After graduating from Harvard Biostatistics in 2007, Evan
accepted a position as an assistant professor in the Department of Statistics
at Brigham Young University. In addition, Evan is an adjunct assistant professor
in the Department of Oncological Sciences at the Huntsman Cancer Institute
at the University of Utah. Evan's research focuses mainly on the development
and application of statistical methods for emerging high-throughput genomic
technologies, such as microarrays and next-generation sequencing. Evan is
the primary mentor for several postdoctoral fellows, graduate students, and
undergraduate students developing statistical methods for solving cancer-related
problems in collaboration with many basic and translational biology labs.
In particular, Evan's research group is working on several cancer-realated projects. One project of focus is the development of a statistical approach for predicting individual risk of breast cancer based on gene expression profiling in peripheral blood cells. Future work in this area will be to incorporate SNPs, copy number variations, epigenomics, and biological pathway profiles into their predictions. They are also collaborating with researchers to develop a diagnostic microarray for Ewing's Sarcomina, and to identify novel SNPs associated with the development of lymphoma. In addition, Evan's group is working on a number of projects that are indirectly associated with cancer, such as estrogen receptor repression, ets-modulated transcription in T cells, and the genome-wide mapping and characterization of chromatin.
Evan enjoys running, basketball, and spending time with his family. Evan and
his wife Holly have 4 children (2 boys and 2 girls). Evan's research group
homepage is located at: http://jlab.byu.edu/
After graduating from the PhD program in 2009, Megan took
a position as an Assistant Member at the Fred Hutchinson Cancer Research Center.
Megan's collaborative research focuses on the design, conduct, and analysis
of cancer clinical trials. She leads the statistical activities related to
Phase II and Phase III clinical trials in Leukemia and Melanoma organized
through the Southwest Oncology Group (SWOG). SWOG is a National Cancer Institute-supported
cancer clinical trial cooperative group.
Megan's methodological research currently focuses on methods for multivariate or clustered survival data. She is investigating survival models that can examine and account for potential clustering within clinical trials, while controlling for treatment and patient prognostic factors. Some specific applications to clinical trials would be to data potentially clustered within treating institution or data clustered within type of treatment institution (e.g., academic hospital versus community-based hospital).
A California Wellness Foundation fellow, Miguel Marino
elected to come to Harvard Biostatistics because of its strong faculty and
connected cancer research group. Initially motivated by the mathematics and
statistics involved with cancer research, Miguel realized that to address
a complex interactive system like cancer, it would take more than mathematical
skills to make any advancement. As a result, Miguel developed scientific skills
outside the scope of statistics through his cognate field that has helped
him to improve as a research scientist and thoughtful statistician. Courses
in the biology, epidemiology and genetics of cancer provided Miguel a comprehensive
understanding of the difficult framework of cancer. The merging of his newly-found
knowledge of the cancer framework and his statistical background inspired
Miguel's interest in identifying significant change patterns in several US
cancer mortality rates. Miguel was recognized for his research in this area
as a Distinguished Student Paper Award winner from the ENAR statistical proceedings
in 2010 and as a finalist for the Student Paper Award from the Statistical
Section of the American Public Health Association in 2010.
In addition, Miguel took full advantage of the resources available in the cancer training grant and began collaborating with members of the population health group at HSPH and clinicians on issues dealing with health risk behaviors and behavioral change. Through these collaborative efforts, Miguel has sharpened his ability to explain statistics in a coherent and straight way. Miguel's ability to fine tune his teaching skills resulted in a distinction in teaching award from the Derek Bok center at Harvard.
Eventually, Miguel plans to become a professor who can put into practice all that he has and will continue to learn from his tenure at Harvard. His dedication to this goal has been acknowledged and supported by the Building Future Faculty Program at NC State University (a program that selects individuals committed to promoting diversity in higher education). Miguel plans to base his future career in cancer research and the statistical issues dealing with such a complicated disease.
Suzanne Szwarc Dalhberg
Suzanne Dahlberg graduated from the Department of Biostatistics in 2005, and joined the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute (DFCI) in 2006. Her primary responsibility is collaborative clinical trials research with investigators from the Eastern Cooperative Oncology Group (ECOG) and the Dana-Farber Acute Lymphoblastic Leukemia (ALL) Consortium. This work involves the design, conduct, and analysis of phase I, II, and III studies on behalf of the ECOG Thoracic Committee and both the adult and pediatric ALL groups at DFCI. Suzanne also sits on the Dana-Farber/Harvard Cancer Center Adult Scientific Review Committee (SRC). Her responsibilities are described below in more detail.
