Modex, a Cloud-based, Centralized Health Economic Model Marketplace to Reduce Costs and Enhance Equity in Cancer
Phase: NA
Conditions: Cancer
Sponsor: Arnold Consultancy & Technology, LLC

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 1 
 
HModEx®, a cloud-based, centralized health economic 
model marketplace to reduce costs and enhance equity 
in cancer 
Unique Protocol Identification Number: 1R43CA297808-01 
National Clinical Trial (NCT) Identified Number:  
Principal Investigator: Renée JG Arnold, PharmD 
Sponsor:  
Arnold Consultancy & Technology, LLC 
Grant Title: HModEx®, a cloud-based, centralized health economic model 
marketplace to reduce costs and enhance equity in cancer 
Grant Number: 1R43CA297808-01 
Funded by: NCI 
Version Number: v.0.010 
20 August 2024 
 
Summary of Changes from Previous Version: 
Affected 
Section(s)  
Summary of Revisions Made  
Rationale  
  Multiple 
Used System Usability Scale in 
Aim 1, as well as in Aim 2 
Objective measurement of usability for 
Aim 1 
  6.2.1 and 
10.1.9.1 
 Substituted Mr. Philbin for Ms. 
Cooley as UX/UI expert 
 Better fit for the study  
 
 
 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 2 
 
 
Table of Contents 
Statement of Compliance .............................................................................................................................................. 5 
Investigator's Signature ............................................................................................................................................. 5 
1 Protocol Summary ...................................................................................................................................................... 6 
1.1 Synopsis ............................................................................................................................................................... 6 
1.2 Schema ................................................................................................................................................................ 8 
1.3 Schedule of Activities ........................................................................................................................................... 9 
2 Introduction ................................................................................................................................................................ 9 
2.1 Study Rationale .................................................................................................................................................... 9 
2.2 Background ........................................................................................................................................................ 10 
2.3 Risk/Benefit Assessment.................................................................................................................................... 10 
2.3.1 Known Potential Risks ................................................................................................................................. 10 
2.3.2 Known Potential Benefits ........................................................................................................................... 10 
2.3.3 Assessment of Potential Risks and Benefits ............................................................................................... 10 
3 Objectives and Endpoints ......................................................................................................................................... 11 
4 Study Design ............................................................................................................................................................. 12 
4.1 Overall Design .................................................................................................................................................... 12 
4.2 Scientific Rationale for Study Design ................................................................................................................. 13 
4.3 Justification for Intervention ............................................................................................................................. 13 
4.4 End-of-Study Definition ..................................................................................................................................... 13 
5 Study Population ...................................................................................................................................................... 14 
5.1 Inclusion Criteria ................................................................................................................................................ 14 
5.2 Exclusion Criteria ............................................................................................................................................... 14 
5.3 Lifestyle Considerations ..................................................................................................................................... 14 
5.4 Screen Failures ................................................................................................................................................... 14 
5.5 Strategies for Recruitment and Retention ......................................................................................................... 14 
6 Study Intervention(s) or Experimental Manipulation(s) ........................................................................................... 15 
6.1 Study Intervention(s) or Experimental Manipulation(s) Administration ........................................................... 15 
6.1.1 Study Intervention or Experimental Manipulation Description ................................................................. 15 
6.1.2 Administration and/or Dosing .................................................................................................................... 15 
6.2 Fidelity ............................................................................................................................................................... 15 
6.2.1 INTERVENTIONIST TRAINING AND TRACKING ............................................................................................ 15 
6.3 Measures to Minimize Bias: Randomization and Blinding ................................................................................. 15 
6.4 Study Intervention/Experimental Manipulation Adherence ............................................................................. 15 
6.5 Concomitant Therapy ........................................................................................................................................ 16 
6.5.1 Rescue Therapy ........................................................................................................................................... 16 
7 Study Intervention/Experimental Manipulation Discontinuation and Participant Discontinuation/Withdrawal .... 16 
7.1 Discontinuation of Study Intervention/Experimental Manipulation ................................................................. 16 
7.2 Participant Discontinuation/Withdrawal from the Study .................................................................................. 16 
7.3 Lost to Follow-Up ............................................................................................................................................... 16 
8 Study Assessments and Procedures ......................................................................................................................... 16 
8.1 Endpoint and Other Non-Safety Assessments ................................................................................................... 16 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 3 
 
