Second-line Therapies for Patients With Type 2 Diabetes and Moderate Cardiovascular Disease Risk
Conditions: Type 2 Diabetes, Cardiac Disease
Sponsor: Mayo Clinic

 
 
Patient preferences for second-line Type 2 
diabetes medication treatment outcomes 
 
NCT05214573 
 
22July2021 

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IRB Minimal Risk Protocol Template 
 
General Study Information 
 
Principal Investigator:       Rozalina G. McCoy, M.D. 
 
 
 
 
 
 
 
 
Study Title:    Patient preferences for second-line Type 2 diabetes medication treatment outcomes 
 
Protocol version number and date:     Version 1; July 22, 2021 
 
Research Question and Aims 
 
Hypothesis: Patients with type 2 diabetes will have different preferences and priorities for what outcomes of 
diabetes management are most important to them. 
 
Aims, purpose, or objectives: This study is a component of a broader study examining the comparative 
effectiveness and safety of second-line glucose-lowering medications in the management of type 2 diabetes. All 
other components of this study (other than this one) use de-identified administrative claims data from 
OptumLabs Data Warehouse (OLDW) and as such are exempt from IRB review. 
 
For this human subjects study, we will elicit patient preferences toward various treatment outcomes using a 
participatory ranking exercise and use these rankings to generate individually weighted composite outcomes. 
These composite outcomes (without any linked participant information) will be used for analyses within OLDW 
that will estimate patient-centered treatment effects of four different classes of second-line medications for Type 
2 diabetes with respect to those composite outcomes specifically. The four drug classes that will be examined 
Note: If this study establishes a human specimen repository (biobank) for research 
purposes, do not use this template. Use the Mayo Clinic Human Specimen Repository 
Protocol Template found on the IRB home page under Forms and Procedures at 
http://intranet.mayo.edu/charlie/irb/ 
 
First-time Use: Use this template to describe your study for a new IRB submission. 
1. Complete the questions that apply to your study. 
2. Save an electronic copy of this protocol for future revisions.    
3. When completing your IRBe application, you will be asked to upload this document to the protocol 
section. 
Modification:  To modify this document after your study has been approved: 
1. Open your study in IRBe. Click on the study ‘Documents’ tab and select the most recent version of the 
protocol. Save it to your files. 
2. Open the saved document and activate “Track Changes”.  
3. Revise the protocol template to reflect the modification points , save the template to your files 
4. Create an IRBe Modification for the study and upload the revised protocol template. 

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are the GLP-1 receptor agonists (GLP-1RA), SGLT2 inhibitors (SGLT2i), DPP-4 inhibitors (DPP-4i), and 
sulfonylureas (SU). 
 
Background (Include relevant experience, gaps in current knowledge, preliminary data, etc.):   
 
Type 2 diabetes (T2D) is one of the most common and serious chronic health conditions in the U.S.1 Optimal 
patient-centered T2D care is predicated on treating each patient with medications that are likely to yield the 
most benefit and risk the least harm, weighing the best available evidence against each patient’s preferences and 
situation. While metformin is consistently recommended as the 1st-line drug in the management of T2D, there is 
less clarity regarding 2nd-line therapy as new drug classes were approved and introduced into practice,2-5 
resulting in decisional dilemmas for patients and clinicians, and potential missed opportunities to select the drug 
that best optimizes the patient’s health. 
 
Cardiovascular disease (CVD), heart failure (HF), and chronic kidney disease (CKD) are leading causes of 
morbidity, mortality, and costs associated with T2D.6-9 Randomized controlled trials (RCTs) designed to 
evaluate the CV safety of T2D drugs revealed additional therapeutic benefits of glucagon-like peptide-1 
receptor agonists (GLP-1RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) with respect to CVD, 
HF, and CKD outcomes among patients at high risk for these events. However, it is unknown how each 
patient’s preferences and values impact the comparative effectiveness of these drugs.   
 
As a result, patients and clinicians face a decisional dilemma at the heart of T2D management: Which drug 
class would be best for the specific set of outcomes desired by an individual patient with T2D? Addressing these 
knowledge gaps is critical for patients and clinicians to engage in shared decision-making; for professional 
societies to develop guidelines that reflect scientific evidence and patient priorities; and for health systems and 
payers to make formulary decisions. 
 
Study Design and Methods 
 
Methods:  Describe in lay terms, completely detailing the research activities that will be conducted by Mayo 
Clinic staff under this protocol. 
 
