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Health Policy Statistics Section

Session Slot: 8:30-10:20 Monday

Estimated Audience Size: 75-125

AudioVisual Request: None

Session Title: Multiple Measurements/Multiple Levels in Health Care Research

Theme Session: Yes

Applied Session: Yes

Session Organizer: Christiansen, Cindy L. Harvard Medical School and Harvard Pilgrim Health Care

Address: Harvard Medical School and Harvard Pilgrim Health Care, 126 Brookline Ave., Suite 200, Boston, MA 02215

Phone: (617) 421-2430

Fax: (617) 859-8112


Session Timing: 110 minutes total (Sorry about format):

3 speakers and 1 discussant Opening Remarks by Chair - 3 minutes First Speaker - 30 minutes Second Speaker - 30 minutes Third Speaker - 30 minutes Discussant - 10 minutes Floor Discusion - 7 minutes

Session Chair: McCaffrey, Dan RAND

Address: RAND, 1700 Main St., PO Box 2138, Santa Monica, CA 90407-2138

Phone: (310) 393-0411 x6735

Fax: (310) 451-7004


1. Methods for the Analysis of Signal Data in Medicine

Land, Stephanie,   Carnegie Mellon University

Address: Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213

Phone: (412) 268-1880

Fax: (412) 268-7828


Abstract: Medical researchers often need to relate signal data (e.g. analog time series or frequently measured time series) to covariates such as the health or treatment status of patients. This talk will focus on regression estimation and inference for such problems.

2. Analysis of hospital quality monitors using hierarchical time series models

Aguilar, Omar,   ISDS Duke University

Address: Duke Univeristy Box 90251, Durham NC, 27708

Phone: 919-684-8840

Fax: 919-684-8594


West, Mike, ISDS Duke University

Abstract: The VA management services department invests considerably in the collection and assessment of data to inform on hospital and care-area specific levels of quality of care. Resulting time series of ``quality monitors'' provide information relevant to evaluating patterns of variability in hospital-specific quality of care over time and across care areas, and to compare and assess differences across hospitals. In collaboration with the VA management services group we have developed various models for evaluating such patterns of dependencies and combining data across the VA hospital system. This paper provides a brief overview of resulting models, some summary examples on three monitor time series, and discussion of data, modelling and inference issues. This work introduces new models for multivariate non-Gaussian time series. The framework combines cross-sectional, hierarchical models of the population of hospitals with time series structure to allow and measure time-variations in the associated hierarchical model parameters. In the VA study, the within-year components of the model describe patterns of heterogeneity across the population of hospitals and relationships among several such monitors, while the time series components describe patterns of variability through time in hospital-specific effects and their relationships across quality monitors. Additional model components isolate unpredictable aspects of variability in quality monitor outcomes, by hospital and care areas. We discuss model assessment, residual analysis and MCMC algorithms developed to fit these models, which will be of interest in related applications in other socio-economic areas.

3. Analyzing patients' satisfaction with care using multi-level random-effects models

Hui, Siu L.,   Indiana University School of Medicine

Address: Division of Biostatistics Indiana University School of Medicine Riley Research Wing, 135 699 West Dr. Indianapolis, IN 46202-5119

Phone: 317-274-2661

Fax: 317-274-2686


Zhou, Xiao-Hua (Andrew), Indiana University School of Medicine

Perkins, Tony, Regenstrief Institute for Health Care

Tierney, Williams M., Regenstrief Institute for Health Care

Abstract: Since patient satisfaction has become more and more an important indicator of health care quality, in this report, we studied patients' satisfaction among elderly sick patients in an academic outpatient clinic. As part of a study of advance directives in an academic outpatient clinic, we interviewed a cohort of more than a thousand elderly patients, many with significant chronic illness, after scheduled appointments with their primary care physicians. Each patient was followed for one year, and after each scheduled visit, the patient was interviewed by a research assistant who administered two patient satisfaction questionnaires: (1) the ten-item instrument created by the American Board of Internal Medicine (ABIM), which evaluates satisfaction with doctor-patient communication and (2) the seven-item questionnaire from the Medical Outcomes Study (MOS), which assesses satisfaction with the visits just completed. We were interested in identifying clinical factors that might affect patients' satisfaction with care. Due to the hierarchical nature of the data and multidimensional nature of patients' satisfaction with care, we considered covariates at three levels, visit, patient, and doctor. We proposed the use of multi-level random-effects regression models to study effects of three-level covariates on patients' satisfaction scores.

Discussant: Katz, Barry   Indiana University School of Medicine

Address: Division of Biostatistics Indiana University School of Medicine Riley Research WING, 135 699 West Dr. Indianapolis, IN 46202-5119

Phone: 317-274-2674

Fax: 317-274-2678


List of speakers who are nonmembers: None

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David Scott