Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics.

Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.

Community-based participatory research approach to evidence-based research: lessons from the Pacific Islander American Health Study.

OBJECTIVES: Reports on the challenges and lessons learned from the Pacific Island American Health Study engagement with community-based organizations (CBOs) and faith-based organizations (FBOs) in Pacific Islander (PI) communities and mechanisms to facilitate the collection of robust data. METHODS: Academic-community partnership building was achieved with PI CBOs and FBOs. Focus group meetings were organized to plan various aspects of the study, develop questionnaire themes and protocols for survey, assist with the interviewer recruitment process, and strategize data dissemination plan. LESSONS LEARNED: The PIA-HS represents a model for overcoming challenges in data collection among small understudied populations. FBOs represent a valuable resource for community-based participatory research (CBPR) data collection and for effective interventions. CONCLUSION: The study methodology can be replicated for other racial/ethnic groups with high levels of religiosity combined with concentrated levels of residential clustering. Expansion of the Pacific Islander American Health Study (PIA-HS) to include other PI subgroups is encouraged.

Use of multiple imputation to correct for nonresponse bias in a survey of urologic symptoms among African-American men.

The Flint Men's Health Study is an ongoing population-based study of African-American men designed to address questions related to prostate cancer and urologic symptoms. The initial phase of the study was conducted in 1996-1997 in two stages: an interviewer-administered survey followed by a clinical examination. The response rate in the clinical examination phase was 52%. Thus, some data were missing for clinical examination variables, diminishing the generalizability of the results to the general population. This paper is a case study demonstrating the application of multiple imputation to address important questions related to prostate cancer and urologic symptoms in a data set with missing values. On the basis of the observed clinical examination data, the American Urological Association Symptoms Score showed a surprising reduction in symptoms in the oldest age group, but after multiple imputation there was a monotonically increasing trend with age. It appeared that multiple imputation corrected for nonresponse bias associated with the observed data. For other outcome measures-namely, the age-adjusted 95th percentile of prostate-specific antigen level and the association between urologic symptoms and prostate volume-results from the observed data and the multiply imputed data were similar.