Washington Statistical Society

Washington Statistical Society Seminars

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May, 2008
2	Fri.	Statistical issues in disease surveillance: A case study from ESSENCE
2	Fri.	George Mason University CDS/CCDS/Statistics Colloquium Series Some Issues Raised by High Dimensions in Statistics
5	Mon.	Statistical Issues Arising in the Interpretation of a Measure of Relative Disparity Used in Educational Funding: The Zuni School District 89 Case
8	Thur.	U. S. Census Bureau 9th Elders Program Seminar Different Directorates, Not So Different Approach
13	Tues.	Multivariate Event Detection and Characterization
15	Thur.	President's Invited Seminar What's Up at the ASA?
16	Fri.	Bayesian Dose-finding Trial Designs for Drug Combinations
June, 2008
10	Tues.	Nonresponse Adjustments in Survey Applications
17	Tues.	Recent Developments in Address-based Sampling
26	Thur.	Multiple Frame Surveys: Lessons from CBECS Experience

Notice about Seminars at the Bureau of Labor Statistics

To attend seminars at BLS, you should be on the BLS Visitors List. To be sure you are on it, please let Stella Godbolt know you plan to attend this seminar:

Stella P. Godbolt
Bureau of Labor Statistics
Office of Survey Methods Research
202-691-6782
godbolt.stella@bls.gov

BLS is located at 2 Massachusetts Avenue, NE. Washington, DC. The Visitor's Entrance is on 1st Street and is opposite Union Station. Remember to bring a photo ID.

Title: Statistical issues in disease surveillance: A case study from ESSENCE

Syndromic surveillance systems attempt to monitor the burden of disease in communities in real time, using health-related data and tools from statistics, epidemiology, informatics, and other disciplines. A potential benefit of such surveillance is early detection and tracking of infectious disease outbreaks.

The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) is a syndromic surveillance system that monitors outpatient visits to military medical treatment facilities. This study examines whether ESSENCE can detect more infectious disease outbreaks, and detect them earlier, using joint monitoring of laboratory test orders and outpatient visit data rather than outpatient visit data alone. Statistical issues that arise from this question include which aberration detection algorithm is best suited to these data sources, how to quantify the tradeoffs among sensitivity, specificity and timeliness for detecting outbreaks, and how to monitor information from multiple data sources simultaneously.

For information, please contact Caroline Wu at 202-687-4114 or ctw26@georgetown.edu

Title: Statistical Issues Arising in the Interpretation of a Measure of Relative Disparity Used in Educational Funding: The Zuni School District 89 Case

This seminar will discuss statistical issues that arose in recent cases. The first case concerns the interpretation of a formula Congress wrote when it revised a law that provides funds for educating children in areas with a large federal presence (e.g. major research lab). Because federal land is not subject to local real estate tax, the primary source of funding education, the law is intended to assist the relevant school districts. We will discuss the statute and the various interpretations that arose during the proceedings and the justifications provided. A counter-example to one of the assertions made by the lawyers at the Supreme Court hearing, which appears to have been accepted by the Court's majority, will also be presented.

GEORGE MASON UNIVERSITY
CDS/CCDS/STATISTICS COLLOQUIUM SERIES

Title: Some Issues Raised by High Dimensions in Statistics

This talk is an overview presentation made by D.M. Titterington as a summary of the activities at Cambridge during the Spring of 2008. Most of twentieth-century statistical theory was restricted to problems in which the number p of 'unknowns', such as parameters, is much less than n, the number of experimental units. However, the practical environment has changed dramatically over the last twenty years or so, with the spectacular evolution of computing facilities and the emergence of applications in which the number of experimental units is comparatively small but the underlying dimension is massive, leading to the desire to fit complex models for which the effective p is very large. Areas of application include image analysis, microarray analysis, finance, document classification, astronomy and atmospheric science. Some methodological advances have been made, but there is a need to provide firm consolidation in the form of a systematic and critical assessment of the new approaches as well as appropriate theoretical underpinning in this 'large p, small n' context. The existence of key applications strongly motivates the programme, but the fundamental aim is to promote core theoretical and methodological research. Both frequentist and Bayesian paradigms will be featured. The programme is directed at a broad research community, including both mainstream statisticians and the growing population of researchers in machine learning.

