Journal Articles

Journal Article
We Have to Be Discrete About This: A Non-Parametric Imputation Technique for Missing Categorical Data
Gill, Jeff, and Skyler J. Cranmer. “We Have to Be Discrete About This: A Non-Parametric Imputation Technique for Missing Categorical Data.” British Journal of Political Science 43, no. 2 (2013): 425-449. Publisher's Version Abstract

Missing values are a frequent problem in empirical political science research. Surprisingly, the match between the measurement of the missing values and the correcting algorithms applied is seldom studied. While multiple imputation is a vast improvement over the deletion of cases with missing values, it is often unsuitable for imputing highly non-granular discrete data. We develop a simple technique for imputing missing values in such situations, which is a variant of hot deck imputation, drawing from the conditional distribution of the variable with missing values to preserve the discrete measure of the variable. This method is tested against existing techniques using Monte Carlo analysis and then applied to real data on democratization and modernization theory. Software for our imputation technique is provided in a free, easy-to-use package for the R statistical environment. 

reprint.bjps_.pdf
Effect of Implementation of a Paediatric Neurocritical Care Programme On Outcomes After Severe Traumatic Brain Injury: A Cohort Study
Gill, Jeff, Jose A. Pineda, Jeffrey R. Leonard, Ioanna G. Mazotas, Michael Noetzel, David D. Limbrick, Martin S. Keller, and Allan Doctor. “Effect of Implementation of a Paediatric Neurocritical Care Programme On Outcomes After Severe Traumatic Brain Injury: A Cohort Study.” Lancet-Neurology 12, no. 1 (2013): 45-52. Publisher's Version Abstract

 

Background

Outcomes after traumatic brain injury are worsened by secondary insults; modern intensive-care units address such challenges through use of best-practice pathways. Organisation of intensive-care units has an important role in pathway effectiveness. We aimed to assess the effect of a paediatric neurocritical care programme (PNCP) on outcomes for children with severe traumatic brain injury.

Methods

We undertook a retrospective cohort study of 123 paediatric patients with severe traumatic brain injury (Glasgow coma scale scores ≤8, without gunshot or abusive head trauma, cardiac arrest, or Glasgow coma scale scores of 3 with fixed and dilated pupils) admitted to the paediatric intensive-care unit of the St Louis Children's Hospital (St Louis, MO, USA) between July 15, 1999, and Jan 15, 2012. The primary outcome was rate of categorised hospital discharge disposition before and after implementation of a PNCP on Sept 17, 2005. We developed an ordered probit statistical model to assess adjusted outcome as a function of initial injury severity. We assessed care-team behaviour by comparing timing of invasive neuromonitoring and scored intensity of therapies targeting intracranial hypertension.

Findings

Characteristics of treated patients (aged 3–219 months) were much the same between treatment periods. Before PNCP implementation, 33 (52%) of 63 patients had unfavourable disposition at hospital discharge (death or admission to an inpatient facility) and 30 (48%) had a favourable disposition (home with or without treatment); after PNCP implementation, 20 (33%) of 60 patients had unfavourable disposition and 40 (67%) had favourable disposition (p=0·01). Seven (11%) patients died before PNCP implementation compared with two (3%) deaths after implementation. The probit model indicated that outcome improved across the spectrum of Glasgow coma scale scores after resuscitation (p=0·02); this improvement progressed with increasing injury severity. Kaplan-Meier analysis suggested that neuromonitoring was started earlier and maintained longer after implementation of the PNCP (p=0·03). Therapeutic intensity scores were increased for the first 3 days of treatment after PNCP implementation (p=0·0298 for day 1, p=0·0292 for day 2, and p=0·0471 for day 3). The probit model suggested that increasing age (p=0·03), paediatric risk of mortality III scores (p=0·0003), and injury severity scores (p=0·02) were reliably associated with increased probability of unfavourable outcomes whereas white race (p=0·01), use of intracranial pressure monitoring (p=0·001), and increasing Glasgow coma scale scores after resuscitation (p=0·04) were associated with increased probability of favourable outcomes.

Interpretation

Outcomes for children with traumatic brain injury can be improved by altering the care system in a way that stably implements a cooperative programme of accepted best practice.

Funding

St Louis Children's Hospital and the Sean Glanvill Foundations.

 

 

Recognized as the most important contribution to the field of pediatric neurocritical care at the Outlook to the Future Plenary Session of the World Congress on Pediatric Intensive Critical Care (Seattle 2014).

