A Multi-centre, Prospective, Observational Study Evaluating Whether Integration of Prognostic and Predictive Algorithms Into Routine Clinical Practice Effect Whether Oncologists Order Multigene Assays in Patients With Early Stage Breast Cancer
Conditions: Breast Cancer
Sponsor: Ottawa Hospital Research Institute

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A multi-centre, prospective, observational study evaluating whether 
integration of prognostic and predictive algorithms into routine clinical 
practice effect whether oncologists order multigene assays in patients with 
early stage breast cancer. (REaCT-Algorithm)  
Sponsor: 
The Ottawa Hospital Research Institute 
725 Parkdale Avenue 
Ottawa, ON, K1Y 4E9 
Coordinating Centre: The Ottawa Hospital Cancer Centre 
Qualified Co-Investigators: 
Dr. Mark Clemons MD, FRCPC (Medical Oncology) 
Division of Medical Oncology, Dept of Medicine 
The Ottawa Hospital Cancer Centre 
E-mail: mclemons@toh.ca
Dr. Arif Ali Awan, MDCM, FRCPC (Medical Oncology) 
Division of Medical Oncology, Dept of Medicine 
The Ottawa Hospital Cancer Centre 
E-mail: aawan@toh.ca
Co-investigators:  
John Hilton 
Demetrios Simos 
Mohammed FK Ibrahim 
Caroline Hamm 
Nadia Califaretti 
Andrew Robinson 
Angel Arnaout 
Susan Robertson  
Brian Hutton 
Dean Fergusson, 
Greg Pond 

 
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Kednapa Thavorn 
 
Funding source: OICR Health Services Research Network grant P.HSR.159 
 
 
Clinical Trial Registration: NCT 04131933 
 
Protocol Date:  14 January 2020 
 
 

 
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PROTOCOL SIGNATURE PAGE 
 
My signature below confirms that I have reviewed and approved this 
protocol and agree that it contains all necessary details for carrying out the 
study as described. I will conduct this protocol as outlined therein, and 
according to Good Clinical Practice and all applicable local regulations 
 
 
 
 
 
 
 
 
 
 
 
 
Qualified Investigator (Please Print) 
 
 
 
 
 
 
 
 
 
 
 
Qualified Investigator Signature 
 
 
 
 
 
 
 
 
 
 
 
Date 
 
 

 
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Table of Contents 
1.0 
SUMMARY .................................................................................................. 5 
2.0 
INTRODUCTION......................................................................................... 6 
3.0 
METHODS ................................................................................................ 12 
3.1 
Study Design ......................................................................................... 12 
3.2 
Study Population and Eligibility .............................................................. 12 
3.3 
Study Procedures .................................................................................. 13 
3.4 
Study Schedule ...................................................................................... 15 
3.5 
Physician Participation ........................................................................... 19 
3.6 
Study Objectives .................................................................................... 19 
3.7 
Study Outcomes .................................................................................... 20 
3.8 
Data Collection ...................................................................................... 21 
4.0 
STATISTICAL ANALYSIS ......................................................................... 22 
5.0 
SAMPLE SIZE .......................................................................................... 24 
6.0 
STUDY TIMELINE .................................................................................... 25 
7.0 
REFERENCES ......................................................................................... 27 
 
 
 

 
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1.0 SUMMARY 
A broad range of prognostic and predictive tools are available for patients with 
newly diagnosed early stage hormone receptor positive, Her2 negative breast 
cancer. These range from free and publicly available mathematical algorithms 
(e.g. NHS Predict, Magee formulae, Gage and Tennessee equations) that 
incorporate standard pathology results, through to expensive genomic tests (e.g. 
Oncotype DX ® and Endopredict ®). It is not known how the use of these 
different scores affects physician decision making with respect to ordering 
genomic tests, nor how well these algorithms predict for the results of Oncotype 
DX ® in the real-world setting. This pragmatic study will help to answer these 
questions. 
 