We will ask a purposive sample of patients with T2D (N=25) to engage in a participatory ranking exercise in 
which they will rank the outcomes of T2D therapy are most important to them personally and then contextualize 
these decisions. Participatory ranking exercises are widely used in studies of preference elicitation to determine 
the relative importance of treatment attributes and outcomes.10 We will develop an inventory of potential 
outcomes and adverse events associated with the four drug classes of interest (e.g., hospitalization, severe 
hypoglycemia). As part of an individual interview with a member of the research team, we will ask patient 
participants to rank these pre-specified outcomes in order of their relative personal importance using a secure 
online survey platform (Qualtrics). The researcher will ask patients to contextualize their ranking decisions. 
Next, participants will be asked to identify additional outcomes that are missing from this prespecified list of 
clinical outcomes, but which may be important to patients with T2D (e.g., medication cost, frequency of 
administration). Finally, participants will be asked to incorporate these additional, patient-generated outcomes 
into their ranking decisions and explain their rationale to the researcher.  

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We will seek to minimize cognitive burden on participants through iterative feedback and pilot-testing with 
patient-investigators, using lay language and avoiding medical jargon, identifying an appropriate number of pre-
specified outcomes for ranking, and ensuring usability of Qualtrics, a secure online ranking platform. The 
ranking exercise will be conducted using telephone or videoconferencing software, or in person, depending on 
patient preference. The ranking exercise will be audio-recorded to ensure an accurate record of participant 
responses to questions about their ranking choices. 
Subsequent to the participatory ranking exercise, all participants will be interviewed using a semi-structured 
interview guide. The interview guide will ask participants about their previous experiences with diabetes 
medications, the factors that are most important to them in general when making health care treatment 
decisions, the types of discussions about treatment options that occur during clinical visits, and any differences 
they have observed in outcomes that are important to health care providers versus those that are most important 
to patients.  
Patient participants (N=25) will be recruited from primary care and endocrinology practices in Mayo Clinic and 
Mayo Clinic Health System in Minnesota and Wisconsin. Inclusion criteria are: ≥21 years old, T2D, use of ≥1 
study drug (GLP-1RA, SGLT2i, DPP-4i, SU). Exclusion criteria are: insulin use, cognitive impairment, 
terminal/advanced illness, non-English speaking, residency in a long-term care setting.  
A study coordinator at Mayo Clinic will recruit patients using Epic to identify patients who meet eligibility and 
sampling criteria. The study coordinator will call eligible participants to describe the study, obtain verbal 
informed consent, obtain HIPAA authorization, schedule the ranking exercise session, and obtain mailing 
information for the remuneration, which will be mailed after completion of the ranking exercise. Sampling will 
be guided by the principle of response saturation. We will use stratified purposeful sampling to intentionally 
select participants representing predefined demographic (age, sex, race/ethnicity, insurance type) and clinical 
(HbA1c level, T2D drugs used) characteristics.  
Study participation will be voluntary, and participants will be compensated with a $25 gift card for taking part 
in this study. 
Subject Information 
 
Target accrual is the proposed total number of subjects to be included in this study at Mayo Clinic. A “Subject” 
may include medical records, images, or specimens generated at Mayo Clinic and/or received from external 
sources.    
 
Target accrual: 25 
 
Subject population (children, adults, groups):  Adults with Type 2 diabetes receiving care from primary care and 
endocrinology practices in Mayo Clinic and Mayo Clinic Health System in Minnesota and Wisconsin. 
 
Inclusion Criteria: ≥21 years old, T2D diagnosis, use of ≥1 study drug (GLP-1RA, SGLT2i, DPP-4i, SU) 
 
Exclusion Criteria:  Insulin use, cognitive impairment, terminal/advanced illness, non-English speaking,  
residency in a long-term care setting. 
 
 

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Biospecimens 
 
Collection of blood samples. When multiple groups are involved copy and paste the appropriate section below 
for example repeat section b when drawing blood from children and adults with cancer.  
 
a. From healthy, non-pregnant, adult subjects who weigh at least 110 pounds. For a minimal risk 
application, the amount of blood drawn from these subjects may not exceed 550ml in an 8 week period 
and collection may not occur more frequently than 2 times per week. 
Volume per blood draw: _____ml   
Frequency of blood draw (e.g. single draw, time(s) per week, per year, etc.) ___________ 
 
b. From other adults and children considering age, weight, and health of subject. For a minimal risk 
application, the amount of blood drawn from these subjects may not exceed the lesser of 50 ml or 3 ml 
per kg in an 8 week period, and collection may not occur more frequently than 2 times per week.   
Volume per blood draw: _____ml 
Frequency of blood draw (e.g. single draw, time(s) per week, per year, etc.) ___________  
 
Prospective collection of biological specimens other than blood: ______________________________ 
 
 
Review of medical records, images, specimens  
 
 
Check all that apply (data includes medical records, images, specimens).  
 
