Describing the Comparative Effectiveness of Colorectal Cancer Screening Tests: The Impact of Quantitative Information
Phase: NA
Conditions: Colorectal Cancer Screening
Sponsor: Indiana University

 
 
 
Information With or Without Numbers For Optimizing 
Reasoning About Medical Decisions (INFORM) 
 
 
 
 
 
Principal Investigator: Peter H. Schwartz, MD, PhD 
Funded by the Patient-Centered Outcomes Research Institute (PCORI) CDR-1403-11040 
PCORI Project title: Describing the Comparative Effectiveness of Colorectal Cancer Screening 
Tests: The Impact of Quantitative Information 
Clinical Trial title: Information With or Without Numbers For Optimizing Reasoning 
About Medical Decisions (INFORM) 
NCT#: NCT02477553 
IRB ID: 1502774679 
IRB approval date: 3/31/2015 
 
 
 
 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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Research Plan 
 
A.  BACKGROUND:   
IMPACT OF THE CONDITION ON THE HEALTH OF INDIVIDUALS AND POPULATIONS 
 
Colorectal cancer (CRC) poses a significant burden in the US population, worsened by the low utilization of 
screening tests that have been proven to save lives. Each year in the United States, over 140,000 people are 
diagnosed with CRC and more than 50,000 die from it, making it the second leading cause of death from 
cancer in the US. 1  Much of the morbidity and mortality could be prevented with the appropriate use of 
available, approved, affordable screening tests.  Just 59% of eligible adults are up to date with recommended 
screening, a rate that falls far short of screening for other cancers, such as mammography and pap tests.2 
Increasing uptake of CRC screening to 70% could save 35,000 lives over 5 years.3   
 
One factor that can help improve screening is the availability of multiple approved tests, allowing individuals 
to choose a test that best fits their preferences. While there are some differences among the leading 
guidelines4-6 – all recommend three tests: colonoscopy every 10 years, flexible sigmoidoscopy every 5 years, or 
annual stool testing with high-sensitivity fecal occult blood testing (FOBT) or fecal immunochemical testing 
(FIT).  Although one guideline prefers colonoscopy,5 all three conclude that average-risk patients should be 
offered a choice among the recommended tests. 
 
Colonoscopy is the most sensitive and specific for identifying polyps or cancers, but it carries significant 
burdens, risks, and expense. Colonoscopy is an invasive procedure involving a one- or two-day preparation to 
clean out the colon, IV sedation, and the need to take a day for the procedure and arrange a ride home.  There 
are expenses, including possible charges for pathological analysis of any removed polyps.  Finally, there are 
significant risks, including hemorrhage and perforation.4   
 
Stool occult blood testing is the second most commonly utilized approach. Of the two recommended types, 
we concentrate here on FIT since it is widely used and available in the clinics where the study will be 
conducted.  Stool blood testing has the advantage of being carried out by the patient in his or her home, is low 
cost, and requires no preparation.  The main limitations are that it must be done annually, requires the patient 
to handle his or her own stool, and any positive test requires evaluation with colonoscopy.  A major perceived 
drawback of stool testing, perhaps especially by primary care physicians rather than patients, is that it 
provides inadequate or inferior screening, since a stool test may fail to identify a polyp or cancer.   
 
Despite its lower sensitivity, though, annual FIT with colonoscopy for any positive test produces a lifetime 
reduction in mortality from CRC that approaches, equals, or even exceeds that produced by colonoscopy, 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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according to several comparative effectiveness analyses.7,8  The reasons for this include:  (1) repeated 
application of FIT increases sensitivity, (2) only a percentage of polyps that are not identified will progress to 
cancer, (3) other sources of bleeding in the colon may lead to a positive FIT and thus trigger a colonoscopy 
where a non-bleeding polyp or cancer is identified; (4) colonoscopy is not a perfect test:  polyps may be 
missed or may develop into cancers between screenings.   
 
This point bears repeating:  Although colonoscopy is the best test for identifying polyps or early cancers on a 
single application, it is not known if this translates into significantly superior reduction in morbidity and 
mortality for all patients. FOBT and FIT are approved in every major guideline because they are good screening 
tests.  Patients who wish to avoid the burdens and risks of colonoscopy may reasonably choose stool blood 
testing, as acknowledged by all the published guidelines, and many patients do appear to prefer them.9-15 
 
Studies also show that when primary care providers discuss CRC screening, they often fail to describe 
alternatives to colonoscopy.16-18  This raises concern that failure to inform patients about alternatives to 
colonoscopy may lead patients to choices that do not truly match their preferences and may result in their 
failing to be screened. In a recent study, an outreach program that recommended just colonoscopy for 
screening resulted in a 38% uptake, while recommending a stool test or offering a choice between 
colonoscopy and stool test resulted in a 67% or 69% uptake, respectively.19  
 