8.2 Safety Assessments ............................................................................................................................................ 16 
8.3 Adverse Events and Serious Adverse Events ..................................................................................................... 16 
8.3.1 Definition of Adverse Events ...................................................................................................................... 16 
8.3.2 Definition of Serious Adverse Events .......................................................................................................... 17 
8.3.3 Classification of an Adverse Event .............................................................................................................. 17 
8.3.4 Time Period and Frequency for Event Assessment and Follow-Up ............................................................ 17 
8.3.5 Adverse Event Reporting ............................................................................................................................ 17 
8.3.6 Serious Adverse Event Reporting................................................................................................................ 17 
8.3.7 Reporting Events to Participants ................................................................................................................ 17 
8.3.8 Events of Special Interest ........................................................................................................................... 17 
8.3.9 Reporting of Pregnancy .............................................................................................................................. 17 
8.4 Unanticipated Problems .................................................................................................................................... 17 
8.4.1 Definition of Unanticipated Problems ........................................................................................................ 17 
8.4.2 Unanticipated Problems Reporting ............................................................................................................ 17 
8.4.3 Reporting Unanticipated Problems to Participants .................................................................................... 18 
9 Statistical Considerations ......................................................................................................................................... 18 
9.1 Statistical Hypotheses ........................................................................................................................................ 18 
9.2 Sample Size Determination................................................................................................................................ 18 
9.3 Populations for Analyses ................................................................................................................................... 18 
9.4 Statistical Analyses............................................................................................................................................. 19 
9.4.1 General Approach ....................................................................................................................................... 19 
9.4.2 Analysis of the Primary Endpoint(s) ............................................................................................................ 19 
9.4.3 Analysis of the Secondary Endpoint(s) ....................................................................................................... 19 
9.4.4 Safety Analyses ........................................................................................................................................... 19 
9.4.5 Baseline Descriptive Statistics .................................................................................................................... 19 
9.4.6 Planned Interim Analyses ........................................................................................................................... 19 
9.4.7 Sub-Group Analyses .................................................................................................................................... 19 
9.4.8 Tabulation of Individual Participant Data ................................................................................................... 19 
9.4.9 Exploratory Analyses .................................................................................................................................. 19 
10 Supporting Documentation and Operational Considerations ................................................................................ 19 
10.1 Regulatory, Ethical, and Study Oversight Considerations ................................................................................ 19 
10.1.1 Informed Consent Process ........................................................................................................................ 19 
10.1.2 Study Discontinuation and Closure ........................................................................................................... 20 
10.1.3 Confidentiality and Privacy ....................................................................................................................... 20 
10.1.4 Future Use of Stored Specimens and Data ............................................................................................... 20 
10.1.5 Key Roles and Study Governance ............................................................................................................. 20 
10.1.6 Safety Oversight ........................................................................................................................................ 20 
10.1.7 Clinical Monitoring .................................................................................................................................... 20 
10.1.8 Quality Assurance and Quality Control ..................................................................................................... 20 
10.1.9 Data Handling and Record Keeping .......................................................................................................... 20 
10.1.10 Protocol Deviations................................................................................................................................. 21 
10.1.11 Publication and Data Sharing Policy ....................................................................................................... 21 
10.1.12 Conflict of Interest Policy ........................................................................................................................ 21 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 4 
 
10.2 Additional Considerations ............................................................................................................................... 21 
10.3 Abbreviations and Special Terms ..................................................................................................................... 21 
10.4 Protocol Amendment History .......................................................................................................................... 22 
11 References .............................................................................................................................................................. 23 
 
 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 5 
 
Statement of Compliance 
The trial will be conducted in accordance with International Council on Harmonisation Good Clinical 
Practice (ICH GCP), applicable United States (US) Code of Federal Regulations (CFR), and the National 
Cancer Institute Terms and Conditions of Award. The Principal Investigator will assure that no deviation 
from, or changes to the protocol will take place without prior agreement from the funding agency and 
documented approval from the Institutional Review Board (IRB), where necessary to eliminate an 
immediate hazard(s) to the trial participants. All personnel involved in the conduct of this study have 
completed Human Subjects Protection and ICH GCP Training. 
 
The protocol, informed consent form(s), recruitment materials, and all participant materials will be 
submitted to the IRB for review and approval. Approval of both the protocol and the consent form(s) 
must be obtained before any participant is consented. Any amendment to the protocol will require 
review and approval by the IRB before the changes are implemented to the study. All changes to the 
consent form(s) will be IRB approved; a determination will be made regarding whether a new consent 
needs to be obtained from participants who provided consent, using a previously approved consent 
form. 
Investigator's Signature 
Principal Investigator or Clinical Site Investigator: 
Signed: 
Date: 
November 20, 2025 
Name*:  
Renée JG Arnold, PharmD 
Title*:  
Principal Investigator 
 
Investigator Contact Information: 
Affiliation*:  
 Arnold Consultancy & Technology, LLC 
Address:  
 15 West 72nd Street – 23rd Floor, NY, NY  10023 
Telephone:  
 917.945.3267 
Email:  
 rarnold@arnoldllc.com 
 
For multi-site studies, the protocol should be signed by the clinical site investigator who is responsible  
for the day to day study implementation at his/her specific clinical site:  
Signed: 
 
 
Date: 
 November 20, 2025 
Name*:  
Renée JG Arnold, PharmD 
Title*:  
Principal Investigator 
Affiliation:  
 Arnold Consultancy & Technology, LLC 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 6 
 
1 Protocol Summary 
1.1 Synopsis 
Title: 
HModEx®, a cloud-based, centralized health economic model 
marketplace to reduce costs and enhance equity in cancer 
Grant Number: 
1R43CA297808-01 
Study Description: 
The investigators intend to create and test an interactive prototype 
of an online health economic model platform, HModEx®™, to 
house oncology models. 
Objectives: 
Primary Objective: Create the HModEx® health economic 
model platform  
Secondary 
Objectives: 
Conduct usability testing of search, quality 
and collaboration on the HModEx® 
prototype for 20 oncology health economic 
models 
Endpoints: 
Primary Endpoint: Iteratively unit test 4 tasks (A) Revise model 
platform and back end, (B) Refine the model 
taxonomy/characterization (i.e., ontology) and 
search module, (C) Revamp submission 
module, and (D) Implement paid modules)  
individually to ascertain 100% pass of 
common functionality (e.g., searching, 
uploading, and evaluating models) in the 
system back end and then iteratively perform 
integration testing via the front-end user 
interface until 100% pass using the System 
Usability Scale (SUS), a validated 
questionnaire. 
Secondary 
Endpoints: 
Measure the success of Aim 2 by (1) 
completion of 80% of tasks in usability 
testing with scores of ≥68 on the SUS, and (2) 
satisfaction with the site. Testing will focus 
on user interaction with the system, 
emphasizing effectiveness, efficiency, 
engagingness, error tolerance, and ease of 
learning. Specifically, the investigators will 
assess the ability of the project team and 
Advisory Panel members to log into the 
platform, configure and manage their 
modeling projects, and annotate or categorize 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 7 
 
them in accordance with an ontological index. 
We will assign panelists to interact with at 
least 2 randomly selected models and 
associated data files, iterating on this 
milestone until the investigators achieve 
100% success. 
Study Population: 
  
