Biostatistics Training Initiative
Dr. Richard Cook, University of Waterloo
Dr. Gregory Pond, McMaster University
Recent technological advances have led to far more complex and much larger datasets in cancer research. There is a critical need for biostatisticians who can work with these data, ensuring they can be used to inform on the next generation of cancer treatments. Since 2010, the Biostatistics Training Initiative (BTI), previously known as the Oncology Research Training and Methods Program (ORTMP), has placed Master’s students from the University of Waterloo in eight-month internships with mentors at Ontario cancer centres. In doing so, the BTI is training the next generation of biostatisticians in the province, ensuring they are equipped with the technical knowledge, skill set and experience to deal with the changing landscape of cancer treatment and clinical trials.
Based on the success of the program in its first five years and feedback from the Ontario research community regarding its continued need for training, expertise and placement, the BTI expanded in 2016. The new expanded BTI engages a broader group of trainees, including PhDs and postdoctoral fellows, and fosters a sense of community amongst participants through a visiting lecture and monthly seminar series.
The BTI is funded through the Ontario Institute for Cancer Research.
The BTI Internship Program is a continuation of the earlier OICR-funded ORMTP in which selected top Master’s students in Biostatistics from the University of Waterloo are placed in eight-month internship positions in cancer research centres across Ontario with joint mentorship by a biostatistician and an oncology researcher. Up to four fully funded placements will be made each year from 2016-2020.
For further information and instructions to apply, please visit https://uwaterloo.ca/biostatistics-training-initiative/internship
The BTI Fellowship Program is designed to support doctoral and postdoctoral training in biostatistics for cancer research. The primary objective of the Program is to support the training of the next generation of biostatisticians in the province, ensuring they are equipped with the technical knowledge, skill set and capability for the application of rigorous quantitative methods for cancer research. Through the receipt of a BTI Studentship/Fellowship Award, biostatistics or statistics doctoral students and postdoctoral fellows at an Ontario university will engage in interdisciplinary, collaborative cancer research with investigators located at an Ontario Host Institution.
Any questions about these awards or how to apply may be emailed to email@example.com.
A monthly seminar series will be held by WebEx/teleconferencing on advanced issues in clinical and population health advances, as well as methodological challenges arising in cancer research. The aim is to engage clinicians, statisticians, interns and fellows. The focus of the seminars will include:
- recently completed high impact trials in oncology within the group and internationally,
- recent developments in the design and analysis of cancer clinical trials,
- biostatistical and more general methodological challenges arising in the planning or conduct of trials, and
- reports of ongoing methodological research conducted by engaged faculty, students and interns.
BTI Fellowship Program
The BTI Fellowship Program is designed to support doctoral and postdoctoral training in biostatistics for cancer research. The primary objective of the Program is to support the training of the next generation of biostatisticians in the province, ensuring they are equipped with the technical knowledge, skill set and capability for the application of rigorous quantitative methods for cancer research. Through the receipt of a BTI Studentship / Fellowship Award, biostatistics or statistics doctoral students and postdoctoral fellows at an Ontario university will engage in interdisciplinary, collaborative cancer research with investigators located at an Ontario Host Institution.
BTI Studentship / Fellowship Award
Up to four Studentship/Fellowship Awards each valued at $15,000 per year for up to two years will be made available. Funds must be used to support doctoral students or postdoctoral fellows conducting biostatistical or statistical research which has an application to a problem in oncology.
Postdoctoral fellows are strongly encouraged to seek leveraged funds from CANSSI at http://www.canssi.ca/research-and-training-opportunities/canssi-postdoctoral-fellowships/.
Eligible applicants include:
- Students currently enrolled in the second or higher year of a Biostatistics or Statistics Doctoral Program at an Ontario university; or
- Current Postdoctoral Fellows in Ontario or individuals beginning a postdoctoral position in Ontario within six months of the application deadline; fellows must have completed doctoral training in biostatistics or statistics.