Multiple national organizations recommend that providers do a better job of describing and explaining the 
alternatives to colonoscopy:  as one motto puts it, “The best test is the on that gets done.”20  An NIH 
consensus panel concluded that a major step in improving CRC screening is to ensure that all patients are 
informed about the full range of tests.21 A nationwide initiative labeled “80% by 2018” (to achieve screening 
uptake of 80% by 2018) led by the National Colorectal Cancer Roundtable and joined by organizations such as 
the American Cancer Society, the American College of Gastroenterology, and others, embraces the goal of 
making all tests available.20  A factor that motivates providers and healthcare systems to offer alternatives to 
colonoscopy is that quality measures count patients as screened if they undergo any approved test. 
 
For all these reason, there is now strong pressure to make alternatives to colonoscopy available, and the main 
alternative to colonoscopy is stool testing, rather than sigmoidoscopy.  A recent study found that under 1% of 
screened patients had sigmoidoscopy.22  There are a variety of reasons: sigmoidoscopy is uncomfortable (no IV 
sedation or anesthetic), only examines half the colon, and has not been well reimbursed.23,24  Other tests such 
as CT colonography are approved by some guidelines but not by others, and are not used widely.  The DA that 
in this study will focus on the choice between colonoscopy and FIT. 
 
2.  Cross-cutting benefits:  While this project focuses on CRC, determining how to best provide information 
about comparative effectiveness of screening strategies applies to other cancers as well, such as breast and 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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prostate.  Although mammography is the standard screening test for breast cancer, for instance, patients may 
choose to be screened annually or biannually, with attendant benefits and risks.25 Many experts assert that 
patients should be given information regarding the comparative effectiveness of such strategies.26,27 This 
project can inform risk communication for screening for CRC and other cancers. 
 
B.  SIGNIFICANCE:  POTENTIAL FOR THE STUDY TO IMPROVE HEALTH CARE AND OUTCOMES and PATIENT 
CENTEREDNESS 
 
Opportunity to improve patient-centered care and outcomes:   Informing patients about their options for 
CRC screening and their comparative effectiveness could produce higher quality decisions, improve the match 
between patient preferences and tests performed, and increase uptake of CRC screening.  Decision aids (DAs) 
are a promising tool for accomplishing this goal.  While DAs are not widely used in clinical medicine, a 2014 
Cochrane review identified 115 randomized, controlled trials of DAs.  These studies show that compared to 
usual care, DAs increase patient knowledge, improve patient-provider communication, and increase uptake of 
recommended interventions in many areas.28 Ten randomized trials have tested a total of 7 different DAs 
designed for CRC screening.29-35  
 
Information that is important to patients:  Patients put a high value on comparative effectiveness information 
regarding CRC screening tests, especially the accuracy and sensitivity of tests,12,36,37 and the risk reduction they 
provide,38-40, and this information has a large impact on their decision-making.13-15  This has been shown by 
multiple studies that compared patient interest and response to a wide range of relevant information.12-15,36-
38,40,41  We have also received comments from patients in multiple settings about the importance of 
information about accuracy and risk reduction provided by CRC screening tests. While patients have mixed 
feelings about receiving specific numbers (e.g. as percentages), many want some indication of the tests’ 
comparative ability to protect them from cancer.  
 
Critical gap in current knowledge:  At the same time, little is known about how to optimally provide 
comparative effectiveness information to patients, especially whether such information should be given 
qualitatively (using words) or quantitatively (using numbers, graphs, and figures), and whether the best format 
varies based on patients’ level of interest and ability. Studies showing that individuals value comparative 
effectiveness information did not directly compare the impact or acceptability of alternative ways of 
presenting such information.  For instance, some studies described sensitivity using just words (“colonoscopy 
is better at finding polyps than other tests”),9,10,12,42-44 while others presented numbers.11,14,38,40 
 
Although guidelines for the design and evaluation of DAs recommend presenting comparative effectiveness 
information using detailed quantitative data, this recommendation is not evidence-based.  The International 
Patient Decision Aids Standards (IPDAS), for instance, the most widely applied guidelines for the design and 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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implementation of DAs, recommend that DAs disclose quantitative data regarding absolute baseline risk (no 
screening), risk reduction provided by each test, and chance of false positives and negatives.  IPDAS 
recommends presenting this information using frequencies and graphs, especially “icon charts” or 
“pictograms,” using a variety of frames (e.g. gain vs. loss).26,45,46  This recommendation appears to be based on 
the assumption that such data will help patients make fully informed decisions.  
 