Phase* or Stage: 
National Institutes of Health (NIH) Small Business Innovation 
Research Phase I Study 
Description of 
Sites/Facilities Enrolling 
Participants: 
9 Expert Advisory Panel Members (4 from US, 5 from 
UK/Netherlands) 
Description of Study 
Intervention/Experimental 
Manipulation: 
Development and test of HModEx® prototype health economic 
model platform with 9 Expert Advisory Panel members  
Study Duration*: 
 1 year 
Participant Duration: 
 8 months 
 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 8 
 
1.2 Schema 
 
 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 9 
 
1.3 Schedule of Activities 
 
 
 
2 Introduction 
2.1 Study Rationale 
Annually, healthcare stakeholders spend an estimated $1B on health economics and outcomes research 
(HEOR), much of it to create or recreate computerized health economic (HE) models. They use HE 
models to estimate the incremental costs and benefits of screening methods and treatments, thus 
identifying the most cost-effective care and facilitating healthcare decision-making in the United States 
and globally. Value assessment guidelines developed by practitioner organizations (e.g., the American 
Society of Clinical Oncology), independent technology assessment review groups (e.g., the Institute for 
Clinical and Economic Review (ICER)), academicians, managed care organizations (MCOs) and other 
healthcare institutions often incorporate measures of cost (affordability) and/or cost-effectiveness. 
Decision-makers are increasingly seeking HE models for use across multiple technologies, as 
demonstrated by the renal cell carcinoma and non-small cell lung cancer pathway pilots led by the 
National Institute for Health and Care Excellence (NICE) in the UK. We use the term “HE model” to 
encompass cost-effectiveness analyses (CEAs), budget impact models (BIMs), and the like.   
Unfortunately, a significant barrier to using HE models has been insufficient access and transparency. 
Authors often create models for a single purpose, after which they languish in journal archives, health 
authority databases or various proprietary settings. With appropriate adaptation and validation, these 
models can be valuable in their application to other decision problems. The dearth of platforms and 
standards to facilitate the sharing of computer models makes the models inaccessible to peer reviewers, 
expert readers, public health officials, and decision makers. When the need arises for a new evaluation 
to inform health policy or value-based pricing (VBP), such as the need to set a drug’s maximum fair 
price, stakeholders must reinvent HE models instead of updating or reusing existing work. The creation 
and management of new models produces a siloed process, requiring considerable time (up to a year) 
and expense. An open source model (OSM) platform and exchange mechanism that catalogs vetted 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 10 
 
models with adequate documentation and is agnostic of model software eradicates many barriers to 
access. Several initiatives have attempted to address this issue; however, a unifying mechanism for HE 
model sharing remains an unmet need, as highlighted by multiple entities, such as ISPOR, Health 
Technology Assessment International (HTAi), and the Society for Medical Decision Making (SMDM). 
 
 
2.2 Background 
New cancer drugs have long been at the forefront of research and debate about rising healthcare 
expenditure in the United States; researchers and decision-makers rely on evidence to support fair 
pricing and ensure patient access. Health economic (HE) models enable decisions about the pricing and 
reimbursement of new technologies in the US and globally and support efficient and equitable 
investments. A significant challenge is that decision-makers perceive HE models to lack credibility 
because of their “black box” nature and lack of validation, preventing evidence from informing practice. 
Annually, stakeholders spend an estimated $1B on health economics and outcomes research (HEOR), 
and there is substantial waste in producing HE models because analysts routinely “reinvent the wheel” 
for similar decision problems. Sharing models by making their source code openly available would 
increase credibility and reduce waste for both modelers and Pharma stakeholders. However, prior 
initiatives have had limited success, partly due to a lack of incentives for model developers to share HE 
models. 
 
