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Brenda F Kurland, PhD

Research Associate Professor, Biostatistics

Contact

Suite 325 Sterling Plaza, 201 N Craig St, Pittsburgh 15213
R-znvy: osx65@cvgg.rqh
Primary Phone: 967-838-6673


Personal Statement

My original research interest, dating back to undergraduate studies in language development, is analysis of longitudinal and other correlated data. Missing data prevent simple pre/post analysis of data collected longitudinally, and are a barrier to more detailed analysis as well. The missing data literature concentrates on implicit or explicit imputation of missing values, so that inference can be made as if the data were not missing. My research considers another possibility: when the “missing” data do not even exist, as for data missing due to death. When analyses accommodate “missing at random” longitudinal data, imputation beyond death can influence study conclusions. For example, since cognitive performance often declines as death approaches, implicit imputation of cognitive performance scores beyond death could lead to lowered estimates of average functioning for survivors. My doctoral dissertation formalized an additional approach: partly conditioning on survival status, using techniques from standard methods such as generalized linear models and weighted estimating equations. The target of inference for these partly conditional models may be referred to as “regression conditioning on being alive.”

More recently, as a cancer center biostatistician, I have worked with investigators to design early phase clinical trials that include placebo controls, pharmacodynamic endpoints, and/or correlative studies to assess measurement of tumor markers. My area of emphasis is clinical research involving functional imaging, specifically PET and breast MRI. Statistical research on incorporating biomarkers into clinical trial study design has emphasized tissue and blood biomarkers. Since functional imaging has different strengths (in vivo functional information and measurement of multiple tumors) and weaknesses (limited access to archived samples), refinements to address statistical treatment of quantitative functional imaging data are a valuable contribution both to the clinical trials literature and to functional imaging.

My independent research in quantitative imaging has encompassed both analysis of correlated data and study design. I have examined within-patient differences in estrogen receptor functioning in breast cancer patients with multiple lesions, to inform design of functional imaging predictive markers to guide use of endocrine therapy. Another current project uses data from PET test-retest studies, calibration studies, and clinical trials to provide parameters for simulation studies to inform the use of quantitative imaging in future clinical trials. For example, the benefits of using simpler imaging protocols at a larger number of sites can be weighed against the potential for greater precision and lower detection thresholds accomplished by sophisticated imaging protocols available at fewer sites.


Education

2002 | University of Washington, Seattle, WA  | PhD, Biostatistics





Teaching

BIOST 2066, Applied Survival Analysis, Fall 2014, 2016


Selected Publications

Kurland BF, Muzi M, Peterson LM, Doot RK, Wangerin KA, Mankoff DA, Linden HM, Kinahan PE.  Multicenter clinical trials using 18F-FDG PET to measure early response to oncologic therapy: Effects of injection-to-acquisition time variability on required sample size. Journal of Nuclear Medicine. 2016 Feb;57(2):226-30.

 
Kurland BF, Mankoff DA.  Structural and molecular imaging in cancer therapy clinical trials.  Handbook of statistics in clinical oncology, third edition, J. Crowley and A. Hoering (Eds).  Chapman and Hall/CRC, pages 387-412, 2012. 

Kurland BF, Peterson LM, Lee JH, Linden HM, Schubert EK, Dunnwald LK, Link JM, Krohn KA, Mankoff DA: Between-patient and within-patient (site-to-site) variability in estrogen receptor binding, measured in vivo by 18F-fluoroestradiol (FES) PET. Journal of Nuclear Medicine 52 (10): 1541-9, 2011. 

Kurland BF, Johnson LL, Egleston BL, Diehr PH: Longitudinal data with follow-up truncated by death: match the analysis method to the research aims. Statistical Science 24 (2): 211-222, 2009. 

Kurland BF, Heagerty PJ:  Marginalized transition models for longitudinal binary data with ignorable and non-ignorable drop-out.  Statistics in Medicine 23:2673-2695, 2004.

http://www.ncbi.nlm.nih.gov/sites/myncbi/brenda.kurland.1/bibliography/47053186/public/?sort=date&direction=ascending


Brenda F Kurland
© 2017 by University of Pittsburgh Graduate School of Public Health

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