 

 
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2.0 INTRODUCTION 
A broad range of prognostic and predictive tools have been developed for 
patients with newly diagnosed early stage breast cancer [1-4]. These include free 
and publicly available algorithms (e.g. NHS Predict 2.1 [5], Adjuvant online! [6-8], 
the Magee equations (1,2,3) [9, 10], Gage [11] and University of Tennessee 
probability models [12]), IHC4 [13] that incorporate standard readily available 
pathology results to genomic tests such as Oncotype DX ®, MammaPrint ®, 
EndoPredict ®, Breast Cancer Index ®.  
 
Oncotype DX ® 
In recent years there has been widespread introduction of multigene genomic 
assays and immunohistochemistry (IHC)-based scores to improve clinical 
decision making for patients with early stage hormone receptor positive, Her2 
negative breast cancer [14-18]. The Oncotype DX ® Recurrence Score (ODX; 
Genomic Health, Redwood City, CA) is a widely used prognostic and predictive 
tool to guide treatment in patients with node-negative, estrogen receptor (ER) 
positive and/or progesterone receptor (PR) positive disease. Among genomic 
tests, Oncotype DX ® and MammaPrint ® are prospectively validated at present 
time [19, 20]. While Oncotype DX ® use has been shown to reduce the overall 
use of adjuvant chemotherapy, its use presents challenges which include: its 
expense (C$4380) restricting its use to resource rich countries significantly 
affects its potential for use in developing countries, in addition, as the test 
requires a tissue specimen to be sent to a central laboratory thereby increasing 

 
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local pathology service demands, it leads to a delay in making treatment 
decisions. Oncotype Dx use also results in higher odds of chemotherapy use in 
patients with small node-negative cancers, lower odds of use of chemotherapy in 
node-positive or large node-negative disease, and an increase in the proportion 
of test requests in the traditional intermediate risk group (i.e. Oncotype DX ® 
Recurrence Score (RS) of 18-30) the group for which until recently there were no 
definitive answers with respect to the benefits of chemotherapy [16, 20]. 
  
While the results of the TAILORx trial showed that overall patients in the 
intermediate risk group derived no additional benefit from chemotherapy, the 
subsequent subgroup analyses have added further confusion for patients and 
health care providers [20]. In this trial, patients in the intermediate risk group 
(adjusted to 11-25 for the trial design) were randomised to endocrine therapy 
with or without chemotherapy. This trial included 9719 patients aged 18-75 with 
1.1-5 cm primary tumours, or 0.4-1.00 cm tumors with unfavourable features. Of 
the randomised patients 62% had 1.1-2 cm cancers, 80% had low-intermediate 
grade tumours and 68% were considered to have clinically low risk disease [6-8] 
The results showed that postmenopausal (defined as those >50 years of age) 
derived no benefit from adding adjuvant chemotherapy to endocrine therapy with 
Oncotype DX ® RS of 11-25. While in patients aged 50 and under there 
appeared to be some benefit for chemotherapy if they had “high”-intermediate 
Oncotype DX ® RS of 21-25. The final recommendations were that for patients 
older than 50 years with Oncotype DX ® RS of 0-25, there is no benefit of 

 
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adjuvant chemotherapy,while for those aged 50 years or younger  Oncotype DX 
® RS of 21-25 there was a 6.3% benefit in invasive disease-free survival at 9 
years from addition of adjuvant chemotherapy. Furthermore, Oncotype DX ® 
does not routinely incorporate classic histo-pathological data and could predict 
the same benefit from chemotherapy for a patient with 0.6 cm and 5.0 cm breast 
cancer while the benefit of chemotherapy is substantially more for a patient 5.0 
cm breast cancer. This is reflected in a recent analysis of the TAILORX data 
which incorporates clinical risk demonstrating that clinical risk is prognostic and 
patients with lower clinical risk had a lower magnitude of benefit from treatment 
[21]. 
 
While the TAILORx results have received significant media attention, we believe 
there are important unanswered questions and potential limitations concerning 
the widespread adoption of Oncotype DX ® . Many of these were raised by the 
study discussant at the 2018 ASCO meeting [22]. These included: 
1. The study failed to evaluate which patients do not need the Oncotype DX 
® test performed.  
2. Decision points for patients with newly diagnosed breast cancer are 
performed at baseline (addressed by TAILORx) and the duration of 
endocrine therapy is not addressed [23].  
3. As the study enrolled patients at low clinical risk of recurrence, we do not 
know the optimal management of patients with stage II-III disease.  
 