U. S. CENSUS BUREAU
9TH ELDERS PROGRAM SEMINAR

Topic: Different Directorates, Not So Different Approach

I hope to provide insight into the earlydevelopment of Jeffersonville; how it originated, expanded, and how it interfaced with the Bureau's subject matter divisions in the 1970's and 1980"s. Also, I will address the changes in the Bureau's collection and publication of foreign trade statistics in the late 1980's and early 1990's. Biography : Don joined the Bureau in 1963 as an Industry Division analyst, moved to Demographic Surveys Division and in 1969 relocated to Jeffersonville in charge of processing the 1969 Census of Agriculture. This "temporary" assignment lasted for 16 years. He became Chief, Data Preparation Division (now NPC) in 1976 until late 1985 when he returned to Suitland as Chief, Data User Services Division. In less than a year, he became Chief, Foreign Trade Division, a position he held until the end of 1993. For much of the year 1993, one of reorganization in the Economic Directorate, Don was the Assistant Director for Economic Programs; Acting Chief, Foreign Trade Division; Acting Chief, Construction Division; and Acting Chief, Industry Division--all at the same time. A recipient of the Department's Silver and Gold Medals, Don retired as Assistant Director of Economic Programs on December 31, 1993.

This seminaris physically accessible to persons with disabilities. Please direct all requests for Sign Language Interpreting Services, Computer Aided Real-time (CART), or other accommodation needs, to HRD.Disability.Program@census.gov. If you have any questions concerning accommodations, please contact the Disability Program Office at 301-763-4060 (Voice), 301-763-0376 (TTY).

Title: Multivariate Event Detection and Characterization

We present the multivariate Bayesian scan statistic (MBSS), a general framework for event detection and characterization in multivariate spatial time series data. MBSS integrates prior information and observations from multiple data streams in a principled Bayesian framework, computing the posterior probability of each type of event in each space-time region. MBSS learns a multivariate Gamma-Poisson model from historical data, and models the effects of each event type on each stream using expert knowledge or labeled training examples. We evaluated MBSS on various disease surveillance tasks, detecting and characterizing disease outbreaks injected into three streams of Pennsylvania medication sales data. We demonstrated that MBSS can be used both as a "general" event detector, with high detection power across a variety of event types, and a "specific" detector that incorporates prior knowledge of an event's effects to achieve much higher detection power. MBSS has many other advantages over previous event detection approaches, including efficient computation and easy interpretation and visualization of results, and allows faster and more accurate detection by integrating information from the multiple streams. Most importantly, MBSS can model and differentiate between multiple event types, thus distinguishing between events requiring urgent responses and other, less relevant patterns in the data. This talk will present an overview of the MBSS framework, and compare MBSS to other recently proposed multivariate detection approaches. Time permitting, I will also discuss how incremental learning (both passive and active) can be incorporated into the MBSS framework and used to improve detection performance, and consider extensions of MBSS to more general pattern detection problems.

PRESIDENT'S INVITED SEMINAR

Title: What's Up at the ASA?

ASA Executive Director Ron Wasserstein will provide a brief update on activities and directions of the association. However, most of the session will be devoted to questions and comments from the participants. Among the many things we could discuss:

Title: Bayesian Dose-finding Trial Designs for Drug Combinations

Treating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in the single-agent trials, which naturally offers rich prior information. We propose Bayesian adaptive designs to search for the maximum tolerated dose combination. We continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying the same is determined by comparing the posterior estimates of the toxicity probabilities of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios.

