OBJECTIVE: To evaluate whether the use of salmeterol is associated with an increased risk of an asthma-related hospitalization or emergency care among asthma patients. METHODS: The data for this study were extracted from the Medstat MarketScan® claims databases occurring between 01/01/00 and 12/31/01. A nested case-control study design was employed. A cohort representing asthma patients was identified in 2000. Among the study cohort, hospitalized cases were identified as those who had the firsttime asthma-related hospitalization in 2001, and were matched to select controls by age (± 5 years), sex, and the number of ambulatory visits for asthma (5:1 control to case ratio). A similar process was applied to evaluating an asthma-related emergency department (ED) visit. The odds of prior salmeterol exposure among cases compared to controls were estimated using conditional multiple logistic regressions. The covariates of interest comprised age, prior hospital admission or ED visit for asthma, number of canisters of inhaled short-acting β₂ agonists and use of other asthma medications. RESULTS: A total of 35,312 subjects were eligible to be the study cohort. In addition, 285 and 640 subjects were identified as hospitalized and ED cases, respectively. The non-significant association was found when the prior salmeterol exposure was treated as a dichotomized variable. However, it was found that one unit increase in the number of canisters of salmeterol was associated with a seven percent decrease in the risk of a hospital admission for asthma (p <0.001). Additionally, current use of salmeterol was associated with a 48 percent decrease in the risk of an asthma-related hospitalization (OR = 0.52; p <0.001). The protective effect of salmeterol did not exist for those with recent or past use of salmeterol. Similar findings were observed for the ED visit outcome. CONCLUSIONS: The use of salmeterol was not found to be associated with an increased risk of an asthma-related hospital admission or ED visit. Conversely, one unit increase in the number of canisters of salmeterol and current use of salmeterol, respectively, were found to decrease the risk in an asthma-related hospitalization or ED visit among asthma patients.

This dissertation consists of three projects in matched case-control studies. In the first project, we employ a general bias preventive approach developed by Firth (1993) to handle the bias of an estimator of the log-odds ratio parameter in conditional logistic regression by solving a modified score equation. The resultant estimator not only reduces bias but also can prevent producing infinite value. Furthermore, we propose a method to calculate the standard error of the resultant estimator. A closed form expression for the estimator of the log-odds ratio parameter is derived in the case of a dichotomous exposure variable. Finite sample properties of the estimator are investigated via a simulation study. Finally, we apply the method to analyze a matched case-control data from a low-birth-weight study. In the second project of this dissertation, we propose a score typed test for checking adequacy of a functional form of a covariate of interest in matched case-control studies by using penalized regression splines to approximate an unknown function. The asymptotic distribution of the test statistics under the null model is a linear combination of several chi-square random variables. We also derive the asymptotic distribution of the test statistic when the alternative model holds. Through a simulation study we assess and compare the finite sample properties of the proposed test with that of Arbogast and Lin (2004). To illustrate the usefulness of the method, we apply the proposed test to a matched case-control data constructed from the breast cancer data of the SEER study. Usually a logistic model is needed to associate the risk of the disease with the covariates of interests. However, this logistic model may not be appropriate in some instances. In the last project , we adopt idea to matched case-control studies and derive an information matrix based test for testing overall model adequacy and investigate the properties against the cumulative residual based test in Arbogast and Lin (2004) via a simulation study. The proposed method is less time consuming and has comparative power for small parameters. It is suitable to explore the overall model fitting.

