Notes on Automating Stem and Leaf Displays

The stem-and-leaf display is a natural semi-graphic technique to include in statistical computing systems. This paper discusses the choices involved in implementing both automated and flexible versions of the display, develops an algorithm for the automated version, examines various implementation considerations, and presents a set of semi-portable FORTRAN subroutines for producing stem-and-leaf displays.

Random Directed Graph Distributions in the Triad Census in Social Networks

This paper uses the concept of the triad census first introduced by Holland and Leinhardt, and describes several distributions on directed graphs. Methods are presented for calculating the mean and the covariance matrix of the triad census for the uniform distribution that conditions on the number of choices made by each individual in the social network. Several complex distributions on digraphs are approximated, and an application of these methods to a sociogram is given.

Sensitivity of Estimates of Markov Transition Matrices to Perturbations and Sampling Error

1 online resource (PDF, 32 pages

Analyzing Social Networks as Stochastic Processes

1 online resource (PDF, 51 pages

Distinguishing Between Stochastic Models of Heterogeneity and Contagion

1 online resource (PDF, 35 pages

A Study of Change in a Regional Corporate Network

1 online resource (PDF, 22 pages

Longitudinal Analysis of Friendship Networks

1 online resource (PDF, 21 pages

Methods for the Analysis of Data From Multivariate Directed Graphs

1 online resource (PDF, 32 pages

Analyzing Data from Multivariate Directed Graphs: An Application to Social Networks

1 online resource (PDF, 29 pages

Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: a Retrospective Cohort Study

Background: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. Methods: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis. The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. Results: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). Conclusions: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged