Biostatistics Training Initiative

Leaders

Dr. Richard Cook, University of Waterloo
Dr. Gregory Pond, McMaster University

Background

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.

Internships

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

Fellowships

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 scientificsecretariat@oicr.on.ca.

Seminar series

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:

  1. recently completed high impact trials in oncology within the group and internationally,
  2. recent developments in the design and analysis of cancer clinical trials,
  3. biostatistical and more general methodological challenges arising in the planning or conduct of trials, and
  4. 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/.

Applicants

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.

Application deadline

Applications, composed of completed Form I, letters of support, biosketch and curriculum vitae (CV), must be submitted to teresa.petrocelli@oicr.on.ca 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

May 21, 2019, 2 – 3 p.m.

Dr. Wenqing He
Professor, Department of Statistical and Actuarial Sciences, University of Western Ontario

Title: Data adaptive support vector machine with application to prostate cancer imaging study

Abstract:Support vector machines (SVM) have been widely used as classifiers in various settings including pattern recognition, texture mining and image retrieval. However, such methods are faced with newly emerging challenges such as imbalanced observations and noise data. In this talk, I will discuss the impact of noise data and imbalanced observations on SVM classification and present a new data adaptive SVM classification method.

This work is motivated by a prostate cancer imaging study conducted in London Health Science Center. A primary objective of this study is to improve prostate cancer diagnosis and thereby to guide the treatment based on statistical predictive models. The prostate imaging data, however, are quite imbalanced in that the majority of voxels are cancer-free while only a very small portion of voxels are cancerous. This issue makes the available SVM classifiers typically skew to one class and thus generate invalid results. Our proposed SVM method uses a data adaptive kernel to reflect the feature of imbalanced observations; the proposed method takes into consideration of the location of support vectors in the feature space and thereby generates more accurate classification results. The performance of the proposed method is compared with existing methods using numerical studies.

Biosketch: Dr. He obtained his PhD in 2002 in Statistics from University of Waterloo. Following a PDF and Research biostatistician position at Mt. Sinai Hospital, Toronto, Dr. He joined the University of Western Ontario as an Assistant Professor in the Dept. of Statistical and Actuarial Sciences. During 2011-2012, Dr. He was a Visiting Research Scholar at the University of Michigan, Dept. of Biostatistics.

Dr. He’s research focuses on analysis of high dimensional data, image data, statistical genetic data, survival data and correlated data. He is also interested in missing data and measurement models, data mining, and statistical computing.

Key presentation/learning objectives:

  • Understand the rational prostate cancer image study;
  • Understand the classification method, especially support vector machine;
  • Understand why and how we can modify the SVM to improve its performance on imbalance data.

Background reading:

  • Ward, A. D., Crukley, C., McKenzie, C. A., Montreuil, J., Gibson, E., Romagnoli, C., Gomez, J., Moussa, M., Chin, J., Bauman, G. S. and Fenstor A. (2012) Prostate: registration of digital histopathologic images to in vivo MR image acquired by using endorectal receive coil.  Radiology , 263, 856-864.
  • Friedman, J., Hastie, T., and Tibshirani, R. (2001). The elements of statistical learning. Springer series in statistics Springer, Berlin.

Locations:

Ontario Institute for Cancer Research
Boardroom 5-20/21, MaRS Centre
661 University Avenue, Suite 510, Toronto, Ontario

University of Waterloo
M3 3001, 200 University Avenue West
Waterloo, Ontario

Online:

  1. Register your name and email:
    a) Go to https://cc.callinfo.com/r/1duteeo6eyfvy&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.
  2. 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

March 26, 2019

  • Inaugural BTI Student Research Day – Waterloo University

January 22, 2019

  • Causal Inference Methods for Quality of Care Comparisons in Health Services Research
    Dr. Olli Saarela, Assistant Professor, Dalla Lana School of Public Health, University of Toronto

October 23, 2018

  • Matching in causal inference for high-dimensional data.
    Dr. Yeying Zhu, Assistant Professor at Department of Statistics & Actuarial Science, University of Waterloo

September 25, 2018

  • 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


March 27, 2018


February 27, 2018


January 23, 2018


November 15, 2017


October 24, 2017


September 26, 2017


May 30, 2017


April 25, 2017


February 28, 2017


Tuesday, January 24, 2017

Dr. Rinku Sutradhar
Senior Scientist, Institute for Clinical Evaluative Sciences

TExamining cancer screening adherence in Ontario using multistate transitional models.

Abstract:
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

Abstract:
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

BTI title slide from Friday November 25
Download the slides

Abstract:
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


April 22, 2016


April 1, 2016

Abstract:
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.