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 firstname.lastname@example.org.
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
For the February 2018 Call for Applications, up to four awards, each valued at $15,000 per year for two years will be made available. All funds from this award must be used to support a doctoral student or postdoctoral fellow conducting biostatistical or statistical research which has an application to a problem in oncology.
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 email@example.com by 5 p.m. EST on January 30, 2018. Additional details are available in the Applicant Guide.
The awards will be announced by the end of February 2018 with funds available by April 1, 2018.
For any questions please contact Teresa.firstname.lastname@example.org
BTI Seminar Series
March 27, 2018, 2-3 p.m.
Speakers: 2017 BTI Interns
Mentor: Dr. Juri Reimand, OICR
Title: Predicting long-range regulatory interactions through gene co-expression analysis across multiple cancer types
Abstract: It is important to explore the insights of gene regulation in cancer. Increasing evidence suggests that genes connected by chromatin loops are transcriptionally co-regulated; co-expression analysis can therefore be used to better understand these regulatory principles and gene function. Using high-throughput transcriptomic data from large scale projects such as the Pancancer Analysis of Whole Genomes (PCAWG), we systematically identified gene pairs associated with long range regulatory interactions. It was found that several subsets of genes are opposite in regulatory patterns for those in tumors and in normal tissues. We hypothesized that gene pairs are dysregulated in cancer if they are differentially co-expressed in healthy individuals. We also found that genes which are proximal to each other in three-dimensional space are more likely to be co-expressed with each other than randomly sampled pairs of genes. In this talk I will present a method based on the three-dimensional conformation of the genome for a better understanding of the regulatory principles and the gene functions. Survival analysis with pathway enrichment are used with high-throughput transcriptomic data from large scale projects.
Mentor: Drs. Rinku Sutradhar, ICES
Title: Examining the uptake of the Edmonton Symptom Screening Assessment among cancer patients in Ontario
Abstract: There is huge potential in mining population-level health data to derive and evaluate insights on health care delivery and outcomes. Linking data from different provincial databases allows us to detect if there are any trends or patterns between an outcome of interest and certain characteristics. In 2007, Ontario’s regional cancer centers began using ESAS (Edmonton Symptom Screening Assessment) to better understand the conditions of cancer patients at palliative centers. In this study, we seek to understand longitudinally and evaluate the factors associated with ESAS uptake among cancer patients seen at centres where ESAS are provided. Better understanding on these associations can suggest which groups of individuals we should target to encourage ESAS uptake. We found ESAS uptake to be significantly associated with cancer type, income quintile among other factors. Chart audit data pertaining to some ESAS surveys collected in 2017 was also visualized to see which symptoms are common and to see if health care providers provided intervention. In this talk, I will talk about the analytical framework and methodology to understand associations between ESAS uptake among cancer patients and characteristics of interest in Ontario. If time permits, I would also briefly introduce the use of multistate models to analyze cervical cancer screening patterns.
Dongyang (Dawn) Yang
Mentor: Dr. Wei Xu, UHN/PMH
Title: The Effect of Two BRM Promoter Polymorphisms on Survival in Head and Neck Squamous Cell Carcinomas
Abstract: The SWI/SNF chromatin remodeling complex is an important regulator of gene expression that has been linked to cancer development. The identification of cancer biomarkers can improve understanding of tumor biology and pave the way for targeted prevention, screening and therapy. Two identified insertion polymorphisms in the BRM promoter (BRM-741 and BRM-1321) could serve as biomarkers to identify individuals who are at increased risk of cancer and may benefit from future targeted interventions. Targeting the head and neck squamous cell carcinoma (HNSCC) patients, it was shown that loss of BRM expression is found in HNSCC at a similar proportion as other solid cancer types and that two BRM promoter polymorphisms are potential susceptibility markers of HNSCC. In this talk, I will present a study to examine BRM expression’s effect on survival of HNSCC. Survival analysis combined with genetic models are used.
Mentor: Dr. Bingshu Chen, CCTG
Title: Biomarker Analysis with Cancer Clinical Trials Data
Abstract: Biomarkers can be used to establish cancer diagnosis, indicate cancer prognosis and predict cancer treatment responses. A prognostic biomarker relates to the natural history of a disease, indicating the likely course of the disease in an untreated individual. For example, a prognostic biomarker identifies patients who will relapse and experience recurrence of their cancer disease regardless the treatment they received. A predictive biomarker is defined as an indicator to identify sub-populations of patients who are most likely to respond to a certain treatment. It is natural to regard a biomarker as a likely mediator of treatment effect, or sometimes a surrogate marker for disease progression. Therefore, in clinical trials it is important to study on how treatment would affect biomarkers related with the disease process of interest, which can shed light on the mechanism of treatment action. I will talk about the statistical methods (non-parametric and parametric) utilized in correlative studies in MA.32 (A phase III Randomized Trial of Metformin versus Placebo on Recurrence and Survival in Early Stage Breast Cancer) with this principal and present some corresponding results.
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
1. Register your name and email:
a) Go to https://cc.callinfo.com/r/1lpmi4h08lgmc&eom (not currently compatible with Microsoft Edge);
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: Phone lines will be muted during each student presentation, opened only for 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.
April 24, 2018M
Speaker: Kelvin Chan, University of Toronto, Sunnybrook Research Institute
Presentation topic: Development/application of innovative Bayesian methods for cancer clinical trials
Past Seminar Series
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
- Multiple outputation for longitudinal data subject to irregular observation[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.