 
2.3 Risk/Benefit Assessment 
  
2.3.1 Known Potential Risks 
There are no known potential risks to the proposed research. 
2.3.2 Known Potential Benefits 
The benefits are that decision makers will have access to vetted models, which will be more rapid than 
the typical 9-12 months it takes to develop a model. 
2.3.3 Assessment of Potential Risks and Benefits 
  
N/A 
 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 11 
 
3 Objectives and Endpoints 
The prototype will accomplish 4 tasks: (A) Revise model platform and back end, (B) Refine the model 
taxonomy/characterization (i.e., ontology) and search module, (C) Revamp submission module, and (D) 
Implement paid modules. 
OBJECTIVES 
ENDPOINTS 
JUSTIFICATION 
FOR ENDPOINTS 
PUTATIVE 
MECHANISMS OF 
ACTION 
Primary 
 Create an interactive 
prototype of 
HModEx®™ using 
infrastructure from 
the Tufts CEVR Open-
Source Model 
Clearinghouse 
framework.  
The prototype will accomplish 
4 tasks: (A) Revise model 
platform and back end, (B) 
Refine the model 
taxonomy/characterization 
(i.e., ontology) and search 
module, (C) Revamp 
submission module, and (D) 
Implement paid modules. 
To evaluate the 
endpoints, the 
investigators will 
Iteratively unit test all 4 
tasks individually to 
ascertain 100% pass of 
common functionality 
(e.g., searching, 
uploading, and 
evaluating models) in 
the system back end and 
then iteratively perform 
integration testing via 
the front-end user 
interface until 100% 
pass using the System 
Usability Scale. 
N/A 
Secondary 
Conduct usability 
testing of search, 
quality and 
collaboration on the 
HModEx® prototype. 
We will use a convenience 
sample of 20 existing oncology 
OS models from GitHub, the 
literature and the Advisory 
Panel (using a combination of 
existing ontologies), accessed 
and analyzed using new 
settings/populations by 8 of a 
10-member Advisory Panel (2 
are Pharma only) and 
evaluated using the AdViSHE 
validation assessment tool to 
document model quality. 
Measure the success of 
Aim 2 by (1) completion 
of 80% of tasks in 
usability testing with 
scores of ≥68 on the 
System Usability Scale, a 
validated questionnaire, 
and (2) satisfaction with 
the site. Testing will 
focus on user 
interaction with the 
system, emphasizing 
effectiveness, efficiency, 
engagingness, error 
tolerance, and ease of 
learning. Specifically, 
N/A 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 12 
 
OBJECTIVES 
ENDPOINTS 
JUSTIFICATION 
FOR ENDPOINTS 
PUTATIVE 
MECHANISMS OF 
ACTION 
the investigators will 
assess the ability of the 
project team and 
Advisory Panel members 
to log into the platform, 
configure and manage 
their modeling projects, 
and annotate or 
categorize them in 
accordance with an 
ontological index.  We 
will assign panelists to 
interact with at least 2 
randomly selected 
models and associated 
data files, iterating on 
this milestone until the 
investigators achieve at 
least 75% success. 
Tertiary/Exploratory 
  
  
  
  
4 Study Design 
4.1 Overall Design 
This is a single-arm study that will build and evaluate a health economic model platform prototype using a 10-
member expert advisory panel of health economists from the US and Europe. For aim 1, Panelists will be 
randomly assigned to interact with several models according to key use cases (e.g. model publishing, model 
searching, user registration, user profile updating, payments, etc.), create wireframes for each of these using 
Axure RP (or equivalent tool), and create a high level click through prototype of the UI for feedback from 
Advisory Panel (AP) members. Once the UI has been defined, the investigators will code up each use case and 
alpha test those with 5 AP members.  We plan to build unit tests and end user test scripts to help identify bugs 
as early as possible in the development process and to aid in debugging. A satisfaction questionnaire, using a 
Likert-like scale, will be devised to ensure that the Advisory Panel finds that 90% of this key functionality is 
stable; then, the investigators will begin beta testing with selected end users to gather additional feedback. 
 