 
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In addition, there is considerable variation in the ordering of Oncotype between 
physicians and between cancer centres in Ontario. A recent survey showed that 
some centres order Oncotype Dx in over 90% of patients, while others order it in 
around 15% of eligible patients. As use of this test costs around $9,000,000 a 
year in Ontario alone (and likely to rise with the recent TAILORx results) this 
equipoise has considerable impact on both patient care and financial impact on 
the health care system. There needs to be a more concerted effect to 
improve both patient care, as well as the spiralling costs of this test, by 
prospectively identifying those patients most likely to benefit from the 
performance of Oncotype DX ® testing. 
 
PREDICT 2.1  
PREDICT breast cancer prognostication and treatment prediction model is an 
online tool available at https://breast.predict.nhs.uk/tool. It was originally 
developed in 2010 using data from the patients in the United Kingdom and then 
validated from independent case series in British Columbia, Malaysia, 
Netherlands and two additional cohorts from United Kingdom [24] consisting of 
11,272 patients. It uses readily available features of patients with breast cancer 
such as age, menopause status, tumor size, lymph node involvement, grade, 
estrogen receptor status, Her2 status, Ki67 (if available), tumor detection method 
to instantly predict patient’s overall survival at 5, 10 at 15 years to provide 
prognosis. In additional, it uses the results of high-quality meta-analysis from 
Early Breast Cancer Trialists Collaborative Group to provide the estimated 

 
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overall survival benefit from different modalities of breast cancer treatment such 
as endocrine therapy and second/third generation adjuvant chemotherapy. It is 
the only breast cancer prognostic model currently available online and has been 
used over 1 million times. Although the recent version of the PREDICT model 
better predicts mortality for younger patients with breast cancer, it also has 
limitations. PREDICT was originally developed based on data from patients 
between 1999-2003 and since then multiple advances such as the use of 
trastuzumab and third generation adjuvant chemotherapy regimens which 
include taxane have improved outcomes. The model has since been updated to 
reflect these advances. In addition, PREDICT uses classic histo-pathological 
features to determine prognosis and data from meta-analysis to determine 
benefit based on population outcomes rather than genomic assays which assess 
individual genomic alterations in a patient’s tumor. Furthermore, both these 
models do not incorporate patient’s co-morbidities especially in an aging 
population with other chronic diseases. 
 
 
In the current prospective trial, we will evaluate whether use of PREDICT 
2.1impacts  physician decision making with respect to ordering Oncotype DX ®, 
particularly in patients aged <50, and other important cancer care metrics 
including chemotherapy use, time to commencement of chemotherapy, and 
resource utilisation. This data will also be used to provide further validation of the 
algorithms in a real-world, multi-centre setting.  
 

 
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Alternative Trial Designs 
Any study that is poised to influence physician decision making 
processes could have multiple alternative designs. The study group has 
experience in performing trials evaluating the use of different risk models in 
subsequent Oncotype DX ® ordering [25]. Designs could include a simple 
randomised trial of Predict plus or minus Oncotype DX ®. However, this would 
not account for confounding due to physician standard practice as it would not be 
possible to prevent physicians who always use PREDICT 2.1 (or Oncotype DX 
®) from doing so. Alternatively, a cluster randomized trial could be performed, 
however, the number of different clusters (i.e. treatment centres) required for 
such a trial make it prohibitive. Simply put, this is a complicated question to 
address and any design will not be perfect. However, we feel the current 
proposal has the greatest chance to broadly impact on patient care, physician 
practice and health care economics. 
 
Ethical Considerations 
The ethics of this type of study have been considered at some length by the 
study team and the methodologists. The previously published Magee study (31) 
had a very similar study design and evaluated the effect of another mathematic 
model on physician decision making regarding Oncotype DX ®  ordering. 
Requesting consent from either patients or physicians will add inherent bias to 
the study as the findings will then not reflect broader clinical practice. As both 

 
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PREDICT 2.1 and Oncotype DX ® are available for all eligible patients this bias 
could significantly impact the results and must be avoided. 
 
