A tutorial on propensity score estimation for multiple treatments using generalized boosted models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99037/1/sim_5753_Supplemental_Appendix_R2_12-26-2012.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99037/2/sim5753.pd

Exploratory Quantile Regression With Many Covariates: An Application to Adverse Birth Outcomes

Covariates may affect continuous responses differently at various points of the response distribution. For example, some exposure might have minimal impact on conditional means, whereas it might lower conditional 10th percentiles sharply. Such differential effects can be important to detect. In studies of the determinants of birth weight, for instance, it is critical to identify exposures like the one above, since low birth weight is a risk factor for later health problems. Effects of covariates on the tails of distributions can be obscured by models (such as linear regression) that estimate conditional means; however, effects on tails can be detected by quantile regression. We present 2 approaches for exploring high-dimensional predictor spaces to identify important predictors for quantile regression. These are based on the lasso and elastic net penalties. We apply the approaches to a prospective cohort study of adverse birth outcomes that includes a wide array of demographic, medical, psychosocial, and environmental variables. Although tobacco exposure is known to be associated with lower birth weights, the analysis suggests an interesting interaction effect not previously reported: tobacco exposure depresses the 20th and 30th percentiles of birth weight more strongly when mothers have high levels of lead in their blood compared with those who have low blood lead levels

Evaluating the Accuracy of 2020 Census Block-Level Estimates in California

In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access

Alcohol Pharmacology Education Partnership: Using Chemistry and Biology Concepts To Educate High School Students about Alcohol

We developed the Alcohol Pharmacology Education Partnership (APEP), a set of modules designed to integrate a topic of interest (alcohol) with concepts in chemistry and biology for high school students. Chemistry and biology teachers (<i>n</i> = 156) were recruited nationally to field-test APEP in a controlled study. Teachers obtained professional development either at a conference-based workshop (NSTA or NCSTA) or via distance learning to learn how to incorporate the APEP modules into their teaching. They field-tested the modules in their classes during the following year. Teacher knowledge of chemistry and biology concepts increased significantly following professional development, and was maintained for at least a year. Their students (<i>n</i> = 14 014) demonstrated significantly higher scores when assessed for knowledge of both basic and advanced chemistry and biology concepts compared to students not using APEP modules in their classes the previous year. Higher scores were achieved as the number of modules used increased. These findings are consistent with our previous studies, demonstrating higher scores in chemistry and biology after students use modules that integrate topics interesting to them, such as drugs (the Pharmacology Education Partnership)