Zobrazeno 1 - 10
of 490
pro vyhledávání: '"Griffin, Beth Ann"'
Autor:
Antonelli, Joseph, Rubinstein, Max, Agniel, Denis, Smart, Rosanna, Stuart, Elizabeth, Cefalu, Matthew, Schell, Terry, Eagan, Joshua, Stone, Elizabeth, Griswold, Max, Sorbero, Mark, Griffin, Beth Ann
Motivated by the study of state opioid policies, we propose a novel approach that uses autoregressive models for causal effect estimation in settings with panel data and staggered treatment adoption. Specifically, we seek to estimate of the impact of
Externí odkaz:
http://arxiv.org/abs/2408.09012
Autor:
Griffin, Beth Ann, Schuler, Megan S., Stone, Elizabeth M., Patrick, Stephen W., Stein, Bradley D., de Lima, Pedro Nascimento, Griswold, Max, Scherling, Adam, Stuart, Elizabeth A.
Background: Policy evaluation studies that assess how state-level policies affect health-related outcomes are foundational to health and social policy research. The relative ability of newer analytic methods to address confounding, a key source of bi
Externí odkaz:
http://arxiv.org/abs/2307.01413
Autor:
May, Larissa S, Griffin, Beth Ann, Bauers, Nicole Maier, Jain, Arvind, Mitchum, Marsha, Sikka, Neal
Publikováno v:
Western Journal of Emergency Medicine, Vol 11, Iss 1, Pp 1-9 (2010)
Background: The purpose of syndromic surveillance is early detection of a disease outbreak. Such systems rely on the earliest data, usually chief complaint. The growing use of electronic medical records (EMR) raises the possibility that other data, s
Externí odkaz:
https://doaj.org/article/33b02eb04de04e5a9272abd06fa1f604
Autor:
Griffin, Beth Ann, Schuler, Megan S., Cefalu, Matt, Ayer, Lynsay, Godley, Mark, Greifer, Noah, Coffman, Donna L., McCaffrey, Daniel
Objective. To provide step-by-step guidance and STATA and R code for using propensity score (PS) weighting to estimate moderation effects. Research Design. Tutorial illustrating the key steps for estimating and testing moderation using observational
Externí odkaz:
http://arxiv.org/abs/2204.03345
Background/aims: While randomized controlled trials are the gold standard for measuring causal effects, robust conclusions about causal relationships can be obtained using data from observational studies if proper statistical techniques are used to a
Externí odkaz:
http://arxiv.org/abs/2112.05035
Autoregressive models are widely used for the analysis of time-series data, but they remain underutilized when estimating effects of interventions. This is in part due to endogeneity of the lagged outcome with any intervention of interest, which crea
Externí odkaz:
http://arxiv.org/abs/2109.03225
Observational studies are often used to understand relationships between exposures and outcomes. They do not, however, allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of con
Externí odkaz:
http://arxiv.org/abs/2107.09009
Autor:
Huang, Melody Y., Vegetabile, Brian G., Burgette, Lane F., Setodji, Claude, Griffin, Beth Ann
We expand upon the simulation study of Setodji et al. (2017) which compared three promising balancing methods when assessing the average treatment effect on the treated for binary treatments: generalized boosted models (GBM), covariate-balancing prop
Externí odkaz:
http://arxiv.org/abs/2107.03922
Autor:
Griffin, Beth Ann, Schuler, Megan S., Pane, Joseph, Patrick, Stephen W., Smart, Rosanna, Stein, Bradley D., Grimm, Geoffrey, Stuart, Elizabeth A.
Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many p
Externí odkaz:
http://arxiv.org/abs/2106.04304
The world is becoming increasingly complex, both in terms of the rich sources of data we have access to as well as in terms of the statistical and computational methods we can use on those data. These factors create an ever-increasing risk for errors
Externí odkaz:
http://arxiv.org/abs/2101.11857