For aim 2, a primary test dataset comprising 20 oncology OSMs from the AP members, CISNET and websites will 
be uploaded into the HModEx® platform, annotated, searched for (using a combination of existing ontologies), 
accessed, and analyzed. Models that meet the vetting criteria will be triaged to one or more AP members based 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 13 
 
on their expertise/interests for further validation. The models will be accessed and evaluated for quality using 
the CADTH validation assessment tool by our 9 modeler AP members. This validation process will include 
annotation using the ontology module (Aim 1) and activation of a paid module for premium content; for this first 
phase, the premium content will be simulated and the paid module will be manually administered. The success 
of Aim 2 will be assessed via the ability to retrieve a model with the requested search characteristics 90% of the 
time (Aim 2A, 2B) and complete 80% of tasks in usability testing with scores of ≥68 on the System Usability Scale, 
a validated questionnaire83, and satisfaction with the site (Aim 2C). Testing will focus on how users interact with 
the system, emphasizing effectiveness, efficiency, engagingness, error tolerance, and ease of learning 83, 84; 
remote usability testing will be conducted using screen-sharing technology. Specifically, the investigators will 
assess the ability for the project team and AP members to log in, configure/manage their modeling projects, 
annotate or categorize them according to an ontological index and for each AP member to modify/evaluate at 
least 2 of the 20 oncology models and associated data files (Figure 4). Panel members will be randomly assigned 
to thus interact with multiple models; the investigators will iterate on this milestone until 100% achieved. A brief 
report will be produced that will review quantitative results, qualitative findings, and design recommendations 
for addressing problem areas. This report will be shared with our AP and will inform any necessary product 
modifications. The Advisory Panel will be charged with the following responsibilities during alpha and beta 
testing of our proposed platform: 
  
--6 members alpha testing for 1 hour each@$200/hr, which includes uploading an oncology model of 
your choosing 
--5 members beta testing for 2 hours each for 2 models, which includes interacting with 5 models on the 
platform 
 
The total remuneration for each Advisory Panel member is up to $2,000  ($200/hr*2 hrs*5 models) over the 
course of 6-12 months after formal awarding of the project. 
 
4.2 Scientific Rationale for Study Design 
See Study Rationale above. 
 
4.3 Justification for Intervention 
N/A 
 
4.4 End-of-Study Definition 
Achievement of milestones, as above. 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 14 
 
5 Study Population 
5.1 Inclusion Criteria 
10 Expert Advisory Panel members 
 
5.2 Exclusion Criteria 
Those not within our already recruited Expert Advisory Panel 
 
5.3 Lifestyle Considerations 
None 
 
5.4 Screen Failures 
N/A 
 
5.5 Strategies for Recruitment and Retention 
The Advisory Panel members have already been recruited from the PI’s knowledge base and the ISPOR Open 
Source Model Special Interest Group. 
 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 15 
 
6 Study Intervention(s) or Experimental Manipulation(s) 
6.1 Study Intervention(s) or Experimental Manipulation(s) Administration 
6.1.1 Study Intervention or Experimental Manipulation Description 
 See Overall Design above. 
6.1.2 Administration and/or Dosing 
 See Overall Design above. 
 