Applications, composed of completed Form I, letters of support, biosketch and curriculum vitae (CV), must be submitted to firstname.lastname@example.org by 5 p.m. EST on January 31, 2019. Additional details are available in the Applicant guide. Awards will be announced by the end of February 2019 with funds available by April 1, 2019.
BTI Seminar Series
October 23 2018, 2 – 3:00 p.m.
Dr. Yeying Zhu, Assistant Professor at Department of Statistics & Actuarial Science, University of Waterloo
Title: Matching in causal inference for high-dimensional data.
Abstract: One of the commonly used approaches to the causal analysis of observational data is matching, which is a systematic way to find comparable treated and untreated subjects based on an appropriate function of the covariates. The causal treatment effects are then estimated from the matched dataset as if the treatment were randomly assigned. In this presentation, we discuss how sufficient dimension reduction can be used to aid causal inference. We propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Under the ignorability assumption, i.e., there is no unmeasured confounders, the consistency of the proposed approach requires a weaker common support condition than the one we often assume for propensity score-based methods. We develop asymptotic properties and conduct simulation studies to show the relative performance among several matching estimators. A data application is presented to illustrate the proposed matching approach.
Key presentation objectives:
- Give a brief introduction about causal inference based on potential outcomes framework, matching algorithms and sufficient dimension reduction methods.
- Demonstrate how sufficient dimension reduction can be used to relax the assumptions for causal inference and to adjust for confounding in a high-dimensional setting.
- Causal inference:
Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945-960.
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics, 25(1), 1-21.
Dr. Yeying Zhu received her B.Sc. in Statistics at East China Normal University in 2006. She went on to complete her master’s degree at the National University of Singapore, and later joined Penn State University in 2008. In 2013, she graduated with a PhD degree in Statistics. She has been an Assistant Professor at Department of Statistics & Actuarial Science, University of Waterloo since 2013.
Dr. Zhu’s research interest lies in the interface between causal inference and machine learning with a focus on the development of algorithms to adjust for confounding in a high-dimensional setting. She is also interested in mediation analysis, which is the study of how a treatment affects the outcome through an intermediate variable. Applications of Dr. Zhu’s research lie in biomedical studies, public health and social sciences.
Ontario Institute for Cancer Research
Boardroom 6-12, MaRS Centre
661 University Avenue, Suite 510, Toronto, Ontario
University of Waterloo
M3 3001, 200 University Avenue West
- Register your name and email:
a) Go to https://cc.callinfo.com/r/1vdvtwn1sre8k&eom;
b) Register your first and last name, email address, and click “Register Now”. Registration can be done several minutes in advance of the webinar. No password is required.
- Join the teleconference:
a) Dial-in using: 1.800.503.2899, access code: 2484428;
b) Important: Phones will be muted during the presentation, and unmuted for question period. All questions must be asked during question period.
In advance of the seminar, we strongly recommend testing the WebEx application to ensure your computer is compatible. To do so, use https://www.webex.com/test-meeting.html to launch the test. You will be required to enter your name and email address. It will then run a test WebEx meeting advising if it was successful. Should you experience technical difficulty, please contact your IT administrator.
We look forward to your participation.
Past Seminar Series
- Population Health Cohorts and Two-Phase Studies
Dr. David Soave, PhD, Assistant Professor, Department of Mathematics, Wilfrid Laurier University
May 22, 2018.