It is not known whether inclusion of such information improves decision-making or screening outcomes.  In 
fact, research has shown that quantitative data may hinder individual decision-making for the following 
reasons: 
• Low numeracy: At least 22% of adults have only the most basic quantitative skills, such as counting, while 
another 33% possess only slightly more advanced math skills, such as simple arithmetic.47,48 Thus, more 
than half of adults cannot comprehend key mathematical concepts used in quantifying risk and benefit 
(probability, percentage, frequency).  Even individuals with moderate numeracy have difficulty 
interpreting and applying information about risk and risk reduction provided by CRC screening tests.49 
• Heuristics and biases: Psychologists, economists, and other researchers have identified numerous aspects 
of human thought process that lead to irrational responses to risk information.50,51  To give just two 
examples:  “Denominator neglect” leads people to exaggerate small dangers, such as a risk that occurs 5 
times per 1,000,000, due to focusing on the numerator.52  “Optimism bias,” leads to underestimating the 
danger of a behavior (e.g. smoking) due to a belief that one will be in the group that is not harmed.53 
• Gist:  According to “dual-process” models of human reasoning such as Fuzzy Trace Theory, people form 
two representations of risk: the first represents the specific probability (in “verbatim memory”), while the 
second reflects a “gist impression” of its importance.52-54  According to such models, the gist impression 
has more power than the verbal memory to influence opinions and actions.54  
For all these reasons, inclusion of quantitative information in DAs may have no effect, or even negative effects 
on patient decision making. Worse, it may reduce screening uptake due to confusion or misunderstanding, a 
true “lose-lose” outcome.   While such a decline in uptake would be acceptable if it was due to patient 
understanding and rational choice, it is a truly unfortunate outcome if it is the result of confusion.   
 
The research necessary to identify the impact of quantitative information, and thus help guide the design of 
future DAs, has not been done, as noted in a number of reviews.   A recent Cochrane review of all randomized, 
controlled trials of Das55 found that just 25 studies examined DAs that presented probabilities.56-73  These 
studies found that patients who viewed DAs with probabilities were more likely to correctly answer questions 
regarding these probabilities than individuals in the control group.  But these studies found no clear benefit to 
presenting probabilities in terms of uptake or other outcomes.  Further, in 24 of the 25 studies of DAs that 
included probabilities, no comparisons were made to DAs that were identical except lacking those 
probabilities. Many studies compared the DA containing quantitative information to a much simpler 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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information packet or website. The single randomized trial that varied the form of quantitative information 
used scripts read to participants (an extremely simple DA) and only included elderly patients.35 
 
Variation in current practice:  Among the 7 DAs for CRC screening tested, there is significant variation 
regarding the type of comparative effectiveness data provided. In addition, studies suggest that presenting 
this data in certain ways reduces uptake and did not improve patient understanding or decision-making.  Four 
of the DAs provided no numbers,29-32 and three provided information regarding the risk reduction or other 
numbers.33-35 The three DAs that increased uptake provided no numbers regarding risk reduction, sensitivity, 
or specificity of the screening tests.30,31,74 And the three DAs that provided at least some quantitative 
information found either no change in uptake,33,35 or lower uptake, compared to controls.34  An editorial 
published with one of these studies34 suggested that the reduction in uptake could have been caused by 
patient misunderstanding of the quantitative information provided: “Although it is important to describe risks 
clearly, the amount of space given to the presentation of risk within DAs might be disproportionate to the 
conceptual facts adults consider when offered screening, and this may over-emphasize the risks associated 
with screening compared with the value of preventing the disease.” 75, p. 949 
 
Preliminary Studies:  Study Design and DAs: Our group conducted a randomized, controlled trial where 108 
patients ages 50-75 years old who were at average risk for CRC and were due for CRC screening were 
randomized to use one of two DAs.  The Control Group used a DA including just the Basic Information module, 
which provides general information about CRC, screening with colonoscopy, sigmoidoscopy, and FIT, including 
comparative effectiveness information presented verbally (no numbers).   
 
The Quantitative group used a DA comprised of the Basic Information module followed by the Quantitative 
module, which included six slides with numbers and icon charts presenting the following values for average 
risk individuals: 
(1) lifetime CRC incidence and CRC mortality (3%, or 30 per 1000), assuming no screening,  
(2) lifetime CRC mortality given regular screening with colonoscopy (0.46%, or 4.6 per 1000) or with FIT 
(0.6%, or 6 per 1000), according to the SimCRC model, 
(3) frequency of an individual FIT turning positive (50 per 1000) and of serious complications from 
colonoscopy (1-2 per 1000). 
Finally, the Quantitative module presented a bar chart displaying average lifetime CRC mortality with no 
screening, colonoscopy, and FIT. 
 
Participants answered a questionnaire before and after viewing the intervention (T0 and T1), regarding 
qualitative and quantitative knowledge, subjective risk of CRC, intent to be screened, and decision conflict. 
Participants were contacted after six months (T2) and asked about screening behavior and the electronic 
health record was checked for confirmation.  

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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Results:  Members of both Control (n=52) and Quantitative groups (n=56) showed significant increases from 
T0 to T1 in qualitative and quantitative knowledge (p < .001), perceived CRC risk (p-value = 0.005), intent to be 
screened (p-value < 0.001), intent to undergo FIT (p-value < 0.001), intent to undergo colonoscopy (p-value = 
0.007), and reduction in overall decision conflict score (p-value < 0.001).  However, no significant between-
group differences in change scores on these variables were observed, with one exception: quantitative 
knowledge scores were higher in the quantitative group (p < .01). 
 
When all variables were examined for interaction with numeracy, two were significant:  perceived risk and 
intent to undergo FIT.  Among participants at above-median numeracy, those in the quantitative arm had a 
significantly smaller increase in subjective risk for CRC than those in the control arm (-0.09 vs. 0.81, p = .009), 
and a significantly greater intent to undergo FIT (1.00 vs. 0.1, p = .01).  There were no significant differences in 
these outcomes among participants whose numeracy scores were below the median. 
 
At 6 months, a higher proportion of patients in the Quantitative arm had completed CRC screening compared 
to those in the Control arm; however, this difference was not significant (39.3% vs. 26.9%, p = 0.173). 
Differences between groups in the proportion being screened with FIT or colonoscopy also were not 
significant.  Among patients with above-average numeracy, there was a higher uptake of FIT in the 
Quantitative group, compared to those in the Control group, that approached significance (12.1% vs. 0%, p = 
0.148); no differences between groups were seen in the low numeracy group.  
 
Conclusions:  
(a) Adding quantitative information to a DA had limited impact overall and on patients with below-average 
numeracy from T0 to T1. But over six months there was a trend towards increased uptake overall among 
those shown quantitative information, compared to those shown only non-quantitative information. 
(b) Among patients with above-average numeracy, providing quantitative information produced a significantly 
smaller increase in perceived CRC risk and a larger increase in intent to undergo FIT from T0 to T1.  Also 
among patients with above-average numeracy, at six months there was a non-significant trend towards 
increased uptake of FIT by those who viewed quantitative information, compared to those who did not. 
 
Applying Findings to Influence Design of DAs:  The results of a larger trial could have significant impact on the 
design of DAs in the future. For instance, consider the following three results scenarios and the conclusions 
that could be drawn about the design of DAs in this area:  
a. Finding that quantitative information significantly increases overall uptake for patients with all levels of 
numeracy would support providing such data to all patients.   

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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b. Finding that quantitative information has a significant effect on perception and behavior only for patients 
with above-average numeracy would support providing quantitative data only to those patients, perhaps 
by making it optional.  
c. Finding that the quantitative information does not improve knowledge in significant ways and reduces 
uptake for patients with all levels of numeracy, would suggest that DAs should not present quantitative 
information in this way, even as an option.  
 
These are only three possible types of findings of the project. Of note, the approach supported by results of 
type (b) – allowing patients to choose whether to view quantitative comparative effectiveness information – 
would differ in important ways from current recommendations and design of DAs.  
 
 
C.  STUDY DESIGN AND APPROACH 
 
1.  RANDOMIZED, CONTROLLED TRIAL:  SPECIFIC AIMS 1 AND 2: 
 
Specific Aim #1:  To compare screening intention, screening behavior, and perceptions of patients eligible 
for CRC screening who view a decision aid (DA) that includes quantitative information versus a DA without 
such data.  We will also collect measures of decision quality. 
 
Hypotheses: 
1.1) 
Participants who view the DA with quantitative information will have larger increases in (a) perceived 
risk of CRC and (b) benefits of screening, compared to those who view the DA without such data. 
1.2) 
A larger percentage of participants who view the DA with quantitative information will (a) intend to be 
screened and (b) complete screening at 6 months, compared to those who view the DA without such 
data.  
 
Specific Aim #2:  To determine whether numeracy moderates the effect of quantitative information.   
Hypotheses: 
2.1) 
Among participants with above-average numeracy, those who view the DA with quantitative 
information will have (a) smaller increases in perceived risk of screening and (b) larger increases in 
perceived benefit of FIT, compared to than those who view a DA without such data. 
2.2) 
Among participant with above-average numeracy, a larger percentage of those who view the DA with 
quantitative information will (a) intend to be screened with FIT, and (b) undergo screening with FIT 
within 6 months, compared to those who view the DA without such data. 
 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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Study Design: To achieve these aims, we will conduct a randomized, controlled trial to compare outcomes 
between two groups: patients who view a DA that presents quantitative information regarding comparative 
effectiveness of colonoscopy and FIT (Quantitative group) and those who view the same DA without such 
quantitative information, which describes comparative effectiveness only verbally (Control group).  
 
Choice of Study Design: This study compares two established approaches to describing comparative 
effectiveness of CRC screening tests.  As described above, roughly half of the DAs regarding CRC screening that 
have been tested include quantitative information about risk, risk reduction, and other factors.  No previous 
research has compared two DAs that are identical except that one presents comparative effectiveness 
information in quantitative terms.  
 
Our trial compares the efficacy of these DAs since it only enrolls patients who match certain criteria and agree 
to be enrolled.  We considered the option of enrolling all potentially eligible patients, but rejected this option 
due to the limited outcomes we could measure (i.e. just screening rate); such a study could not assess impact 
on understanding, perception, intention, and decision quality.   
 
Choice of Comparators:  As in the pilot study, those in the Control group will view a basic information module, 
based on the one used in our preliminary studies.  Those in the Quantitative group will view the basic 
information module and a quantitative module, again based on the one used in pilot.   We have already 
modified the basic information module based on input from patients who participated in our preliminary 
study and have now participated in three group discussions carried out after participation. In particular, we 
have increased clarity regarding the procedure and risks of colonoscopy and the fact that lack of a family 
history and lack of symptoms does not imply the absence of polyps or cancer.   
 
We also plan the following changes in the quantitative module, in part to match the recommendations of 
IPDAS and in part based on input from patients who participated in group discussions and serve on the Patient 
Advisory Board.  Using icon charts and numbers (proportions and frequencies), we will state average lifetime 
incidence with and without screening, and probability of false positives and false negatives after colonoscopy 
and FIT. In the first three months of the project, we will make these modifications and conduct cognitive 
interviewing with patients who view the DAs. We will also show the modules to the Patient Advisory Board 
and Community Advisory Board for input, discuss the cognitive interviewing, and make final changes. 
 
Choice of Outcomes:  Screening behavior, a primary outcome for this study, can be assessed reliably, and is 
central to assessment of a DA.  Perceptions of risk of CRC and benefit of screening, and intention to undergo 
screening, is also relevant to evaluation of impact of a DA.  We have chosen to assess Test Choice (see 
Measures, below) to measure concordance between intention and behavior, a measure that has been 
recommended to assess quality of decision-making.76-79  We chose not to assess concordance between patient 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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characteristics and stated preference due to significant methodological and conceptual difficulties, following 
Sepucha (2010) and others.76,80  We will also use the decision conflict scale, which has well validated 
psychometric properties and has been used in many studies of DAs to assess decision quality.55,81 The decision 
conflict scale is limited, though, since it measures patients’ feelings about their decision rather than the 
decision itself.80 
  
Theoretical framework: Our theoretical framework is provided by the Health Belief Model,82 which has been 
previously applied to research on CRC screening behavior.83-85 We expect that, compared to patients who view 
the DA without quantitative information, those who view the DA with quantitative information will have a 
larger increase in perceived risk of CRC and the benefit of screening, resulting in a larger increase in intention 
to undergo screening (hypothesis 1.1), and higher screening rate (hypothesis 1.2). This fits with the Health 
Belief Model’s assumption that action is caused by perception of susceptibility to the illness and benefit from 
the action.82 
 
We also expect that among patients with above-average levels of numeracy, those who view the quantitative 
information will have a smaller increase in perceived risk of CRC, and will have a larger increase in perceived 
benefits of FIT, compared to those who view the DA without quantitative information (Hypothesis 2.1).  
According to the Health Belief Model, this larger perceived benefit should lead to a higher percentage 
choosing FIT as their intended screening behavior, and eventually in a higher percentage undergoing this test 
within six months (Hypothesis 2.2), compared to those who do not view the quantitative information. 
 