As the senior statistician of the Thoracic Committee for ECOG, Suzanne oversees all aspects of their clinical trials and correlative studies. Her work includes the ongoing phase III study E1505 to evaluate the effect of bevacizumab in combination with adjuvant chemotherapy in patients with completely resected early stage non-small cell lung cancer. Her major responsibilities have been the design and activation of new studies, monitoring and preparation of data monitoring committee reports for ongoing studies, and conducting final primary analyses and subset analyses on closed studies. Suzanne's collaboration with the Thoracic Committee has resulted in an editorial discussing the issues surrounding a conclusion of noninferiority from trials designed for superiority and a manuscript that presents an analysis of the association between bevacizumab-induced hypertension and patient outcomes. She has also co-authored a book chapter on noninferiority trials.
At DFCI, Suzanne is the primary statistician for the ALL Consortium adult protocols, whose primary objective is assessing pediatric treatment regimens in adults in an effort to increase the disease cure rate. She also provides statistical support for pediatric ALL projects as needed, and these projects include retrospective analyses of minimal residual disease as a predictor of relapse, secondary malignant neoplasms after treatment with dexrazoxane, the association between pancreatitis and CFTR mutations, analysis of gene mutations in T cell ALL, and neurocognitive outcomes of patients.
As a member of the Dana-Farber/Harvard Cancer Center (DF/HCC) Adult SRC, Suzanne reviews the scientific merit and statistical design of new protocols as well as provide feedback to senior leadership to help direct the scientific agenda and priority-setting of the DF/HCC.
Denise Scholtens, Ph.D. graduated from our program in 2004, and is an Assistant Professor in the Department of Preventive Medicine, Program in Biostatistics at Northwestern University Feinberg School of Medicine (FSM) in Chicago, IL. She is a member of the Robert H. Lurie Comprehensive Cancer Center (RHLCCC) Biostatistics Core and her statistical contributions to the design and analysis of RHLCCC clinical and basic science studies for a variety of cancers have supported publications in journals including Cancer, Journal of General Internal Medicine, Journal of Surgical Research, Journal of Molecular Diagnostics, Journal of Surgical Oncology, and International Journal of Cancer.
Recent joint work with collaborators in the Department of Neurological Surgery
at FSM allowed for a blend of more classic biostatistics methodology with computational
biology data analysis yielding a true exercise in translational medicine research.
Dr. Scholtens worked in close conjunction with a multidisciplinary team of investigators
to develop and execute a network model analysis identifying cooperative genetic
gain and loss events in gliomas. Culling data from The Cancer Genome Atlas Pilot
the team transitioned from high-throughput network modeling of gene expression
and chromosomal copy number screens to the pinpointing of two new physical protein
interaction mechanisms contributing to the dysregulation of epidermal growth
factor signaling in glioblastomas and ultimately impacting survival outcome
for those stricken with the disease. A pair of companion papers describing this
work appeared in the Journal of the American Medical Association in 2009 and
has motivated new glioma-related and statistical methodology projects currently
In support of her collaborative work, Dr. Scholtens maintains active research
in node-and-edge graph theoretic analyses of molecular interaction data. Statistical
likelihood estimation techniques for local graph features and tools for generating
reference distributions when exploring associations among multiple graphs are
published in Bioinformatics. These works, along with research done in collaboration
with investigators at the European Bioinformatics Institute (EBI) and appearing
in print in Genome Biology recently culminated in a data analysis pipeline featured
in the Nature Publishing Groupís Nature Protocols in 2009, fully executable
using the RpsiXML, ppiStats, and apComplex Bioconductor software packages (http://www.bioconductor.org).
Dr. Scholtens regularly contributes to training seminars on the use of these
and other high-throughput data analysis methods, both at Northwestern and other
US universities and at the EBI in Cambridge, UK.
Dr. Scholtens regularly provides peer reviews for books in the Springer Series in Statistics, as well as the journals Applied Bioinformatics, Bioinformatics, BMC Bioinformatics, Genome Biology, Journal of Statistical Planning and Inference, Nature Biotechnology, Statistical Applications in Genetics and Molecular Biology, and Statistics in Medicine. She served as an assistant statistical editor for the Archives of Internal Medicine from 2006-2008 and currently performs regular statistical reviews for the Journal of the American Medical Association. She also serves on the Program Committees for the International Conference on Intelligent Systems for Molecular Biology/European Conference on Computational Biology (ISMB/ECCB) in the protein interactions and molecular networks subject area and for the Critical Assessment of Massive Data Analysis (CAMDA) conference series.