6.2 Fidelity 
6.2.1 INTERVENTIONIST TRAINING AND TRACKING 
The user experience (UX)/user interface (UI)/human factors expert, Mr. Kevin Philbin, has 19 years of 
experience in this field. He will be developing clickable grayscale wireframes – sufficient to begin 
evaluating the product against key user tasks. Mr. Philbin will then evaluate this prototype via UX study 
sessions with 6 AP members. An informal summary of findings and observations will be provided, with 
learnings incorporated into the following iterative prototype. Second Iteration Prototyping and Testing: 
Based on learnings from the UX studies of the first iteration prototype, Mr. Philbin and company will 
collaborate with the programmer to evolve the (or create a new) prototype of the HModEx® platform. 
The prototype will have higher levels of content, interactive, and/or visual fidelity than the previous 
prototype, sufficient to validate design changes and continue to evaluate the product against key user 
tasks. Mr. Philbin will then evaluate this prototype via UX study sessions with 9 Advisory Panel 
members. Each participant will be asked to complete a System Usability Scale (SUS) questionnaire at the 
end of their session. Mr. Philbin will provide an informal summary of findings and observations, with all 
learnings incorporated into a final recommendation for Content, Information Architecture, Screen 
Layout, Interactive Design, and Visual Design for the initial launch of HModEx®. 
6.3 Measures to Minimize Bias: Randomization and Blinding 
The models will be assigned so as to make sure that the Advisory Panel member to which they are 
assigned has not previously interacted with this model and has interest in the model subject matter. 
 
6.4 Study Intervention/Experimental Manipulation Adherence 
N/A 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 16 
 
6.5 Concomitant Therapy 
 N/A 
6.5.1 Rescue Therapy 
 N/A 
7 Study Intervention/Experimental Manipulation 
Discontinuation and Participant 
Discontinuation/Withdrawal 
7.1 Discontinuation of Study Intervention/Experimental Manipulation 
Upon achievement of all endpoints for both study aims. 
 
7.2 Participant Discontinuation/Withdrawal from the Study 
N/A 
 
7.3 Lost to Follow-Up 
N/A 
 
8 Study Assessments and Procedures 
8.1 Endpoint and Other Non-Safety Assessments 
See study design. 
 
8.2 Safety Assessments 
N/A 
 
8.3 Adverse Events and Serious Adverse Events 
8.3.1 Definition of Adverse Events 
 N/A 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 17 
 
8.3.2 Definition of Serious Adverse Events 
 N/A 
8.3.3 Classification of an Adverse Event 
 N/A 
8.3.3.1 Severity of Event 
 N/A 
8.3.3.2 Relationship to Study Intervention/Experimental Manipulation 
 N/A 
8.3.3.3 Expectedness 
 N/A 
8.3.4 Time Period and Frequency for Event Assessment and Follow-Up 
 N/A 
8.3.5 Adverse Event Reporting 
 N/A 
8.3.6 Serious Adverse Event Reporting 
 N/A 
8.3.7 Reporting Events to Participants 
 N/A 
8.3.8 Events of Special Interest 
 N/A 
8.3.9 Reporting of Pregnancy 
 N/A 
 
 
8.4 Unanticipated Problems 
8.4.1 Definition of Unanticipated Problems 
8.4.2 Unanticipated Problems Reporting 
 A Data Safety and Monitoring Plan (DSMP) will be developed that will include a brief description of the study 
design, potential risks and benefits for participating in the study, procedures for data review and reportable 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 18 
 
events, roles and responsibilities of study staff and the monitoring entity (PI and PhD student), and data 
management, quality control and quality assurance. 
8.4.3 Reporting Unanticipated Problems to Participants 
 This will be within the DSMP. 
 
 
9 Statistical Considerations 
9.1 Statistical Hypotheses 
• 
Primary Efficacy Endpoint(s): 
To evaluate the primary endpoints, the investigators will Iteratively unit test all 4 tasks individually to 
ascertain 100% pass of common functionality (e.g., searching, uploading, and evaluating models) in the 
system back end and then iteratively perform integration testing via the front-end user interface until 
100% pass using the System Usability Scale (SUS), a validated questionnaire. 
  
