Bayesian Imputation Methods to Measure Quality of Life with Missing Data

• Activity Number: 82
• Type: Contributed
• Date/Time: Monday, August 12, 2002 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section*
• Author(s): Amy Umbach*+ and Sujit Ghosh
• Affiliation(s): North Carolina State University and North Carolina State University
• Keywords: Bayesian imputation ; Missing at random ; Ordinal data ; Quality of life ; Questionnaire survey

Abstract

The most widely used general health outcomes measure is the SF-36 Health Status questionnaire. The SF-36 is a thirty-six item general health survey that evaluates eight dimensions of health. This questionnaire is therapeutic non-specific. Often times, analysis is done to determine if a subject's quality of life is better on one drug than another. This can be beneficial when marketing a drug. Thus, the SF-36 form is often used in clinical trials. One problem that is often encountered during a clinical trial is missing data. The current method for dealing with missing data might not be the best. In this paper, we develop a new method to estimate missing responses in quality of life data. We present 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. The current method of evaluating SF-36 data converts the data into eight score functions and treats the score functions as continuous data. When applying our method, we will treat the data as categorical.