Center for HIV Identification, Prevention, and Treatment Services (CHIPTS)

Dr. Sung-Jae Lee is an Assistant Professor-in-Residence in the Department of Psychiatry and Biobehavioral Sciences at UCLA David Geffen School of Medicine and Core Scientist for CHIPTS Methods Core. Dr. Lee is an epidemiologist whose research has included adaptation of family-based interventions for HIV-affected families in Thailand, examination of HIV vaccine acceptability, assessment of HIV-testing preferences, and assessment of pre-exposure prophylaxis acceptability. He has conducted HIV research in Thailand for the past 14 years. His current ongoing research project focuses on designing a family-focused intervention for People Living with HIV in Thailand to assist them with their HIV disclosure decisions and pathways.

Dr. Scott Comulada is a biostatistician who has served on the UCLA School of Medicine faculty since he joined the Department of Psychiatry and Biobehavioral Sciences as an Assistant Professor-in-Residence in 2010. He has been a Statistician and then a Research Scientist for the Semel Institute Center for Community since 1999. Dr. Comulada was an Associate Director of the Methods Core for the Center for HIV Prevention, Identification, and Treatment Services (CHIPTS) from 2009 to 2010 and is currently a CHIPTS Methods Core Scientist. He earned his B.S. in Biophysics at Pacific Union College, Angwin. Dr. Comulada earned his M.P.H. in Public Health at Loma Linda University, Loma Linda. He earned his M.S. and Dr.P.H. in Biostatistics at the University of California, Los Angeles. Dr. Comulada is currently part of a cross-disciplinary team of scientists, including psychologists, sociologists, and computer scientists, who are developing research methods to assess and evaluate behavioral data from mobile phone-based health applications.

Presented on 4/10/12. Self-management of risk behaviors is a cornerstone of future population health. Using mobile phones for routine self-monitoring is a cost-efficient strategy for self-management. Despite benefits, new challenges are also introduced. Costs, logistics, and appropriateness of mobile phones for the intended population need to be considered. Daily reports that are common to mobile data collection versus retrospective self-reports that are common to traditional studies offer new opportunities to provide participant feedback and model behavior patterns. At the same time, new challenges are introduced in data management, presentation, user uptake, and analysis. In the first presentation, we will cover mobile phone-based study design scenarios and issues. The second presentation will cover analytic strategies to examine participant preferences using conjoint analysis around this new technology and time-series analyses to model daily reports.

presented on: 4/10/12. Self-management of risk behaviors is a cornerstone of future population health. Using mobile phones for routine self-monitoring is a cost-efficient strategy for self-management. Despite benefits, new challenges are also introduced. Costs, logistics, and appropriateness of mobile phones for the intended population need to be considered. Daily reports that are common to mobile data collection versus retrospective self-reports that are common to traditional studies offer new opportunities to provide participant feedback and model behavior patterns. At the same time, new challenges are introduced in data management, presentation, user uptake, and analysis. In the first presentation, we will cover mobile phone-based study design scenarios and issues. The second presentation will cover analytic strategies to examine participant preferences using conjoint analysis around this new technology and time-series analyses to model daily reports.

Abstract: Survey data often include missing values due to nonresponse. Especially, sensitive questions such as questions about income or assets tend to show higher percentage of missing values. When missing values occur, complete-case analysis may lead to biased estimates of parameters. Korean Longitudinal Study of Aging(KLoSA) is a longitudinal study to evaluate aging trends in the Korean population and apply the results to the social welfare and labor policy. KLoSA collected baseline data in 2006 and first follow-up data in 2008. We conducted multiple imputation based on hotdeck to handle missing values in KLoSA baseline and first follow-up data. In this study, we explain the imputation strategy adopted for filling in missing values of major outcome variables in KLoSA.