3.0 METHODS 
3.1 Study Design 
A multi-center prospective cohort study, with a pre- versus post-comparison, 
evaluating how making PREDICT 2.1 results available to treating physicians 
impacts on subsequent ordering of Oncotype DX ®, chemotherapy use, time to 
commencement of chemotherapy, and health care resource utilization. In 
addition, in order to understand physician reasoning and comfort with making 
systemic therapy decisions with either PREDICT 2.1, Oncotype DX ® or both, a 
physician questionnaire will be performed in order to drive subsequent 
knowledge mobilisation strategies. 
The study will involve participation from 6-8 Ontario sites that will be chosen to 
reflect a diverse patient population in Ontario and the site’s involvement in the 
REaCT program. 
 
3.2 Study Population and Eligibility 
Patients that are eligible for Oncotype DX ® testing under current funding 
parameters with complete pathology reports for newly diagnosed early stage 
breast cancer will be eligible for this prospective trial. Patients that are ineligible 
for OncoType DX ® testing but insist on testing and choose to self-pay, are not 
eligible for this study. 

 
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Inclusion criteria: 
• histologically confirmed primary breast cancer 
• no prior chemotherapy 
• eligible for Oncotype DX ® testing as per current Ontario funding criteria 
including: ER positive, PR positive or negative, HER2 negative, lymph 
node status negative or micro-invasive disease, tumour >1 cm in size (or 
if ≤1cm, must be grade 2/3 or have lymph node micrometastasis). 
Exclusion criteria: 
• neoadjuvant treatment including window of opportunity trials 
• recurrent breast cancer 
 
3.3 
Study Procedures 
Each site will have a different process for determining eligibility.  Research staff 
at each site will be confirming Oncotype DX ® eligibility from pathology reports 
and will be working with New Patient booking staff (or similar) to get weekly/daily 
clinic lists of new patients whose tumors meet eligibly for Oncotype DX ® testing. 
 
Patient tumours at each site will enter study screening when a complete 
pathology report is received.  Once a patient tumour is deemed eligible, it is 
given a Study ID number. Pathology data is collected by study staff.  At all points 
of the study, patients with eligible tumours will meet with their treating physician 
to discuss the individualised testing and treatment plan that is best for them.  

 
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Once the physician meets with the patient, we will also collect the chemotherapy 
treatment plan details.  
 
After 3 months of activation, physicians will start completing a questionnaire for 
each new patient with an eligible tumour.  Delaying the questionnaires for the first 
3 months is to prevent bias. 
After 6 months of activation, the physicians will receive a teaching session 
(Intervention) consisting of PowerPoint slides which will present an updated 
review on the benefits and limitations of Oncotype DX and PREDICT testing.  
 
After this teaching session, study staff at each site will be using the pathology 
data to run the PREDICT tool for each patient and will provide the results to each 
physician prior to his/her meeting with each patient.  Again after 3 months, we 
will re-introduce the physician questionnaire for the last 3 months of the study. 
In summary: 
Month 1 to 3:  pathology and chemo data collected, NO physician questionnaire 
Month 4 to 6:  pathology and chemo data collected, PLUS physician 
questionnaire 
Intervention teaching after 6 months of study activation 
Month 7 to 9:  pathology and chemo data collected, PREDICT 2.1 tool used, NO 
physician questionnaire 
Month 10 to 12:  pathology and chemo data collected, PREDICT 2.1 tool used, 
PLUS physician questionnaire 

 
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3.4 Study Schedule 
 
Study phase 
Patient activities 
Physician activities 
Pre-intervention 
Baseline to month 
3 
 
 
 
 
 
 
 
 
 