Shuli Li grew up in Jinan, a beautiful city located in the northeast part of China. In 1998, she came to the U.S. to study Nutritional Science at Rutgers University. Up until 2000 Fall, she thought biology would be her final professional destiny until a required graduate course in Biostatistics department became a turning point in her career. "Not only I was fascinated by those statistical methodologies, I was also amazed by the fact how beautifully statistics and life science were fused together", Shuli says. After graduating with a master's degree in Biostatistics, she got a job offer from Dana Farber Cancer Institute and came to Boston in 2003 as a Biostatistician. "My understanding of statistics and its application in clinical research have been deepened through collaborating with investigators on a wide variety of projects". Shuli viewed her experience of working at DFCI as an even more invaluable asset in her career. After a few years working Shuli decided to go back to school to get her Ph.D degree because "Now I see clearly that biostatistics is something that I wanted to pursue further. More importantly, I can get the answers to the questions I have long been fascinated with". As a third year doctoral student, Shuli's current research interest to obtain unbiased estimate of treatment effect in randomized cancer trials when noncompliance is present. She believes that her background in biology and statistics would be her greatest asset. Eventually she wants to dedicate her time on developing and applying useful statistical methods in cancer related research.
Dr. Patricia L. Stephenson currently serves as the lead statistician for studies in ovarian cancer, renal cell carcinoma, gastrointestinal stromal tumors, and non-small cell lung cancer at Rho, Inc. (Chapel Hill, NC). She joined Rho after working with the Eastern Cooperative Oncology Group (ECOG) Coordinating Center at the Dana-Farber Cancer Institute. As a research fellow jointly at the Harvard School of Public Health and the Dana-Farber Cancer Institute, she served as the primary statistician for ECOG lung and breast cancer studies and worked on special projects in cancer. Her research focused on design issues in clinical trials of survival data and the early detection of ovarian cancer.
Her work at Rho over the past 6 years has continued to focus on oncology. Dr. Stephenson was selected as one of 42 participants to attend the 1st American Association for Cancer Research (AACR) Cancer Biostatistics Workshop (July 2008), an intensive week-long training program preparing statisticians to face the challenges associated with our emerging understanding of cancer biology and the complex design and analysis issues that often arise in cancer clinical trials of targeted therapies. Since the workshop, she has co-designed a Statistics Oncology Seminar (S.O.S.) training series for over 55 biostatisticians and selected members from other departments at Rho, Inc.
Her work has included support in the development of protocols and statistical analysis plans for both pharmaceutical and government-funded projects. Other work has included applications of mixed models, survival data techniques, Bayesian methodology, and preparation of specialized reports for submission to the FDA. The motivation for her continued involvement in the area of oncology continues to be the impact that this disease has had on family and friends.
Originally from Harlingen, TX, Layla Parast became interested in biostatistics after taking a course in survival analysis and longitudinal data analysis while at Stanford University where she received her Masters degree in Statistics. She was able to further explore the field by collaborating with Stanfordís School of Medicine and Department of Radiation Oncology to determine the benefit of surgery for retroperitoneal sarcoma. In addition, she consulted with physicians at the Lucile Packard Childrenís Hospital to detect a decrease in pediatric mortality rates after the implementation of a rapid response team. After working as a statistical consultant for Palo Alto Medical Foundation, Layla came to Harvard University as a doctoral candidate in the Department of Biostatistics.
Now in her third year at Harvard, Laylaís current research with her dissertation advisor, Dr. Tianxi Cai, focuses on landmark prediction of survival. In studies designed to develop prognostic classifiers based on predictive markers, measurements are often ascertained at baseline and patients are followed over time for the occurrence of certain clinical conditions or death. When there are multiple markers available to assist in prediction, it is of clinical interest to construct an optimal prognostic index based on available marker information. Although recent advancement in technology has lead to a wide range of genetic and biological markers that hold great potential in improving the prediction of survival outcomes and such new classifiers promise better disease prognosis, the accuracy in identifying short term vs. long term survivors remains unsatisfactory for most complex diseases. It has been often argued that short term clinical outcomes may have potential in predicting long term survival. To optimally select prevention and treatment strategy, it would be of great interest to develop comprehensive prognostic systems for patients that could make prediction about both the short term survival and the long term survival given the short term outcome. Such evaluations provide a more complete picture of the long term trajectory of disease progression and thus can be helpful for patients to make risk benefit decisions.
