Person:

Meng, Xiao-li

The National Latino and Asian American Study (NLAAS) is a large scale survey of psychiatric epidemiology, the most comprehensive survey of this kind. A unique feature of NLAAS is its embedded experiment for estimating the effect of alternative orderings of interview questions. The findings from the experiment are not completely unexpected, but nevertheless alarming. Compared to the survey results from the widely used traditional ordering, the self-reported psychiatric service-use rates are often doubled or even tripled under a more sensible ordering introduced by NLAAS. These findings explain certain perplexing empirical findings in literature, but at the same time impose some grand challenges. For example, how can one assess racial disparities when different races were surveyed with different survey instruments that are now known to induce substantial differences? The project documented in this paper is part of an effort to address these questions. It creates models for imputing the original responses had the respondents under the traditional survey not taken advantage of the skip patterns to reduce interview time, which resulted in increased rates of incorrect negative responses over the course of the interview. The imputation modeling task is particularly challenging because of the complexity of the questionnaire, the small sample sizes for subgroups of interests, and the need for providing sensible imputation to whatever sub-population that a future user might be interested in studying. As a case study, we report both our findings and frustrations in our quest for dealing with these common real-life complications.

Motivated by the need to meaningfully implement the Institute of Medicine's (IOM's) definition of health care disparity, this paper proposes statistical frameworks that lay out explicitly the needed causal assumptions for defining disparity measures. Our key emphasis is that a scientifically defensible disparity measure must take into account the direction of the causal relationship between allowable covariates that are not considered to be contributors to disparity and non-allowable covariates that are considered to be contributors to disparity, to avoid flawed disparity measures based on implausible populations that are not relevant for clinical or policy decisions. However, these causal relationships are usually unknown and undetectable from observed data. Consequently, we must make strong causal assumptions in order to proceed. Two frameworks are proposed in this paper, one is the conditional disparity framework under the assumption that allowable covariates impact non-allowable covariates but not vice versa. The other is the marginal disparity framework under the assumption that non-allowable covariates impact allowable ones but not vice versa. We establish theoretical conditions under which the two disparity measures are the same and present a theoretical example showing that the difference between the two disparity measures can be arbitrarily large. Using data from the Collaborative Psychiatric Epidemiology Survey, we also provide an example where the conditional disparity is misled by Simpson's paradox, whereas the marginal disparity approach handles it correctly.