 
Data Collection:  there’s no 
direct patient involvement. The 
CRA will collect de-identified 
information from patient’s 
health record regarding:  
• Eligibility for Oncotype DX ® 
(ODX) reimbursement by 
OHIP (histologically 
confirmed primary breast 
cancer, no prior 
chemotherapy, ER positive, 
PR positive or negative, 
HER2 negative, lymph node 
status negative or micro-
invasive disease, tumour >1 
cm in size (or if ≤1cm, must 
be grade 2/3 or have lymph 
node micrometastasis)) 
• Whether ODX was ordered 
and if so, the ODX 
recurrence score 
• Data required for Predict 
(age, menopause, 
ER/Her2/Ki67 status, tumor 
size, tumor grade, method 
of detection, number of 
positive lymph nodes) 
• Which chemotherapy 
regimen was planned and 
what chemotherapy the 
patient received 
• Time from medical 
oncologist visit to starting 
chemotherapy 
 
Month 4 to 6 
 
Questionnaire and Data 
Collection: Starting at month 
4, once a physician has 
decided on the systemic 
therapy recommendation for 
each patient, the physicians 
will be asked to complete a 
questionnaire to assess their 
comfort with this 
recommendation. The 
following will be recorded: 
• Whether ODX was ordered 
• Whether Predict 2.1 was 
used 
 
Intervention 
At 6 months 
No patient intervention 
Physicians will be provided 
with educational material 
regarding the strengths and 
weakness of OncoTypeDX 
testing and the Predict 2.1 tool 
Post-
intervention 
Months 6 to 9 
 
 
 
Data Collection: exactly as the 
pre-intervention phase with no 
direct patient intervention.  The 
CRA will collect de-identified 
information from patient’s 
health record regarding:  
Predict 2.1: CRA will use the 
Predict 2.1 tool for all patients 
that are potentially eligible for 
Oncotype DX testing. This 
result will be provided to the 
treating medical oncologist in 
the clinic. 
 

 
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Month 10 to 12 
• Eligibility for Oncotype DX ® 
(ODX) reimbursement by 
OHIP (histologically 
confirmed primary breast 
cancer, no prior 
chemotherapy, ER positive, 
PR positive or negative, 
HER2 negative, lymph node 
status negative or micro-
invasive disease, tumour >1 
cm in size (or if ≤1cm, must 
be grade 2/3 or have lymph 
node micrometastasis)) 
• Whether ODX was ordered 
and if so, the ODX 
recurrence score 
• Data required for Predict 
(age, menopause, 
ER/Her2/Ki67 status, tumor 
size, tumor grade, method 
of detection, number of 
positive lymph nodes) 
• Which chemotherapy 
regimen was planned and 
what chemotherapy the 
patient received 
• Time from medical 
oncologist visit to starting 
chemotherapy 
Predict 2.1: CRA will use the 
Predict 2.1 tool for all patients 
that are potentially eligible for 
Oncotype DX testing. This 
result will be provided to the 
treating medical oncologist in 
the clinic. 
Questionnaire and Data 
Collection: Starting at month 
10, once a physician has 
decided on the systemic 
therapy recommendation for 
each patient, the physicians 
will be asked to complete a 
questionnaire to assess their 
comfort this recommendation. 
The following will be recorded: 
• Whether ODX was ordered 
• Whether Predict 2.1 was 
used 
 
 
Pre-intervention: 
Baseline to Month 6: To obtain information on baseline use and physician 
practice pattern across multiple cancer centres, for the first 6 months of the 
study, the study CRA will use the patients’ electronic medical record (EMR) to 
collect de-identified data for each new patient who is eligible for Oncotype DX 
testing, and whether or not the treating physician used Oncotype DX ® or Predict 
2.1. During this 6-month period, the CRA will NOT provide the oncologist with 
PREDICT 2.1 results. If the physician wishes to use the PREDICT 2.1 tool on 
their patients, they will be allowed to do so. The CRA will record chemotherapy 

 
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treatment information and the time between seeing the medical oncologist and 
starting chemotherapy.  
Months 4 to 6:  Once a physician has made a decision about a patient’s 
treatment, the physician will complete a questionnaire [25] to assess their 
comfort with their systemic therapy recommendations for each patient.  
Collecting this information from physicians after 3 months of baseline behaviour 
removes potential influence of the questionnaire prompting physicians to 
consider PREDICT 2.1 in their standard decision-making. 
 