Laylaís dissertation develops conditional prognostic rules for the prediction
of long term outcomes based on baseline marker information along with short term
outcome. This proposed method could potentially shed light on how the incremental value of a new marker may vary across sub-populations. For example, if a new marker is only useful for predicting time of death among those with a good prognosis, one may expect that the incremental value is near zero for predicting whether a patient will have metastasis by some landmark time, but is high for predicting time of death among who are metastasis-free at the landmark time. This may provide a useful tool for practitioners to decide when the new marker is needed in addition to conventional risk factors.
Layla illustrates this procedure using a breast cancer gene expression study where the predictive ability of a chromosome instability genetic score, denoted by CIN25, is examined for metastasis-free and overall survival in breast cancer patients. Layla will present her research at the 2010 Institute of Mathematical Statistics Annual Meeting Gothenburg, Sweden as a recipient of the 2010 IMS Laha Travel Award.
While at Harvard, Layla has also had the opportunity to work on a number
of collaborative projects beyond her dissertation work. In the summer of
2008 she was a research assistant at the Dana-Farber Cancer Institute Department
of Biostatistics where she examined actual versus preferred sources of HPV
Information Among black, white, and Hispanic parents. She also had the opportunity
to apply methods taught in the Bayesian Data Analysis Course to look at
Bayesian methods in a meta-analysis for rare events.
Dr. Ping Hu is employeed in the Biometry Research
Group, Division of Cancer Prevention, at the National Cancer Institute.
She graduated from our doctoral program in June 1996. Her dissertation work
focused on cancer clinical trials. Her research at NCI currently involves
the following five projects.
PLCO Cancer Screening Trial:
Dr. Hu has participated in this project since 2001. She serves as the key
point person from NCI for the lung subcommittee. She works with study PI's,
medical oncologists, radiologists and other scientists from different Screening
a statistician, her other important work is to help solve statistical questions
and provide statistical consultations. She has been actively involved in
two papers i) Baseline Chest Radiograph for Lung Cancer Detection in the
Randomized Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial,
authored by Martin Oken, Pamela Marcus, Ping Hu etc, and ii) Results of
Baseline and Three Subsequent Lung Cancer Screens in the Intervention Arm
of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Randomized Cancer
Screening Trial, authored by William Hocking, Ping Hu etc. The first one
has been published in JNCI in 2005 and second one has been accepted by JNCI
in 2009. She also collaborated on the paper "Factors Associated with Human
Small Aggressive Non-Small Cell Lung Cancer" which was published in Cancer
Epidemiology Biomarkers & Prevention in 2007.
Dr. Hu is currently leading
Study of the cumulative false positive risk for PLCO lung
cancer screening test - chest x-ray T0-T3.
Study of the sensitivity
of chest x-ray and cancer preliminary sojourn time for the PLCO and NLST
National Lung Screening Trial (NLST): Dr. Hu worked with Dr. Richard
Fagerstrom, DCP to develop the statistical part for the design of the NLST.
Dr. Hu employed a statistical model developed by Hu and Zelen in 1997 specifically
for early detection trials. She did power analysis and estimated the sample
size for NLST. Dr. Hu further developed a statistical model to estimate
the mortality rate by incorporating many possibilities, such as i) variable
compliance rates; ii) different sensitivities and iii) different time points
for participants transferring from control screening to study screening,
in preparation for the NLST 2006 DSMB meeting. Dr. Hu also developed a statistical
model to estimate lung cancer cases at 10-15 years of follow-up for both
study and control groups, in preparation for the NLST 2007 DSMB meeting.
