This project considers how one might augment a limited amount of data from randomized controlled trial (RCT) with more plentiful data from an observational database (ODB), in order to estimate a causal effect. In our motivating setting, the ODB has better external validity, while the RCT has genuine randomization. We work with strata defined by the propensity score in the ODB. Subjects from the RCT are placed in strata defined by the propensity they would have had, had they been in the ODB. Our first method simply spikes the RCT data into their corresponding ODB strata. Our second method takes a data driven convex combination of the ODB and RCT treatment effect estimates within each stratum. Using the delta method and simulations we show that the spike-in method works best when the RCT covariates are drawn from the same distribution as in the ODB. Our convex combination method is more robust than the spike-in to covariate-based inclusion criteria that bias the RCT data. We apply our methods to data from the Women's Health Initiative, a study of thousands of postmenopausal women which has both observational and experimental data on hormone therapy (HT). Using half of the RCT to define a gold standard, we find that a version of the spiked-in estimate yields stable estimates of the causal impact of HT on coronary heart disease.
Prediction of individuals’ race and ethnicity plays an important role in studies of racial disparity. Bayesian Improved Surname Geocoding (BISG), which relies on detailed census information, has emerged as a leading methodology for this prediction task. Unfortunately, BISG suffers from two data problems. First, the census often contains zero counts for minority groups in the locations where members of those groups reside. Second, many surnames—especially those of minorities—are missing from the census data. We introduce a fully Bayesian BISG (fBISG) methodology that accounts for census measurement error by extending the naïve Bayesian inference of the BISG methodology. We also use additional data on last, first, and middle names taken from the voter files of six Southern states where self-reported race is available. Our empirical validation shows that the fBISG methodology and name supplements substantially improve the accuracy of race imputation, especially for racial minorities.
Although propylene-and-polyethylene-glycol and saline have been used in clinical siudies as placebos, their possible therapeutic role as wetting agents in the treatment of perennial rhinitis was investigated. Clinical and laboratory response to these agents was measured in eighteen patients during a 2-week baseline period and with 4 weeks of active treatment in a double blind randomized study. After 2 and 4 weeks there was a significant improvement compared to baseline in nasal function (/'<005) and blockage index (P<00\) combining both groups, with no difference between treatments. Patients had less sneezing at 2 and 4 weeks (P < 001), and less stuffiness at 4 weeks (P < 0 01). There was a significant correlation between improvement in blockage index and nasal biopsies when both were judged independently of the other. This study has demonstrated that wetting agents offer both subjective and objective improvement in the treatment of perennial rhinitis and merit consideration prior to (or along with) other agents with known systemic side effects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.