  Only data that exists before the IRB submission date will be collected.   
 
Date Range for Specimens and/or Review of Medical Records:   
Examples: 01/01/1999 through 12/31/2015, or all records through mm/dd/yyyy.  
 
Note: The Date Range must include the period for collection of baseline data, as well as follow-up data, 
if applicable. 
 
  The study involves data that exist at the time of IRB submission and data that will be generated after IRB 
submission. Include this activity in the Methods section.  
Examples 
• The study plans to conduct a retrospective chart review and ask subjects to complete a questionnaire.  
• The study plans to include subjects previously diagnosed with a specific disease and add newly 
diagnosed subjects in the future.  
 

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  The study will use data that have been collected under another IRB protocol. Include in the Methods section 
and enter the IRB number from which the research material will be obtained. When appropriate, note when 
subjects have provided consent for future use of their data and/or specimens as described in this protocol.  
 
Enter one IRB number per line, add more lines as needed 
 
 Data    
 Specimens  
 Data & Specimens  ______________________________________ 
 
 Data    
 Specimens  
 Data & Specimens  ______________________________________ 
 
 Data    
 Specimens  
 Data & Specimens  ______________________________________ 
 
 
Data Analysis 
 
 
Power analyses may not be appropriate if this is a feasibility or pilot study, but end-point analysis plans are 
always appropriate even if only exploratory. Provide all information requested below, or provide justification if 
not including all of the information.  
 
Power Statement:  We anticipate 25 participants to be adequate to identify the heterogeneity of outcomes that 
matter to people living with T2D. Sampling will be guided by the principle of response saturation. We will use 
stratified purposeful sampling to intentionally select participants representing predefined demographic (age, 
sex, race/ethnicity, insurance type) and clinical (HbA1c level, T2D drugs used) characteristics.11,12 Analysis (i.e. 
generation of the individually weighted composite outcome) will occur concurrent with data collection, and we 
anticipate reaching saturation in the variability of patient preference rankings of the various outcomes with no 
more than 25 participants.13-15  
 
Data Analysis Plan:  
Quantitative analysis of the participatory ranking exercise: Each participant’s responses will be used to generate 
an individualized weighted composite outcome. We will consider a utility function that is a weighted 
combination of the individual endpoints specified by each participant, with weights determined by the patient’s 
preferences as proposed in Butler et al.16 The patient’s preference weights will be scaled to sum to 1, such that 
the patient-specific outcome will be a convex combination of the eligible outcomes. With the weighted utility 
function, we then define potentially counterfactual treatment effects, defined as the expected utility function 
under the four different treatment exposures. The optimal individual treatment rule is defined as in Qian and 
Murphy17 as the treatment that maximizes the expected utility function.  
We will then use the composite outcomes generated using the participator ranking exercise (see above) in a 
study that uses de-identified data within OLDW. Specifically, we will emulate a target trial comparing the 
effectiveness of GLP1-RA, SGLT2i, DPP-4i, and SU classes with respect to each of these patient-specified 
composite outcomes. The target parameter will be the six pairwise differences in the personalized utility 
function between the drug classes. We provide an overview of that analysis below, though all analyses using 