METHODS: 
Modification of DA modules, utilizing input of Patient Advisory Board: Prior to implementing the trial, we will 
modify the two DA modules that we have used in pilot testing, as described under Choice of Comparators, 
above, in keeping with the advice of our Patient Advisory Board.  We will pilot test the new modules by 
conducting cognitive interviewing with 20 eligible patients who will not be enrolled in the study.  We will 
present the modified DAs and the result of this cognitive interviewing to our Patient Advisory Board and 
Community Advisory Board within 2 months of the initiation of our project, to allow final changes and 
initiation of the study on time. 
 
Conduct Trial:   Study Design is shown in Figure 1.  Sites:  We will use three groups of primary practices:  (1) 
Five IU Health practices previously belonging to the Methodist Medical Group (MMG); (2) Four IU Health 
practices previously belonging to the IU Medical Group (IUMG); and, (3) Two Eskenazi Health practices. These 
eleven primary care sites served over 22,000 individual patients in 2013-14, approximately 7500 of whom are 
eligible for CRC screening.  These clinics serve a range of socioeconomic groups, ranging from upper-income 
suburban (Carmel, Fishers), to middle-income urban/ suburban (Greenwood #1/ #2, Glendale), to lower-
income urban (E. Washington, W.38th St.)   

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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Inclusion and Exclusion Criteria:  Patients will be eligible if they are (1) age 50-75 years and (2) have not had 
colonoscopy performed in last 10 years, sigmoidoscopy in last 5 years, or fecal occult blood testing (including 
FIT) in last 1 year.  Patients will be excluded from consideration if they are (1) undergoing workup for 
symptoms consistent with colon cancer, such as weight loss or rectal bleeding, (2) have a diagnosis or medical 
history conferring elevated risk for CRC including polypectomy or colon cancer, inflammatory bowel disease, 
certain inherited syndromes, or a significant family history of CRC, or (3) are unable to speak and read English. 
 
Recruitment:  As shown in Table 1, we believe that 
approximately 7500 patients seen in the 11 clinics in 
2013-14 were eligible for CRC screening (based on 
electronic health information used to report quality 
measures).  Given this pool of potentially eligible 
patients, we are confident we can recruit 15 patients 
per week, which would allow us to achieve our goal 
of 720 patients in 48 weeks. Of note, our timeline 
allows 68 weeks (16 months) to complete 
recruitment.  We have an established track record of 
successful recruiting in the IU Health system, in the 
preliminary studies reported above and other pilot 
research.  In these studies, recruiting just from the 
five MMG clinics, we recruited 8-10 patients per 
week when our systems were optimized.  The 
inclusion of the IUMG IU Health clinics and Eskenazi 
clinics more than doubles potentially eligible patients.  
The ResNet system, which will assist with recruitment 
at the IUMG and Eskenazi clinics, has a 40-year 
history of successful recruitment efforts.  
 
Randomization:  Our biostatistician will generate a 
stratified randomization scheme (i.e., within each 
clinic) that will be implemented by the research 
assistants.  
 
Subject recruitment and enrollment:  Potentially 
eligible patients will be identified using a query of the 
electronic medical record system, either by the IU 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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Health Clinical Research Systems working in partnership with the Regenstrief Data Core (for IU Health sites), or 
solely by the Regenstrief Data Core (for Eskenazi sites).  IU Health patients with an upcoming doctor’s visit or 
Eskenazi patients who are due to be seen will be sent a letter of introduction and explanation about the study 
signed by the patient’s physician.  The letter will also include a phone number that patients can call to “opt 
out” of being contacted.  A week later, the research assistant will call patients who have not called the “opt 
out” number to explain the study and answer questions about study requirements, potential risks, and 
compensation.  The research assistant will make an appointment for eligible, willing patients to meet the 
research assistant at the clinic before a clinic visit.  For those patients who refuse to be assessed for eligibility, 
or who are eligible but refuse to participate, the RA will record reasons they are willing to share.  In addition, 
we will collect demographic, insurance, and other information about those who refuse to participate, as 
allowed by the IRB.   
 
Intervention Delivery:   As in the preliminary studies, the DA will consist of a Powerpoint presentation with 
audio track that the patient views and controls on a laptop.  At the clinic meeting, the RA will review inclusion 
and exclusion criteria and obtain written informed consent and for patients who are willing, HIPAA waiver to 
allow an EMR check for screening uptake.  Patients will then complete the baseline questionnaire (Time 0 (T0)), 
be randomized to Control or Quantitative arm, and use the appropriate DA. 
 
