Held on March 10, 2005 at the Wyndham Glenview Suites.
The Program consisted of three presentations:
- Confidence Regions for Random-Effects: Calibration Curves with Heteroscedastic Errors.
Dulal Bhaumik, Ph.D., Center for Health Statistics, University of Illinois at Chicago
- Data Safety and Monitoring Boards: Some Personal Perspectives.
Robert (Skip) Woolson, Ph.D., Department of Biostatistics, Bioinformatics & Epidemiology, Medical University of South Carolina
- VA Cooperative Study #364: Long term patency of saphenous vein and left internal mammary artery grafts after coronary artery bypass surgery.
Thomas Moritz, M.S., Hines Veterans Affairs Hospital
Confidence Regions for Random-Effects: Calibration Curves with Heteroscedastic Errors by Dulal Bhaumik, Ph.D., Center for Health Statistics, University of Illinois at Chicago
Dr. Dulal Bhaumik is presently an Associate Professor of Biostatistics and Psychiatry at the University of Illinois at Chicago (UIC). He joined UIC in September 2002 after 12 years of teaching and research at the University of South Alabama Department of Mathematics and Statistics. He obtained his Master’s degree in Statistics from the Indian Statistical Institute in 1983 and his Ph.D. in Statistics from the University of Maryland (UMBC) in 1988. In 2002, he won the American Statistical Association’s Youden Prize for Contributions to Interlaboratory Calibration. Dr. Bhaumik has over 25 peer-reviewed publications including articles in the Journal of the Royal Statistical Society, Journal of the American Statistical Association, and Technometrics. He is also Co-Principal Investigator for several current NIH/NIMH funded research projects.
Confidence bounds are constructed for a random-effects calibration curve model. An example application is analysis of analytical chemistry data in which the calibration curve contains measurements y for several values of known concentration x in each of q laboratories. Laboratory is considered a random-effect in this design, and the intercept and slope of the calibration curve are allowed to have laboratory-specific values. This presentation focuses on: (i) develop an appropriate inter-laboratory calibration curve for heteroscedastic data of the type commonly observed in analytical chemistry, (ii) compute a point estimate for an unknown true concentration x when corresponding measured concentrations y1, y2, … yq' are provided from q' laboratories (i.e., a subset of the original q laboratories used to calibrate the model, where 1 < q' < q), (iii) compute the asymptotic mean and variance of the estimate, (iv) construct a confidence region for x. The methods are then illustrated using both simulated and typical inter-laboratory calibration data.Other relevant applications of the general approach will be highlighted.
Data Safety and Monitoring Boards: Some Personal Perspectives by Robert (Skip) Woolson, Ph.D., Department of Biostatistics, Bioinformatics & Epidemiology, Medical University of South Carolina
Biographical Background Dr. Robert F. Woolson is presently Professor of Biostatistics, Bioinformatics and Epidemiology at the Medical University of South Carolina (MUSC) in Charleston, and Professor Emeritus of Biostatistics and Statistics at University of Iowa (UI). He joined MUSC in August of 2002 after concluding 29 years on the medical school/public health school faculty at UI. At UI he was Professor and Head of Biostatistics and Associate Dean for Research for the UI College of Public Health. Dr. Woolson conducts both collaborative clinical research and biostatistical methodologic research. At UI he founded and directed the Clinical Trials Statistical Data Management Center, which continues to serve as a resource for the design, conduct, coordination, and statistical analysis of multi-center clinical trials.He was principal investigator for several large NINDS grants to coordinate multi-center trials as well as statistical methodology grants—TOAST, IHAST, COSS (planning grant).