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OLDW data have been previously deemed exempt from IRB review as all data is statistically de-identified by 
Optumlabs prior to being made available to researchers. 
Primary analyses will be conducted under an intention-to-treat approach, modified to define “intention” as a 
patient having two consecutive fills of the assigned drug. All patients will continue follow-up and complete all 
outcome assessments until the planned conclusion of the study or end of observation in OLDW.  
We will estimate multinomial propensity score models using data from 12 months prior to the index date to 
account for the multiple treatments.18 Predictions from the estimated propensity score model can be used for 
inverse probability weighting (IPW) to emulate baseline randomization. We will allow for flexible modeling 
strategies with controls to assess for overfitting. Our proposal is to use the super learner framework19 with a 
diverse set of individual prediction algorithms included in the ensemble. The super learner framework allows 
for flexible estimation of an ensemble predictor with the theoretical properties of cross-validation for model 
selection to control for overfitting. The framework has been demonstrated to outperform individual algorithms 
for the estimation of propensity scores.20,21 The distribution of the predicted propensity scores will be evaluated 
for evidence of violations of the positivity assumption. For moderate violations of positivity, the propensity 
scores will be truncated at 0.01 and 0.99 to control inflation of the standard errors. Our intention is to use the 
entire distribution of OLDW as our population of interest, but if more extreme positivity violations are 
observed, the reference population will be narrowed to a realistic treatment intervention population. 
The proposed IPW weight with multinomial treatment groups is unstablized, as our primary estimand is the 
population average treatment effect.22 If weights have extreme values when evaluating the distributions, then 
the stabilized version using the denominator proposed in Yoshida23 for the optimal matching stabilized weights 
with multiple treatments will be used as a sensitivity analysis to avoid large confidence intervals for the effect 
estimates. The final propensity scores will then be incorporated as IPWs into a time-to-event model. If the 
proportional hazards assumptions hold, this will be a Cox model; otherwise, we will use Akaike Information 
Criteria to identify the best parametric time-to-event specification for the data. For the final model, we will 
report model diagnostic and performance statistics, and either hazard or time ratios with 95% confidence 
intervals. A single model with all treatment groups will be estimated and the omnibus test for at least one 
treatment group having a different cumulative incidence rate will be evaluated. If that test is statistically 
significant, then each pairwise comparison will be tested and pairwise effects estimated.  
The targeted learning algorithm for the causal hazard rate as defined in Moore and van der Laan24 will be 
implemented to estimate the four treatment hazard rates and the corresponding hazard ratios or time ratios, 
according to the model specification, will be estimated for each of the 6 pairwise comparisons between 
treatment groups. A single overall omnibus test for the four hazard rates being equal to each other versus at least 
one different will be conducted at the 0.05 significance level, with pairwise comparison p-values adjusted using 
the Holm closed sequential multiple testing procedure.25,26  
Predefined subgroup analyses for heterogeneity of treatment effects (HTE) will be evaluated for the following 
characteristics: sex (female vs. male), race/ethnicity (Black, Hispanic, Asian, other vs. White), and age (45-64, 
65-74, ≥75 vs. <45 years). While there is no definitive evidence of HTE by these characteristics, the incidence 
of CVD and kidney disease outcomes are known to vary27-30 and may be mediated by different responses to 
therapeutic interventions. We will also assess for HTE as a function of different levels of underlying CVD risk, 
as the examined drugs may be more effective at reducing the risks of primary and secondary endpoints among 
patients at higher risk for these events or with certain CVD risk factors already present.31-35 36-39 The primary 
model will be used to estimate treatment effects across subgroups, with the targeted parameter for the subgroup 
analysis being the difference in the predicted treatment effect for each pairwise comparison between the 

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subgroups.40 We will estimate multivariable effect prediction models41,42 to assess for HTE between the 
predefined subgroups by incorporating specific parameters for the statistical interaction effect between the 
treatment and subgroup variables.36,37 Propensity score weights will be used to emulate randomization 
according to the target trial framework. Internal cross-validation will be used to control for overfitting of the 
prediction model.43 Adjustment for multiple testing will be done accounting for the total number of predefined 
HTE analyses. 
A secondary analysis will be conducted under an as-treated approach, whereby patients will be censored upon 
discontinuation of the assigned treatment (no fills after 30 days beyond the dispensed pill count) or addition of 
another drug (i.e. another 2nd-line drug or insulin). To adjust for the likely informative censoring post-baseline, 
time-dependent propensity score models will be estimated and applied to dynamically reweight the population 
and account for time-varying confounding and informative loss to follow-up. The time-dependent propensity 
score models will be based on all prior covariate information up to that time point. This will address two 
concerns with use of observational methods for comparative effectiveness, immortal time bias44 and time-lag 
bias,45 as we will allow for time-dependent exposures and reweighting on time-conditional propensity scores.46  
 
Qualitative analysis of the accompanying interview: Interviews will be transcribed and analyzed according to a 
general thematic analysis approach (Braun & Clark, 2012) to identify patterns of meaning across patient 
interviews. 
 
 
 

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Suissa S. Lower Risk of Death With SGLT2 Inhibitors in Observational Studies: Real or Bias? Diabetes 
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Suissa S, Moodie EE, Dell'Aniello S. Prevalent new-user cohort designs for comparative drug effect 
studies by time-conditional propensity scores. Pharmacoepidemiol Drug Saf. 2017;26(4):459-468.