Data collection and follow-up:  Data collection for assigned patients will occur at three time points: 1) Baseline 
(T0), before using the DA; 2) immediately after viewing the DA (T1), and; 3) Six months after randomization 
(T2).  T2 data will be elicited by telephone call by a research assistant who is blinded to the patient’s trial 
group assignment. Data will be entered directly into a REDCap survey (by the patient at T0 and T1; the 
research assistant at T2) that flags any missing data, which has minimized missing data in our preliminary 
studies.  At the time of T2, the EMR will be accessed to confirm screening uptake, again by a research assistant 
blinded to the patient’s group assignment. Subjects will receive compensation in the form of gift cards after 
the clinic meeting ($30) and T2 phone call ($20).  
 
Outcome measures and time of collection: 
• Perceived risk for CRC (T0 and T1):  Five-item scale containing items originally developed by Champion to 
measure perceived breast cancer risk86,87 and a measure of perceived age and gender-adjusted 
comparative risk,88 recently applied to CRC screening.89 Each item uses a four-point response option, 
where 1 = very unlikely and 4 = very likely, to assess participants’ beliefs about how likely they are to get 
CRC in the next 5 years, in the next 10 years and sometime during their lifetime. Two items assess 
likelihood of getting CRC if they do or do not have regular colon testing.89 
• Perceived benefit of screening (T0 and T1):  Assessed separately for colonoscopy and FIT, using items 
drawn from validated scales for measuring benefits, barriers, and self-efficacy of colonoscopy and FIT.89-91 
All scales have Likert responses (1 = strongly agree to 5 = strongly disagree). 

 
 
 
 
1502774679 - Schwartz, Peter - Research Plan - INFORM                                                                                                                                                                         initial submission 3-31-2015 
 
 
 
 
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• Screening intention (T0 and T1):  Which of the following do you think you will do in the next 6 months?  
(Choices:  Get a colonoscopy, Get a stool test, Get another colon test, Not get any colon test, Don’t know) 
• Decision conflict (T0 and T1):  The Decision Conflict Scale is a sixteen-item instrument that assesses 
patients’ subjective feeling regarding the decision process over five areas. Each item is a likert-style 
question with five-point response option, where 1= strongly agree and 5 = strongly disagree.  This scale has 
well documented psychometric characteristics and has been used in many studies of DAs.81 
• Screening behavior:  Six-months after viewing the intervention, participants will be asked whether they 
have completed colonoscopy, FIT, other screening, or no screening, and results will be checked with the 
electronic medical record.  Discrepancies between patient report and the EMR will be investigated and 
evaluated according to procedures established in our pilot studies. Screening rate will be calculated. 
 
Additional measures:   We will also collect the following information regarding participant characteristics to 
examine covariation and explore moderation effects: 
• Knowledge of CRC and CRC screening (T0 and T1): Qualitative knowledge assessed with eight True/ False 
questions regarding general information (including risk factors, screening test options, and test frequency) 
and quantitative knowledge (T1) assessed with four multiple-choice questions regarding probability of 
outcomes, as in the pilot study. 
• Numeracy (T0) will be assessed with the Subjective Numeracy Scale, a validated instrument that involves 8 
Likert-style questions.92,93   
• Literacy (T0) will be assessed with the REALM-SF instrument.96 
• Demographic data (T0) (including age, gender, income, education), as well as data on personal and family 
medical history, previous screening, and previous MD recommendations, utilizing a questionnaire that has 
been used by our team and other researchers in multiple studies of CRC screening.89  
 
Analytic methods:   Sample Size Justification:  For the two quantitative aims, our primary outcome is CRC 
screening behavior (recoded as yes or no).  For Aim 1, in our pilot study we found a difference in overall CRC 
screening rates between the control and quantitative group to be 14/52=26.9% vs 22/56=39.3%, a difference 
we consider to be clinically important.  In order to detect this difference with a two-sided chi-square test and 
level of significance of 0.05, a sample size of 241 per group (482 total) is needed. 
 
For Aim 2, when examining the interaction of numeracy and the intervention, our preliminary data suggests 
there will be no intervention effect on FIT in the low numeracy group, but that in the high numeracy group the 
intervention will lead to an increase in FIT of about 12% (0% in control group vs. 12.1% in quantitative group).  
Using the data from our pilot, the sample size needed was determined based on the calculations provided in 
Demidenko (2008)95 for detecting an interaction between two binary covariates (intervention group and 
numeracy) in a logistic regression model.  We assumed the probability of being in either intervention group or 
in either numeracy group of 0.5, prevalence of FIT of 0.2, and odds ratio for FIT based on intervention group 

 
 
 
 
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=1.68, odds ratio for FIT based on numeracy = 0.45, and odds ratio of numeracy by intervention group = 1.  In 
this case we would need a total of 600 (300 per group) to have 80% power (alpha=0.05) to detect an 
interaction OR of 3.2, which is similar to the interaction OR estimated from our preliminary data of 3.7.    Thus, 
to have sufficient power for both aims, we will target a total of 600 evaluable patients.  
 
