Uchino, K., Billheimer, D., & Cramer, S. C. (2001). Entry criteria and baseline characteristics predict outcome in acute stroke trials. Stroke, 32(4), 909-916.
PMID: 11283391;Abstract:
Background and Purpose - We sought to study the range of entry criteria and baseline characteristics in acute stroke trials and to understand their effects on patient outcomes. Methods - Randomized, placebo-controlled therapeutic trials in patients with acute ischemic stroke were identified. Entry criteria, baseline clinical characteristics, and outcome were extracted for the placebo group of each trial. The relationship between key variables was then determined. Results - Across 90 placebo groups identified, there was great variation in entry criteria and outcome measures. This was associated with divergent outcomes; for example, in some studies most placebo group patients died, while in other studies nearly all had no disability. Entry criteria were significantly correlated with outcome; for example, higher age cutoff for study entry correlated with 3-month mortality. Entry criteria also predicted baseline clinical characteristics; for example, wider time window for study entry correlated directly with time to treatment and inversely with stroke severity (initial National Institutes of Health Stroke Scale score). Baseline characteristics predicted outcome. Greater stroke severity predicted higher 3-month mortality rate; despite this, successful thrombolytic trials have enrolled more severe strokes than most trials. The mean age of enrollees also predicted 3-month mortality and was inversely related to percentage of patients with 3-month Barthel Index score ≥95. The strongest predictors of 3-month mortality were obtained with multivariate models. Conclusions - Acute stroke studies vary widely in entry criteria and outcome measures. Across multiple studies, differences in entry criteria, and the baseline clinical characteristics they predict, influence patient outcomes along a continuum. In some studies, enrolling a specific subset of patients may have improved the chances of identifying a treatment-related effect, while in others, such chances may have been reduced. These findings may be useful in the design of future stroke therapeutic trials.
Billheimer, D., Guttorp, P., & Fagan, W. F. (2001). Statistical Interpretation of Species Composition. Journal of the American Statistical Association, 96(456), 1205-1213.
Abstract:
The relative abundance of different species characterizes the structure of a biological community. We analyze an experiment addressing the relationship between omnivorous feeding linkages and community stability. Our goal is to determine whether communities with different predator compositions respond similarly to environmental disturbance. To evaluate these data, we develop a hierarchical statistical model that combines Aitchison's logistic normal distribution with a conditional multinomial observation distribution. In addition, we present an algebra for compositions that includes addition, scalar multiplication, and a metric for differences in compositions. The algebra aids interpretation of treatment effects, treatment interactions, and covariates. Markov chain Monte Carlo (MCMC) is used for inference in a Bayesian framework. Our experimental results indicate that a high degree of omnivory can help to stabilize community dynamics and prevent radical shifts in community composition. This result is at odds with classical food-web predictions, but agrees with recent theoretical formulations.
Ware, L., Koyama, T., Billheimer, D., Landeck, M., Johnson, E., Brady, S., Bernard, G., & Matthay, M. (2011). Advancing donor management research: design and implementation of a large, randomized, placebo-controlled trial. Ann Intensive Care, 1(1), 20.
Ming, L. i., Gray, W., Zhang, H., Chung, C. H., Billheimer, D., Yarbrough, W. G., Liebler, D. C., Shyr, Y., & J., R. (2010). Comparative shotgun proteomics using spectral count data and quasi-likelihood modeling. Journal of Proteome Research, 9(8), 4295-4305.
PMID: 20586475;PMCID: PMC2920032;Abstract:
Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher's Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples. © 2010 American Chemical Society.
Berry, C. E., Billheimer, D., Jenkins, I. C., Lu, Z. J., Stern, D. A., Gerald, L. B., Carr, T. F., Guerra, S., Morgan, W. J., Wright, A. L., & Martinez, F. D. (2016). A Distinct Low Lung Function Trajectory from Childhood to the Fourth Decade of Life. American journal of respiratory and critical care medicine, 194(5), 607-12.
BIO5 Collaborators
Dean Billheimer, Stefano Guerra, Fernando Martinez
Low maximally attained lung function increases the risk of chronic obstructive pulmonary disease irrespective of the subsequent rate of lung function decline.