Dr. Woolson served on the VA Cooperative Studies Evaluation Committee for six years and served a term as a member of the NIAID Data Safety & Monitoring Board for AIDS Therapeutic Trials Program. At the present time he is a member of several DSMB’s including: the NIAID Hematopoietic Stem Cell Transplant program; and for VA Cooperative Study # 526, a trial evaluating thyroid hormone for heart failure. He has also served on DSMB’s for a number of corporate trials, mostly trials involving potential stroke therapies. His methodological research interests include longitudinal data analysis, survival analysis, and clinical trial / epidemiological methods. His statistical research grants have been supported by the National Institute of Mental Health and the National Cancer Institute. Dr. Woolson has mentored numerous K30, K08 and other clinical research trainees, as well as graduate students; and faculty. He is an ASA Fellow, is presently on the editorial board for Statistics In Medicine, and previously served as an associate editor for Controlled Clinical Trials and for Statistical Methods for Medical Research.
Abstract Safety and Monitoring Boards (DSMB’s) are often appointed for the purpose of reviewing interim data during the course of a randomized clinical trial. Such boards are charged with reviewing accumulating evidence to see if there is sufficient evidence to conclude a trial on the basis of benefit, lack of benefit, or if there is undue harm to study participants. Such DSMB’s have held a prominent place in the conduct of federally sponsored trials, for example NIH, VA and related funding agencies. DSMB’s are also common in corporate sponsored trials, particularly those sponsored by pharmaceutical corporations. Indeed, common practice today within any academic health center is for a data safety and monitoring plan to be in place for a clinical study, and for a clinical trial this data safety and monitoring plan would be to have a DSMB with formal guidelines for consideration of early trial termination.
Biostatisticians have the opportunity to serve on DSMB’s, or to be one of the key liaisons between a study and an external DSMB. This presentation discusses general issues, challenges and personal experiences in the context of DSMB.
VA Cooperative Study #364: Long term patency of saphenous vein and left internal mammary artery grafts after coronary artery bypass surgery by Thomas Moritz, M.S., Hines Veterans Affairs Hospital
Thomas Moritz is presently a Biostatistician in the Cooperative Studies Program Coordinating Center at Hines Veterans Affairs Hospital. He obtained a B.S. in Mathematics at Marquette University and M.S. degrees in Applied Statistics and Biostatistics at Iowa State University and the Medical College of Wisconsin, respectively. During his 20 year tenure at the Hines Coordinating Center he has managed studies in lung and colon cancer, pulmonary disease, liver disease, cardiac surgery and vascular surgery. He has over 50 publications and has also presented at the Society of Clinical Trials and NIC/ASA meetings.
The VA Cooperative Studies Trial defined long-term (ten-year) saphenous vein graft (SVG) and left internal mammary artery (IMA) patency in patients undergoing coronary artery bypass graft (CABG) surgery. Traditionally, reports of long-term graft patency rates have used coronary angiography results from a single time point after CABG. Patency rates from a single time period distant from the original operation may result in biased estimates if there is no accounting for interceding interventions such as repeat coronary surgery, percutaneous coronary intervention or death. In this study, patients had between 1 and 5 serial angiograms.
The data from this study posed two major analytic problems. The first problem was that the exact time of graft occlusion could not be known. This problem is addressed by using interval-censored observations in the survival analysis (PROC LIFETEST in SAS® ). This analysis requires identification of the time interval in which the occlusion occurred. Comparisons of Kaplan-Meier product-limit survival curves were made with the log-rank test. Although time-related analyses of graft patency data used the exact date of each postoperative angiogram, for convenience of presentation, some information is presented in arbitrarily defined time frames. The second analytic problem was related to the fact that there were multiple grafts, i.e., clustered observations, within a patient. It has previously been demonstrated that graft patency within a patient is not independent. This does not affect the estimates for patency rates, but does cause the standard error terms to be underestimated. Recently, the SAS® macro, IWM, has become available for the analysis of clustered, interval-censored survival data. This approach produces robust estimates of the standard error terms by adjusting for the correlated nature of the clustered observations. Patient-related risk variables, graft-related risk variables and CABG surgery processes of care variables were used as candidate independent variables in the IWM macro to identify the set of variables that jointly predict ten-year graft patency.