Lung Cancer Project Among Tin Miners In Yunnan, China: Dr. Hu led and
designed two projects: i) Lung cancer follow-up project among tin miners
in Yunnan, China. Dr. Hu collaborated with scientists in China to collect
10 year follow-up data which includes participant characteristic as well
as clinical results information. Dr. Hu evaluated the screening results
of chest x-ray and sputum cytology conducted in this study. Three papers
have been published/submitted:
Fan YG, Hu P, Jiang Y, Chang RS, Yao SX, MD; Wang W, He J, Prorok
P, Qiao YL. Association between Sputum Atypia and Lung Cancer Risk in
an Occupational-based Cohort in Yunnan, China. CHEST 2009;
Fan YG, Jiang Y, Chang RS, Yao SX, Hu P, Qiao YL. Retrospective analysis
of screening results of lung cancer cases among occupational population
at high risk of lung cancer. Chinese Journal of Lung Cancer
2007; 10(2): 102-106.
Prior lung disease and lung cancer risk in an Occupational-based Cohort
in Yunnan, China. Fan YG, Hu P, Jiang Y, Chang RS, Yao SX, MD; Wang
W, He J, Zhou QH, Prorok P, Qiao YL. (submitted to American Journal
of Respiratory and Critical Care Medicine in one month).
ii) Lung cancer biomarker project. Dr. Hu and collaborators from EDRN designed
and proposed this project, which is considering the use of sputum samples
from a completed screening study of tin miners in Yunnan, China. A pilot
study was conducted to evaluate long-time-stored specimens from a lung cancer
screening cohort of Tin Miners in Yunnan, China. Sputum specimens that have
been stored at room temperature for more than 10 years were evaluated. Preliminary
results show that both genotyping and methylation biomarkers can be generated
from the sputum specimens with success rates of about 80% and 73% respectively.
The pilot study indicates that i) large quantity of specimens are available
and still useful for generating biomarkers and ii) the archived sputum specimens
collected annually prior to lung cancer manifestation are unique and valuable
resources for discovering and evaluating early lung cancer biomarkers. The
long term goals are i) to discover and validate biomarkers for cancer early
detection and prevention by using archived specimens collected before cancer
diagnosis and ii) to integrate biomarkers into statistical models to study
the relationship between the biomarkers and survival end points.
Study for Early Detection of Lung Cancer: A new proposal is headed by Ping
Hu in collaboration with Drs. Eva Szabo, Neil Caporaso, Wendy Wang from DCP,
DCEG of NCI and Drs Jie He, Youlin Qiao from CICAMS, China. The aims of
this project are i) to collect rare and unique bio-specimen samples and
ii) to indentify early biomarkers for early stage lung cancer. The lung
cancer specimens from the tissue bank at Cancer Institute, Chinese Academy
of Medical Sciences (CICAMS) will be studied. Subjects targeted are stage
I & II, tumor size<3cm & AAH lesion lung cancer patients. It is estimated
that CICAMS could accrue about 80 stage IA, tumor size<3cm or 10 stage IA,
tumor size<3cm & AAH lung cancer patients. Follow-up information for study
participants could be attained to 99%. It is reported that bio-specimens
for AAH lung cancer patients are very rare, valuable and unique resources
in the world.
Esophageal Cancer Study in China: CICAMS is planning a new
project on Esophageal Cancer Study. Dr. Hu, as a Statistical Consultant,
is collaborating with them.
The study is carried out at 100 medical hospitals/centers
in whole China.
50,000 participants will be accrued in two years
data will be collected: such as baseline, pathology, etiology, epidemiology,
clinical result, etc.
And in statistical modelling research, Dr. Hu is collaborating
with her colleagues at BRG, DCP/NCI. They have developed two new methodologies
for estimating the cumulative risk of false positive for multiple screening
exams with incomplete test results. Dr. Hu is also collaborating with Professor
Marvin Zelen at Harvard University. They have developed new methodologies
for analysis of cluster randomized trials.
Dr. Ann Lazar is currently a postdoctoral fellow in the Department of Biostatistics. She has been actively engaged in developing novel statistical methods for randomized clinical trials of patients diagnosed with cancer. She has studied the heterogeneity of treatment efficacy within these trials to evaluate whether a particular cohort of patients or subgroup responds differently to a treatment. In particular, Dr. Lazar and her colleagues have further developed Subpopulation Treatment Effect Pattern Plot (STEPP), a graphical display of the pattern of differences in treatment effectiveness as a function of a continuous covariate. Her work includes extending the STEPP method to the competing risk setting. This extension is particularly useful for identifying biomarkers as prognostic factors for the risk of disease recurrence and predictors of treatment effectiveness. Dr. Lazar has worked under the direction of her post-doctoral mentors: Professor Richard Gelber of the International Breast Cancer Study Group and Professor Robert Gray of the Eastern Cooperative Oncology Group.