 

Dynamic Elicited Priors for Updating Covert Networks
Gill, Jeff, and John Freeman. “Dynamic Elicited Priors for Updating Covert Networks.” Network Science 1, no. 1 (2013). Publisher's Version
Bayesian Analytical Methods: A Methodological Prescription for Public Administration
Gill, Jeff, and Chris Witko. “Bayesian Analytical Methods: A Methodological Prescription for Public Administration.” Journal of Public Administration Research and Theory (2013). Publisher's Version
j.1467-9868.2005.00521.x.pdf
Sampling Schemes for Generalized Linear Dirichlet Process Random Effects Models
Gill, Jeff, Minjung Kyung, and George Casella. “Sampling Schemes for Generalized Linear Dirichlet Process Random Effects Models.” Statistical Methods & Applications (2011). Publisher's Version

 

Reprint

New Findings from Terrorism Data: Dirichlet Process Random Effects Models for Latent Groups
Gill, Jeff, Minjung Kyung, and George Casella. “New Findings from Terrorism Data: Dirichlet Process Random Effects Models for Latent Groups.” Journal of the Royal Statistical Society, Series C (2011). Publisher's Version
Estimation in Dirichlet Random Effects Models
Gill, Jeff, Minjung Kyung, and George Casella. “Estimation in Dirichlet Random Effects Models.” Annals of Statistics (2010). Publisher's Version
Penalized Regression, Standard Errors, and Bayesian Lassos
Kyung, Minjung, Jeff Gill, Malay Ghosh, and George Casella. “Penalized Regression, Standard Errors, and Bayesian Lassos.” Bayesian Analysis (2010).
blasso-forba.pdf
Circular Data in Political Science and How to Handle It
Gill, Jeff, and Dominik Hangartner. “Circular Data in Political Science and How to Handle It.” Political Analysis 18, no. 3 (2010). Publisher's Version
Nonparametric Priors For Ordinal Bayesian Social Science Models: Specification and Estimation
Gill, Jeff, and George Casella. “Nonparametric Priors For Ordinal Bayesian Social Science Models: Specification and Estimation.” Journal of the American Statistical Association (2009). Publisher's Version
jasaa07046.pdf
Characterizing the Variance Improvement in Linear Dirichlet Random Effect Models
Gill, Jeff, Minjung Kyung, and George Casella. “Characterizing the Variance Improvement in Linear Dirichlet Random Effect Models.” Statistics & Probability Letters (2009). Publisher's Version
Gill, Jeff. “Is Partial-Dimension Convergence a Problem for Inferences From MCMC Algorithms?Political Analysis 16, no. 2 (2008): 153-178. Abstract
Increasingly, political science researchers are turning to Markov chain Monte Carlo methods to solve inferential
problems with complex models and problematic data.  This is an enormously powerful set of tools based on replacing
difficult or impossible analytical work with simulated empirical draws from the distributions of interest.
While practitioners are generally aware of the importance of convergence of the Markov chain, many are not fully aware
of the difficulties in fully assessing convergence across multiple dimensions.  In most applied circumstances
\emph{every} parameter dimension must be converged for the others to converge.  The usual culprit is slow mixing of
the Markov chain and therefore slow convergence towards the target distribution.  This work demonstrates the partial
convergence problem for the two dominant algorithms and illustrates these issues with empirical examples.
mpm019v1.pdf
Exploration of associations between governance and economics and country level foot-and-mouth disease status by using Bayesian model averaging
Garabed, R. B., Jeff Gill, W. O. Johnson, A. M. Perez, and M. C. Thurmond. “Exploration of associations between governance and economics and country level foot-and-mouth disease status by using Bayesian model averaging.” Journal of the Royal Statistical Society, Series A (2008). Publisher's Version
j2e1467-985x2e20082e005342ex.pdf
Barker, Christopher, William K. Reisen, Bruce F. Eldridge, Wesley O. Johnson, and Jeff Gill. “Mosquitoes In Space and Time: Meteorologic and Edaphic Factors Affecting Culex Tarsalis Abundance in California.American Journal of Tropical Medicine and Hygiene 77, no. 5 (2007): 168.
Accuracy: Tools for Accurate and Reliable Statistical Computing
Altman, Micah, Jeff Gill, and Michael McDonald. “Accuracy: Tools for Accurate and Reliable Statistical Computing.” Journal of Statistical Software 21, no. 1 (2007).
The Etiology of Public Support for the Designated Hitter Rule
Zorn, Chris, and Jeff Gill. “The Etiology of Public Support for the Designated Hitter Rule” 2, no. 2 (2007).
Does Turnout Decline Matter? Electoral Turnout and Partisan Choice in the 1997 Canadian Federal Election
Gill, Jeff, and Michael Martinez. “Does Turnout Decline Matter? Electoral Turnout and Partisan Choice in the 1997 Canadian Federal Election.” Canadian Journal of Political Science, no. 39 (2006): 343-362.
Bayesian Inference in Public Administration Research: Substantive Differences from Somewhat Different Assumptions
Gill, Jeff, and Kevin Wagner. “Bayesian Inference in Public Administration Research: Substantive Differences from Somewhat Different Assumptions.” International Journal of Public Administration 28, no. 1 (2005): 5-35. Publisher's Version
An Entropy Measure of Uncertainty in Vote Choice
Gill, Jeff. “An Entropy Measure of Uncertainty in Vote Choice.” Electoral Studies 24, no. 3 (2005): 371-392. Publisher's Version
HTML, PDF, R code for running the model: entropy.data2.s, entropy.prior2.s, entropy.model2.s
Elicited Priors for Bayesian Model Specifications in Political Science Research
Gill, Jeff, and Lee Walker. “Elicited Priors for Bayesian Model Specifications in Political Science Research.” Journal of Politics 67, no. 3 (2005): 841-872. Publisher's Version
jop_342.pdf

Pages