Intervention Teaching Session (at 6 months): All medical oncologists involved 
in the study will receive a teaching session to remind physicians of both the 
strengths and weaknesses of Oncotype DX ® and PREDICT 2.1 tools. 
 
Post-Intervention 
Months 6 to 12: In the second 6 months of the study, the CRA will use the 
PREDICT 2.1 tool for all patients that are potentially eligible for Oncotype DX ® 
testing.  The PREDICT model results will be attached to each patient chart and 
therefore available to the treatment oncologist.   
Months 10 to 12: Once a physician has made a decision about a patient’s 
treatment, the physician will complete the questionnaires again [25] to assess 
their comfort with the systemic therapy recommendations for each patient.   
 
 

 
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This study timeline requires that each site will have its own study start date and 
pre-intervention and post-intervention periods.  Throughout the entire study 
timeline, the study CRA at each site will collect de-identified routine diagnostic 
history and pathology information from the patient’s electronic medical record 
and record it in the study database.  
 
Rationale:   
The Pre- and Post-Intervention periods have been chosen to practically allow 
sufficient time for individual centres to accrue patients and to allow sufficient data 
to be available to reflect any effects of the teaching session within the allocated 
study-funding window. 
 
As the study makes no treatment recommendations based on the PREDICT 2.1 
model and the oncologist can use the PREDICT 2.1 tool at any time if they so 
wish, all patients in this study are receiving a standard of care discussion and 
treatment decisions with no additional risk. The study will therefore not involve 
consenting patients or physicians as all treatment decisions will be made by each 
patient and physician as per clinical standard of care.  The waiver of consent 
model has been used in the past in REaCT trials [25], with great success as it 
avoids the significant issue of physician bias. REB application will be through 
OCREB and the trial will be registered on clinicaltrials.gov. 
 
 

 
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3.5 
Physician Participation 
It is our hope that all medical oncologists that see patients with early stage breast 
cancer will participate so that the study captures broad clinical practice at all 
participating sites.  There is no recruitment target for physician participation.   
 
As described above, during months 4-6 and 10-12 of the study, physicians will 
receive ONE questionnaire for EACH patient that would meet the eligibility 
criteria for Oncotype DX ® testing.  There are two versions of the physician’s 
questionnaire (Pre-Intervention at 4-6 months and Post-Intervention at 10-12 
months).  This questionnaire consists of 8-9 questions and should take less than 
5 minutes to complete.  The questionnaire will be identified with the patient Study 
ID but will not record the physician’s name.  There are no risks to the physician 
bin completing the questionnaire.  The study will not collect any data on 
individual physicians’ practice patterns but instead on each centre’s practice as a 
whole.  Completion of the questionnaire constitutes implied consent.  A physician 
can also refuse to complete a questionnaire for a specific patient. 
 
3.6  Study Objectives 
Primary Objective:  
 
To assess whether providing individual patient prognostic and predictive 
scores from PREDICT 2.1 affects the rate of subsequent requests for Oncotype 
DX testing. 
   

 
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 Secondary Objectives: 
To assess whether routine availability of PREDICT 2.1 affects: the frequency of 
chemotherapy use, type of chemotherapy use, the time to starting chemotherapy, 
endocrine therapy or radiation therapy and Oncotype DX ® cost and total health 
system costs and subsequent health care utilization. In order to use these study 
findings for knowledge mobilisation strategies, we need to understand 
physician’s reasoning and comfort with making systemic therapy decisions. 
Physicians will therefore complete a questionnaire [25] after seeing each patient. 
 
3.7 Study Outcomes 
Primary Endpoint: The primary outcome will be the proportion of patients for 
which Oncotype DX ® is ordered, defined as the number of patients with 
Oncotype DX ® orders divided by the number of patients eligible for Oncotype 
DX ® testing. 
 
Secondary Endpoints:  
Frequency and type of chemotherapy used will be collected from the patient’s 
electronic medical record as will timing to start chemotherapy, radiation therapy 
and endocrine therapy.  Prognostic risk scores, including Magee formulae, Gage 
and Tennessee equations will be calculated using patient and tumour 
characteristics. These scores will also be compared with Oncotype DX ®  results 
when performed. Physician questionnaire results will be used to assess 
physician comfort when making systemic therapy decisions. 
 

