rifamycin-resistant CDI. Cases and controls did not differ according to demographic characteristics, length of hospital stay, known risk factors of CDI, type of CDI-onset, and pre-infection medical co-morbidities. Our results on 37 rifaximin-resistant isolates (MIC ≥32 &mgr;g/ml) showed more than half of isolates had a rifaximin MIC ≥256 &mgr;g/ml, and out of these isolates, 19 isolates had MICs ≥1024 &mgr;g/ml. ^ Conclusions. Using a large series of rifamycin-non-susceptible isolates, no patient characteristics were independently associated with rifamycin-resistant CDI. This data suggests that factors beyond previous use of rifamycin antibiotics are primary risk factors for rifamycin-resistant C. difficile. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^