Publications

There is some confusion in political science, and the social sciences in general, about the meaning and interpretation of
interaction effects in models with non-interval, non-normal outcome variables.  Often these terms are casually thrown into a model
specification without observing that their presence fundamentally changes the interpretation of the resulting coefficients.  The
work here explains the conditional nature of reported coefficients and their standard errors in models with interactions, defining
the necessarily different interpretation required by generalized linear models, and providing a general analytical method for
correctly calculating coefficient standard errors in models with second-order or higher interactions.  Methodological issues are
illustrated with an application of generalized linear models with interactions applied to voter information structured by
electoral systems and resulting legislative representation in comparative politics.

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. 

 

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).

 

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.