Semiparametric modeling is a combination of the parametric and nonparametric models in which some functions follow a known form and some others follow an unknown form. In this dissertation we made contributions to semiparametric modeling for matched case-crossover data. In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. Any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. However, some matching covariates such as time, and/or spatial location often play an important role as an effect modification. Failure to include them makes incorrect statistical estimation, prediction and inference. Hence in this dissertation, we propose several approaches that will allow the inclusion of time and spatial location as well as other effect modifications such as heterogeneous subpopulations among the data. To address modification due to time, three methods are developed: the first is a parametric approach, the second is a semiparametric penalized approach and the third is a semiparametric Bayesian approach. We demonstrate the advantage of the one stage semiparametric approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. To address modifications due to time and spatial location, two methods are developed: the first one is a semiparametric spatial-temporal varying coefficient model for a small number of locations. The second method is a semiparametric spatial-temporal varying coefficient model, and is appropriate when the number of locations among the subjects is medium to large. We demonstrate the accuracy of these approaches by using simulation studies, and when appropriate, an epidemiological example of a 1-4 bi-directional case-crossover study. Finally, to explore further effect modifications by heterogeneous subpopulations among strata we propose a nonparametric Bayesian approach constructed with Dirichlet process priors, which clusters subpopulations and assesses heterogeneity. We demonstrate the accuracy of our approach using a simulation study, as well a an example of a 1-4 bi-directional case-crossover study. / Ph. D.

A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing those complex data in this dissertation. We have then provided several contributions to semiparametric and nonparametric methods for dealing with the following problems: the first is to propose a method for testing the significance of a functional association under the matched study; the second is to develop a method to simultaneously identify important variables and build a network in HDHC data; the third is to propose a multi-class dynamic model for recognizing a pattern in the time-trend analysis. For the first topic, we propose a semiparametric omnibus test for testing the significance of a functional association between the clustered binary outcomes and covariates with measurement error by taking into account the effect modification of matching covariates. We develop a flexible omnibus test for testing purposes without a specific alternative form of a hypothesis. The advantages of our omnibus test are demonstrated through simulation studies and 1-4 bidirectional matched data analyses from an epidemiology study. For the second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We develop our approach under a semiparametric kernel machine regression framework, which can allow for the possibility that each variable might be nonlinear and is likely to interact with each other in a complicated way. We demonstrate our approach using simulation studies and real application on genetic pathway analysis. Lastly, for the third project, we propose a Bayesian focal-area detection method for a multi-class dynamic model under a Bayesian hierarchical framework. Two-step Bayesian sequential procedures are developed to estimate patterns and detect focal intervals, which can be used for gas chromatography. We demonstrate the performance of our proposed method using a simulation study and real application on gas chromatography on Fast Odor Chromatographic Sniffer (FOX) system. / Doctor of Philosophy / A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.

Background: Non-disclosure of HIV positive status to a partner threatens to reverse gains made in prevention of mother-to-child transmission (PMTCT) in resource limited settings. Determining the association between non-disclosure and infant HIV acquisition is important to justify focussing on disclosure as a strategy in PMTCT programmes. Objective: To determine the association between non-disclosure of HIV positive status to a partner and mother-to-child transmission (MTCT). Methods: Using a matched case-control design, we compared 34 HIV positive infants to 146 HIV negative infants and evaluated whether the mothers had disclosed their HIV status to their partner. Results: Non-disclosure was more frequent among cases (overall, 16.7%; cases, 52.8%; controls 7.6%), p<0.001 and significantly associated with MTCT (aOR 8.9 (3.0-26.3); p<0.0001), with male partner involvement partially mediating the effect of non-disclosure on MTCT. Conclusions: There is a need for PMTCT programs to focus on strategies to improve male partner involvement and partner disclosure without compromising the woman’s safety. / Health Studies / M. (Public Health)

Background: Non-disclosure of HIV positive status to a partner threatens to reverse gains made in prevention of mother-to-child transmission (PMTCT) in resource limited settings. Determining the association between non-disclosure and infant HIV acquisition is important to justify focussing on disclosure as a strategy in PMTCT programmes. Objective: To determine the association between non-disclosure of HIV positive status to a partner and mother-to-child transmission (MTCT). Methods: Using a matched case-control design, we compared 34 HIV positive infants to 146 HIV negative infants and evaluated whether the mothers had disclosed their HIV status to their partner. Results: Non-disclosure was more frequent among cases (overall, 16.7%; cases, 52.8%; controls 7.6%), p<0.001 and significantly associated with MTCT (aOR 8.9 (3.0-26.3); p<0.0001), with male partner involvement partially mediating the effect of non-disclosure on MTCT. Conclusions: There is a need for PMTCT programs to focus on strategies to improve male partner involvement and partner disclosure without compromising the woman’s safety. / Health Studies / M. (Public Health)

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