 
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Economic analysis:  
We will conduct a cost analysis and compare total health system costs before 
and after the model implementation from the Ontario government’s perspective. 
The cost components will include Oncotype DX cost (C$4380), adjuvant 
chemotherapy cost and other health services costs. We assum that the 
PREDICT 2.1 tool is readily available at no cost. The cost associated with other 
health services among patients who receive and those who do not receive 
adjuvant chemotherapy (including cost of cancer clinic, in-patient services, 
physician billing, chemotherapy, endocrine therapy, supportive drug, home care, 
in-patient and emergency department costs) [26] will be obtained from a 
concurrent cohort study and Canadian sources. Based on this analysis, we will 
then extrapolate the cost findings to the entire population of Ontario to report the 
financial implications of our intervention to Cancer Care Ontario. 
 
3.8 Data Collection 
Data collection begins when a pathology report is completed and deemed eligible 
for Oncotype testing under the Ontario criteria. The following information will be 
collected from the patient’s electronic medical record: 
• Eligibility for Oncotype DX ® (ODX) reimbursement by OHIP (histologically 
confirmed primary breast cancer, no prior chemotherapy, ER positive, PR 
positive or negative, HER2 negative, lymph node status negative or micro-
invasive disease, tumour >1 cm in size (or if ≤1cm, must be grade 2/3 or have 
lymph node micrometastasis)) 

 
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• Whether ODX was ordered and if so, the ODX recurrence score 
• Data required for PREDICT 2.1 algorithm (age, menopause, ER/Her2/Ki67 
status, tumor size, tumor grade, method of detection, number of positive 
lymph nodes) 
• Which chemotherapy regimen was planned and what chemotherapy the 
patient received 
• Time from medical oncologist visit to starting chemotherapy 
Data collected will be de-identified (no patient identifiers) and will not be linked to 
the treating physician.  Data will be entered by the CRA at each site into a secure 
study-specific database. 
 
 
4.0 STATISTICAL ANALYSIS 
The primary analysis will be based on a hierarchical logistic regression 
analysis, with time period (pre-intervention versus post-intervention) as the 
primary covariate of interest. For this model, patient characteristics are 
considered nested within treating physician, which is nested within treatment 
centre. Adjustments will be performed for baseline characteristics. From our 
survey, it was evident that there was significant variability between centres (and 
between physicians) for ordering Oncotype DX. Data from Ottawa shows that 
approximately 15% of approximately 600 patients treated by 8 physicians have 
this test performed. In contrast, Kingston (5 physicians) sees around 200 eligible 
patients a year and Oncotype DX is ordered for around 65% of these patients, 

 
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Thunder Bay (3 physicians) sees around 70 patients a year and also tests 
around 65% of eligible patients, and Newmarket (3 physicians) sees around 130 
eligible patients a year and Oncotype DX is ordered for around 75% of these 
patients.  Windsor (3 physicians) and Kitchener (4 physicians) both see around 
150 patients per year and orders Oncotype DX testing on 90% of eligible 
patients. Given concerns that use of hierarchical clusters may not sufficiently 
account for the substantial variability between sites, analyses will also be 
conducted by site, grouped by baseline rate of testing (i.e. Kingston, Thunder 
Bay and Newmarket will be combined, as will Windsor and Kitchener; Ottawa will 
be a separate group on its own). A similar analysis will be performed using 
hierarchical logistic regression. In addition, key supportive analyses will also be 
performed using a two-sample chi-square test within each site separately, 
comparing the change in proportion of patients for which Oncotype DX is ordered 
for each physician (i.e. post-intervention proportion – pre-intervention proportion 
for physician A, B, C, …). 
 
Analyses of secondary endpoints will be performed globally across all sites 
combined, and for each site individually. The chi-square test or Wilcoxon rank 
sum test will be used as appropriate for categorical or continuous endpoints 
respectively. Results will be presented using tables and figures, and exact 
confidence intervals will be constructed for outcomes of interest. Bootstrapping 
may be used for estimation purposes if required. All tests will be two-sided and 
statistical significance will be defined at the α=0.05 level.  

