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Bayesian Imputation Methods to Measure Quality of Life

The most widely used general health outcomes measure is the SF-36 Health Status questionnaire. The SF-36 is a 36 item general health survey which evaluates eight dimensions of health. This questionnaire is therapeutic non-specific. Often times, an analysis is done to determine if a subject’s quality of life is better on one drug than another. This can be beneficial to the patient when selecting a drug and to the company when marketing a drug. The SF-36 form is often used in clinical trials. One problem that is often encountered during a clinical trial is missing data. The industry standard for dealing with missing data of this type might not be the best. The industry standard of evaluating SF-36 data converts the data into eight score functions and treats the score functions as continuous data, even though they are discrete. We take a Bayesian perspective to obtain parameter estimates based on the posterior distribution of the model parameters. We employ Gibbs sampling to obtain simulation-based estimates. One of the practical advantages of our proposed method is that the MCMC method can be implemeted using WinBUGS. WinBUGS is a windows-based software package that is specialized for implementing MCMC-based analysis of full probability models. It allows the user to easily construct models and is available on the World Wide Web. In this thesis, we begin by presenting background information for modeling SF-36 health survey data. We then develop the method of estimating missing responses in quality of life data, taking into account the ordering in the data. We present two simulation studies to validate our proposed method. This method is applied to data from a clinical trial conducted by GlaxoSmithKline Pharmaceutical company. The trial is an open-label, multinational, parallel group study to evaluate the impact of oral Naratriptan 2.5mg on migraines. It has been observed that people in different countries respond differently to the SF-36 questionnaire. In order to account for these differences, we conclude this thesis by fitting an ordinal response model with varying cut-points. One benefit of this type of model is that it allows one to compare treatments across countries.

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Assessing teratogenicity of antiretroviral drugs: monitoring and analysis plan of the Antiretroviral Pregnancy Registry.

This paper describes the Antiretroviral Pregnancy Registry’s (APR) monitoring and analysis plan. APR is overseen by a committee of experts in obstetrics, pediatrics, teratology, infectious diseases, epidemiology and biostatistics from academia, government and the pharmaceutical industry. APR uses a prospective exposure-registration cohort design. Clinicians voluntarily register pregnant women with prenatal exposures to any antiretroviral therapy and provide fetal/neonatal outcomes. A birth defect is any birth outcome > or = 20 weeks gestation with a structural or chromosomal abnormality as determined by a geneticist. The prevalence is calculated by dividing the number of defects by the total number of live births and is compared to the prevalence in the CDC’s population-based surveillance system. Additionally, first trimester exposures, in which organogenesis occurs, are compared with second/third trimester exposures. Statistical inference is based on exact methods for binomial proportions. Overall, a cohort of 200 exposed newborns is required to detect a doubling of risk, with 80% power and a Type I error rate of 5%. APR uses the Rule of Three: immediate review occurs once three specific defects are reported for a specific exposure. The likelihood of finding three specific defects in a cohort of < or = 600 by chance alone is less than 5% for all but the most common defects. To enhance the assurance of prompt, responsible, and appropriate action in the event of a potential signal, APR employs the strategy of ‘threshold’. The threshold for action is determined by the extent of certainty about the cases, driven by statistical considerations and tempered by the specifics of the cases.

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A Quasi-Likelihood Approach for Overdispersed Binomial Data When N Is Unobserved

Several methods for the analysis of binomial data when the denominator, N, is unknown have been developed. Each of these methods requires that the mean of the distribution of N is known. In this article, we develop a quasi-likelihood technique that allows for the estimation of the means of the distributions needed to define the expected value and variance of the observed response and suggest a different form of the variance function. We illustrate the results of the proposed analysis and the results obtained when the mean of the distribution of N is assumed known through the analysis of a surviving jejunal crypt data set. Although the proposed method shows inflated standard errors of the parameter estimates in the cited example, the proposed method performs as well as a previously published method in all simulated conditions. Moreover, in cases where E(N) is misspecified, the proposed method outperforms the previously published method.

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Clinical Experience in Adults and Children Treated with Intravenous Peramivir for 2009 Influenza A (H1N1) Under an Emergency IND Program in the United States

Background. Peramivir, an investigational intravenous neuraminidase inhibitor in Phase 3 trials for hospitalized patients, was made available during the 2009 H1N1 influenza pandemic under the Emergency Investigational New Drug (eIND) regulations. We describe the clinical characteristics and outcomes of all patients for whom peramivir was requested under the eIND.

Methods. After obtaining eIND approval from the Food and Drug Administration and local institutional review board approval, clinicians caring for hospitalized patients with influenza administered intravenous peramivir and collected information on demographic characteristics, clinical characteristics, and outcomes.

Results. From April through October 2009, peramivir was requested for 42 patients and administered to 20 adults and 11 children. At hospitalization, all patients had rapidly progressing, radiographically confirmed viral pneumonia with respiratory failure, and all but 1 patient required mechanical ventilation. In most patients, including 1 person with documented oseltamivir-resistant infection, the illness had progressed despite oseltamivir treatment. Peramivir was administered for 1–14 days (median duration, 10 days). The 14-day, 28-day, and 56-day survival rates were 76.7%, 66.7%, and 59.0%, respectively. Peramivir was generally well tolerated.

Conclusions. Intravenous peramivir was well tolerated and was associated with recovery in most patients hospitalized with severe 2009 H1N1 influenza viral pneumonia and treated under an eIND.

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