This sample size will provide ample power for our other outcomes, which are all ordinal or numerical scales. 
For example, in examining the interaction of perceived risk and numeracy in Aim 2, we would have 99% power 
to detect the hypothesized interaction based on the means and standard deviation of this outcome from our 
pilot study.  In our pilot study the attrition rate was 17.2% (39 of 227 recruited patients).  We will allow for a 
dropout rate of 20%, and thus we will enroll 720 total participants. In addition to allowing for attrition, we will 
plan to enroll possibly 30 more participants. This is because we want to continue with our recruitment 
procedures until we reach our recruitment goal. This may mean that potential participants will be sent a 
recruitment letter during the same week that we reach our goal. We want to be able to follow-through with 
our recruitment procedures with these people even if we have met our recruitment goal.   We aim to enroll 
11-15 patients per week, to achieve our goal, in 45-66 weeks. Our timeline allows 68 weeks (16 months) to 
complete recruitment. 
 
Data Management and Analysis Plan: We will create a secure web-based system to capture study data using 
the REDCap database management system. We will review and process data using multiple verification and 
edit checking programs (e.g. range-checks, missing data reports). We will also conduct rudimentary analyses 
to ensure that the data have been properly collected and to identify any outliers or errors. A consort diagram 
will be constructed for reporting that accounts for all missing data.  
 
For Aim 1, our primary analyses will be to compare intention and overall CRC screening rates and the 
perceived risk and benefits of CRC screening between the two groups using either mixed effects logistic 
regression (for CRC intention and screening) or linear mixed models (for perceived risk and benefits)  that 
include a random effect for clinic.  Similar analyses will be used to compare FIT and Colonoscopy intention and 
screening rates and perceived risk and benefits. In addition we will do supplemental analyses treating CRC 
intention and screening as a three-level outcomes (yes/don’t know/no) and fit multinomial logistic regression 
models to explore if there are differences between those that respond don’t know vs no (relative to yes).  For 
Aim 2, our primary analysis will be to compare the outcomes by group and numeracy (split into low and high 
based on the median) using the same general modeling strategy described above.  Main effects for 
intervention and numeracy will be included in the model as well as an intervention-by-numeracy interaction 
term and random effect for clinic.  If the interaction term is significant, we will test the intervention effects 
separately in the two numeracy groups.   
 

 
 
 
 
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Other analyses to be conducted and reported will include basic descriptions of all study variables by group and 
time, assessment of internal reliability for scales by Cronbach’s alpha, assessing changes of time by group in 
for potential mediators by ANCOVA, exploring mediation effects using MPLUS software, exploring moderation 
for a few key variables (knowledge, literacy, demographics), and examining decision quality. To examine 
mediation, the effect sizes for the direct, indirect, and total effects will be estimated and tested for the paths 
from intervention group membership to the intervention mediators (perceived risk and benefits) and from 
mediators to primary outcomes (screening). Moderation will be assessed using the same methods described 
for numeracy in Aim 2. To assess decision quality, we will calculate concordance between intention and 
behavior for FIT, Colonoscopy, Don’t Know, and No screening using kappa statistics for each group and 
compare the kappa values using a chi-square test.96  We will assess the direction of the discordance by 
examining the two-way contingency tables that cross-classify intention and actual screening outcome. We will 
compare decision conflict over time between the two groups using linear mixed models. Finally, we will 
tabulate refusal reasons and reasons why screening is not up-to-date at enrollment and compare refusers to 
enrollees using two sample t-test, chi-square tests or Fisher’s Exact test as appropriate.  
 
Sensitivity analysis will be conducted for each outcome to assess the effects of the covariance structure on the 
results and determine which covariance structure is appropriate. Other assumptions, such as normality of 
residuals, will also be checked and appropriate modifications to the analysis plan applied if needed, such as 
transformations or use of non-parameteric methods.  All analyses will be intent-to-treat.  From our pilot study, 
we expect missing data to be minimal (less than 2%) for subjects during participation.  Thus, for missing data 
on scales we will use mean imputation as long as two-thirds of the questions have been answered (or per 
validated instrument instructions, if otherwise).  Longitudinally, we expect attrition to be less than 20%.  We 
will compare T0 information for those lost to follow-up to those that are not to investigate the plausibility of 
the data being missing at random (MAR), and conduct pattern mixture models analyses to see how the results 
could change based on the MAR assumption. 

 
 
 
 
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