 
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No interpolation will occur for missing data except in situations where the missing 
data is obvious (i.e. if a patient has missing Her2 status but receives 
trastuzumab, it can be assumed with confidence that the patient must be Her2 
positive). No statistical adjustments will be made for multiple tests performed, 
however, appropriate caution will be made in interpretation of results, particularly 
for the analysis of secondary endpoints. 
 
  
5.0 SAMPLE SIZE 
Sample size calculations for hierarchical regression models are difficult, as they 
require estimates of the number of clusters, the size of each cluster, the effect of 
intervention on each cluster separately and intra-cluster correlation. Without prior 
information, these estimates are unlikely to be accurate. Hence, statistical power 
and sample size calculations will be based on practical considerations.  
 
It is hypothesized that if the use of PREDICT 2.1 decreases the odds of 
Oncotype DX ordering by approximately 50% or more, the usage of Predict 2.1 
will be of clinical importance. It is known that approximately 300 patients are 
seen in a given six-month period in Ottawa, and approximately 15% of eligible 
patients undergo Oncotype DX testing. For the other centres, the values are 75 
and 75% (Newmarket), 75 and 90% (Windsor), 75 and 65% (Kingston), 35 and 
65% (Thunder Bay), and 75 and 90% (Grand River). Based on these estimates, 

 
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a simple, two-sided, α=0.05, logistic regression model would have 80% power to 
detect an odds ratio of 0.46 (from 15% to 7.46%) in Ottawa, an odds ratio of 0.38 
(from 90% to 77.4%) in Grand River/Windsor, and an odds ratio of 0.53 (from 
70% to 55.4%) in Newmarket/Kingston/Thunder Bay. Overall, if one 
conservatively estimates at least 500 patients will be eligible pre-intervention and 
500 patients will be eligible post-intervention, a simple logistic regression 
analysis would have over 80% power (2-sided, α=0.05) to detect an odds ratio of 
0.67, assuming that approximately 50% of patients undergo Oncotype DX testing 
in the pre-intervention period. Combined, these estimates give assurance that 
the sample size for this study is sufficient to capture clinically meaningful effects. 
 
 
6.0 STUDY TIMELINE 
Funding to begin: Sept 1, 2019 
Application to OCREB Sept 2019 
REB approval Nov 2019 
Site initiation visits Dec 2019-Feb 2020 
Completion of initial 6-months of study May 2020 
Educational intervention June 2020 
Completion of second 6-months of study December 2020 
Data analysis Jan-Feb 2021 
Funding term end date: March 31, 2021  
 
 
 

 
14 January 2020 
26 
 
Table 1. Characteristics of study algorithms  
Algorithm 
name 
Required 
clinic-path 
data 
Formula Population 
– LN pos 
and neg? 
What it 
tells you 
NHS 
Predict 2.1 
Age 
Screen 
detected or not 
Tumour size 
LN 
involvement 
Grade 
ER 
Her2 
Ki67* 
On line 
Node pos 
Node neg 
10 yr 
distance 
disease 
free survival 
Magee 1 
Tumour size 
Grade,  
ER 
PR 
Her2 
Ki-67  
On line 
Macro-node 
negative 
Surrogate 
Oncotype 
score 
Magee 2 
Tumour size, 
grade, ER, PR, 
Her2 without 
Ki-67 
On line 
Macro-node 
negative 
Surrogate 
Oncotype 
score 
Magee 3 
ER, PR, HER2 
Ki-67  
On line 
Macro-node 
negative 
Surrogate 
Oncotype 
score 
Gage et al 
(2015) 
ER 
PR 
Grade 
simple 
algorithm 
based on 
tumor grade, 
PR >1% and 
ER>20%. 
 
 
 
 
University 
of 
Tennessee 
predictive 
algorithm 
PR 
Grade 
 
 
 
Surrogate 
Oncotype 
score 
 
 
 
 

 
14 January 2020 
27 
 
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