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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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