- Treatment-biomarker interaction through local likelihood method
Dr. Wenyu Jiang, Associate Professor, Department Mathematics and Statistics, Queen’s University
April 24, 2018
- Underestimation of Variance of Predicted Mean Health Utilities Derived from Multi-Attribute Utility Instruments: The Use of Multiple Imputation as a Potential Solution
Dr. Eleanor Pullenayegum, Senior Scientist, Hospital for Sick Children
March 27, 2018
- Presentations from the 2017 BTI Interns
February 27, 2018
- Bayesian Adaptive Designs – From Theory To Practice
Dr. Jack Lee, Dept. of Biostatistics, University of Texas MD Anderson Cancer Center
January 23, 2018
- Bayesian methods for biomarker threshold models with binary and survival data [PDF]
Bingshu Chen, PhD, Queen’s University
November 15, 2017
- BTI Distinguished Lecture [PDF]
Jerry Lawless and Stephen George
October 24, 2017
September 26, 2017
- Biomarker-based subgroup identification for precision medicine[PDF]
Viswanath Devanarayan, PhD, FAAPS, Charles River Laboratories
May 30, 2017
- Multiple outputation for longitudinal data subject to irregular observation[PDF]
Eleanor Pullenayegum, Sick Kids
April 25, 2017
- A novel region-based Bayesian approach for genetic association with next generation sequencing (NGS) data coverage.[PDF]
Laurent Briollais, Lunenfeld Mount Sinai
February 28, 2017
- Biomarkers for Treatment Stratification in Early Prostate Cancer[PDF]
Dr. David Berman, MD, PhD, Director, Queen’s Cancer Research Institute, Queen’s University
Tuesday, January 24, 2017
Dr. Rinku Sutradhar
Senior Scientist, Institute for Clinical Evaluative Sciences
TExamining cancer screening adherence in Ontario using multistate transitional models.
Understanding disparities among women in breast cancer screening adherence is of considerable interest, however prior work has been methodologically limited. Our work longitudinally examines adherence to screening, and determines factors associated with becoming adherent. The cohort consisted of 2, 537, 960 women age 50-74 from Ontario, Canada. Using age as the time scale, a relative rate multivariable regression was implemented under the 3-state transitional model to examine the association between covariates (all time-varying) and the rate of becoming adherent. Individual- and physician-level characteristics played an important role in a woman’s adherence to screening. Our research improves the quality of evidence regarding disparities among women in adherence to breast cancer screening, and provides a novel methodological foundation to investigate adherence for other types of cancer screening, including cervix and colorectal cancer screening.
Tuesday December 13, 2016
Methodology to Identify Environmental and Host Genomic Association on Human Microbiome Data
Dr. Wei Xu
Assistant Professor, University of Toronto, Dalla Lana School of Public Health
Technological advances in genomic sequencing have enabled researchers to unveil the wide variability of bacteria presented within different locations of the body, i.e. the microbiome, and how it relates to disease. However, our understanding of how microbiomes affect diseases is still unclear. It is necessary to better understand both environmental and host genetic factors impact the composition of the microbiome to improve disease management. Powerful statistical and bioinformatics tools are needed to overcome these knowledge gaps.
Friday November 25, 2016
Phase II Study Design in Oncology Drug Development
Dr. Wendy Parulekar, MD
Senior Investigator, Cancer Clinical Trials Group, Queens University
The Phase II trial has a pivotal role in drug development since the decision to proceed with further evaluation of a drug/ drug combination is based on the efficacy and safety data generated from this type of study. Important additional generated from the Phase II trial may include elucidation of the mechanism of action of a new therapy and delineation of the target population for administration. The objectives of this webinar are to provide the clinician perspective on Phase II trial design and conduct including classification of designs and statistical framework. Examples of actual clinical trials will be drawn from the casebook of the Canadian Cancer Trials Group.
May 20, 2016
- Patient Reported Outcome Measures (PROMs) for use with children and adolescents: a view across disciplines. [PDF]
Gillian Lancaster, PhD, Lancaster University, United Kingdom
April 22, 2016
- How clinical trial design pitfalls slow progress against cancer [PDF]
David J. Stewart, MD, Head, Division of Medical Oncology, University of Ottawa/The Ottawa Hospital
April 1, 2016
- The analysis of progression-free survival, overall survival and markers in cancer clinical trials [Video]
Richard Cook, PhD,Professor of Statistics, Department of Statistics and Actuarial Science, University of Waterloo
Cancer clinical trials are routinely designed on the basis of event-free survival time where the event of interest may represent a complication, metastasis, relapse, or progression. This talk is concerned with a number of statistical issues arising from the use of such endpoints including the interpretation of results based on composite endpoints, the consequences of naïve analysis of event times subject to dual censoring schemes, and the causal interpretation of treatment effects. Related issues in the joint analysis of longitudinal and survival data will also be highlighted.