For information, please contact Caroline Wu at 202-687-4114 or ctw26@georgetown.edu

Title: Nonresponse Adjustments in Survey Applications

Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Nonresponse: Examples from Multiple Surveys Nonresponse weighting is a commonly used method to adjust for bias due to unit nonresponse in surveys. Theory and simulations show that, in order to effectively reduce bias without increasing variance, a covariate used for nonresponse weighting adjustment needs to be highly associated with both response and the survey outcome. In practice, these requirements pose a challenge that is often overlooked. Recently some surveys have begun collecting supplementary data, such as interviewer observations and other proxy measures of key survey outcomes. These variables are promising candidates for nonresponse adjustment because they should be highly correlated with the actual outcomes. In the present study, we examine the extent to which traditional covariates and new proxy measures satisfy the weighting requirements for the National Survey of Family Growth, the Medical Expenditure Survey, the U.S. National Election Survey, the European Social Surveys and the University of Michigan Transportation Research Institute Survey. We provide empirical estimates of the association between proxy measures and the likelihood of response as well as the actual survey responses. We also compare unweighted and weighted estimates under various nonresponse models. Results show the difficulty of finding suitable covariates and the need to improve the quality of proxy measures. s to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios.

Assessment of the Impact of Health Variables on Nonresponse Adjustment in the Medical Expenditure Panel Survey The Medical Expenditure Panel Survey(MEPS) is a large complex sample survey, designed to provide nationally representative annual estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. A new panel of households is selected each year for the MEPS from households that responded to the previous year's National Health Interview Survey(NHIS). Nonresponse is a common problem in household sample surveys. To compensate for nonresponse and to reduce the potential bias of the survey estimates, two separate nonresponse adjustments are performed in development of analytic weights in MEPS. The first, the focus of this presentation, is an adjustment for dwelling unit (DU) level nonresponse to account for nonresponse among those households subsampled from NHIS for the MEPS. The adjustment is carried out using socio-economic, demographic, and health variables that are available for both respondents and nonrespondents. In this study, we examine the impact of health variables on the MEPS DU level nonresponse weight adjustment. Response propensity scores are calculated based on logistic regression models and quintiles of the propensity scores are used to adjust the MEPS base weights. Comparisons of the nonresponse adjusted weights and selected survey variables with and without inclusion of health variables as a nonresponse adjustment covariate are discussed.

Title: Recent Developments in Address-based Sampling

Increasingly, survey researchers are reverting back to address-based methodologies to reach the general public for survey administration and related commercial applications. Essentially, there are three main factors for this change: evolving coverage problems associated with telephone-based methods; eroding rates of response to telephone contacts; and on the other hand, recent improvements in the databases of household addresses available to researchers. This presentation provides an assessment of these three factors along with an over view of the structure of the Delivery Sequence File (DSF) of the USPS that is often used for construction of address-based sampling frames. Moreover, key enhancements available for the DSF will be discussed. While reducing undercoverage bias particularly in rural areas where more households rely on P.O. Boxes and inconsistent address formats such enhancements enable researcher to develop more efficient sample designs as well as broaden their analytical possibilities through an expanded set of covariates for hypothesis testing and statistical modeling tasks.

Title: Multiple Frame Surveys: Lessons from CBECS Experience

Because there is no single frame of buildings in the country, the Commercial Building Energy Consumption Survey relies on federal databases, commercially- available databases and field listing to construct a sampling frame. The 2007 round of CBECS, which NORC is currently fielding on behalf of the Energy Information Administration of the DOE, sampled from seven frames. The difficulty in sampling simultaneously from many frames is in adjusting for the overlap among them. To calculate the correct weights, we must know the true probability of selection for all selected cases: for each selected building we must either know its probability of selection from each of the frames to which it belongs or we must remove it from all of the frames but one. We will present the steps we took to remove these overlaps before the sample was fielded. Despite our best efforts, duplicates were discovered in the field: we will discuss the ways in which we modified the probabilities of selection once data collection was underway. We must also confront the fact that undetected duplicates remain, and that some of the probabilities of selection will not be correct. The CBECS experience can help other surveys to decide whether incorporating additional frames is worth the difficulty. Our findings also apply to dual-frame phone surveys, where it may not be possible to fully deduplicate the frames.