Dr. Lazar has pursued her independent research agenda in cancer by receiving training from members of the Harvard School of Public Health (HSPH). In fact, Dr. Lazar has been mentored by two professors from HSPH: Professor Richard Gelber of the International Breast Cancer Study Group (IBCSG) and Professor Robert Gray of the Eastern Cooperative Oncology Group (ECOG). These professors have worked very closely with Dr. Lazar to help her develop the research skills needed to assume a leadership role in cancer research. Her mentors have also introduced Dr. Lazar to collaborators from other universities with interests in cancer research. She has completed over forty hours of training in cancer clinical trials. This training provided Dr. Lazar with the skills needed to analyze and interpret data from two ECOG phase II clinical trials of patients with advanced unresectable hepatocellular carcinoma and gastric cancer. She has also completed a course in writing and publishing research findings. Additional funding from HSPH was provided to Dr. Lazar to allow her to continue her training with a technical science writer. In addition, she has attended numerous workshops and seminars taught by faculty and staff from HSPH. These include four sessions of responsible conduct in research training, public speaking courses for teachers, workshops on how to prepare your application materials for a position in academia as well as applications for grant funding. Finally, Dr. Lazar has been an active member at conferences and groups dedicated to cancer research: Quantitative Issues in Cancer Research seminars at HSPH, Dana-Farber Cancer Institute retreats, Eastern North American Region/International Biometric Society in 2008 and 2009, 2009 17th annual Harvard Schering-Plough conference focusing on global clinical trials and the 2008 conference on emerging quantitative issues in parallel sequencing hosted by HSPH. These training experiences have ensured that Dr. Lazar is prepared for a career in cancer research.
Training at the Harvard School of Public Health has helped Dr. Lazar realize her potential as a contributor to research in cancer. This traineeship provided Dr. Lazar with the tools needed to develop novel statistical methodology. She and her colleagues have submitted three manuscripts that are under review at peer-reviewed journals including the Journal of Clinical Oncology as well as recently publishing a paper. In addition to these papers, she and her colleagues have developed free software that can implement Subpopulation Treatment Effect Pattern Plot (STEPP), a novel approach to graphically illustrate the pattern of differences in treatment effectiveness as a function of a continuous covariate. For example, this software was used to analyze data from the Breast International Group (BIG) 1-98 clinical trial, which compared letrozole versus tamoxifen as adjuvant therapy for postmenopausal women with hormone-receptor positive breast cancer. Dr. Lazar has published a technical report of a phase II study of ZD1839 in advanced unresectable hepatocellular carcinoma. She has also presented her scholarly work at conferences and meetings, such as the Eastern North American Region/International Biometric Society in 2008 and 2009 in San Antonio Texas and New Orleans Louisiana, respectively, and Quantitative Issues in Cancer Research Working Seminar Group. She has been invited to present her work at universities, most recently at the New Researchers in Statistics and Probability at the University of British Columbia, Canada. By presenting her work at these different venues, Dr. Lazar has been able to expand her network of colleagues in cancer research. Finally, after Dr. Lazar completes her training at the Harvard School of Public Health, she will continue her research at the University of California, San Francisco (UCSF) in the position of Assistant Professor.
Jie Jenny Huang
For the past two years, Dr. Huang has been working as an associate director in early clinical development on oncology trials at Genentech, Inc., a member of the Roche group, South San Francisco, CA. She enjoys being a strategic partner in and contributing to the development of several promising oncology drugs that may improve the care for cancer patients. Jie has been overseeing the development, design, conduct, as well as the analysis of oncology clinical trials on Non-Small Cell Lung Cancer (NSCLC), Breast Cancer, Colorectal Cancer and Multiple Myeloma.
Before moving to California, Dr. Huang was an assistant professor at Department of Preventive Medicine and the founding director for the Biostatistics Collaboration Center at Northwestern University, Feinberg School of Medicine, Chicago, IL. She worked closely with many investigators on NIH/NCI grant applications as well as projects evaluating different imaging techniques on cancer treatment response measurement.
Since 2007, Dr. Huang has been reviewing grants for NCI Small Grant Program for Cancer Epidemiology on a regular basis and enjoyed helping investigators get funded to further their research on cancer epidemiology which may lead to better cancer treatment and cancer care.
Her research interests are survival data analysis and innovative clinical trial design and analysis.