• 
Secondary Efficacy Endpoint(s): 
We will measure the success of the secondary endpoint by (1) completion of 80% of tasks in 
usability testing with scores of ≥68 on the SUS, and (2) satisfaction with the site. Testing will focus on 
user interaction with the system, emphasizing effectiveness, efficiency, engagingness, error 
tolerance, and ease of learning. Specifically, the investigators will assess the ability of the project 
team and Advisory Panel members to log into the platform, configure and manage their modeling 
projects, and annotate or categorize them in accordance with an ontological index.  We will assign 
panelists to interact with at least 2 randomly selected models and associated data files, iterating on 
this milestone until the investigators achieve at least 75% success. 
9.2 Sample Size Determination 
This is a convenience sample of 20 models to be evaluated by 9 of our Expert Advisory Panel members. 
 
9.3 Populations for Analyses 
Expert Advisory Panel. 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 19 
 
9.4 Statistical Analyses 
9.4.1 General Approach 
 See above. These are established methods of evaluating UX/UI and software usability. 
9.4.2 Analysis of the Primary Endpoint(s) 
 See above. 
9.4.3 Analysis of the Secondary Endpoint(s) 
 See above. 
9.4.4 Safety Analyses 
 N/A 
9.4.5 Baseline Descriptive Statistics 
Descriptive statistics of the Expert Advisory Panel will include age, sex, primary geography, years of 
practice in the health economics field, modeling expertise characteristics, and specific expertise in the 
oncology area. 
9.4.6 Planned Interim Analyses 
 N/A 
9.4.7 Sub-Group Analyses 
 N/A 
9.4.8 Tabulation of Individual Participant Data 
 N/A (Data will be aggregated). 
9.4.9 Exploratory Analyses 
 N/A 
 
 
10 Supporting Documentation and Operational 
Considerations 
10.1 Regulatory, Ethical, and Study Oversight Considerations 
10.1.1 Informed Consent Process 
 N/A 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 20 
 
10.1.1.1 Consent/assent and Other Informational Documents Provided to participants 
 N/A 
10.1.1.2 Consent Procedures and Documentation 
 N/A 
10.1.2 Study Discontinuation and Closure 
 When all study aims/milestones have been achieved. 
10.1.3 Confidentiality and Privacy 
 Participant data will be collected using initials only and all data will be kept in a secure Box file with 
access only on a need-to-know basis. 
10.1.4 Future Use of Stored Specimens and Data 
 All data will be saved in the Box file to inform an SBIR Phase II submission. 
10.1.5 Key Roles and Study Governance 
Principal Investigator  
Medical Monitor or Independent Safety 
Monitor 
 Renée JG Arnold, PharmD 
  
  
  
  
  
  
  
  
  
  
10.1.6 Safety Oversight 
 See above about the DSMP. 
10.1.7 Clinical Monitoring 
 This will be accomplished by Drs. Arnold and Minhas, as well as by Ms. Wang. 
10.1.8 Quality Assurance and Quality Control 
 See above for DSMP. 
10.1.9 Data Handling and Record Keeping 
10.1.9.1 Data Collection and Management Responsibilities 
 UX/human factor testing data will be collected by Mr. Philbin and analyzed by Dr. Arnold, Dr. Minhas 
and Ms. Wang. 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 21 
 
10.1.9.2 Study Records Retention 
 Study records will be retained in a secure Box account. 
10.1.10 Protocol Deviations 
Protocol deviations will be reported to the IRB within 72 hours, according to the seriousness of the 
deviation, as delineated in the DSMP. 
10.1.11 Publication and Data Sharing Policy 
 Results of the study will be presented and published in a timely fashion. 
10.1.12 Conflict of Interest Policy 
 N/A 
 
 
10.2 Additional Considerations 
None 
 
10.3 Abbreviations and Special Terms 
AE 
Adverse Event 
ANCOVA Analysis of Covariance 
CFR 
Code of Federal Regulations 
CLIA 
Clinical Laboratory Improvement Amendments 
CMP 
Clinical Monitoring Plan 
COC 
Certificate of Confidentiality 
CONSORT Consolidated Standards of Reporting Trials 
CRF 
Case Report Form 
DCC 
Data Coordinating Center 
DHHS 
Department of Health and Human Services 
DSMB 
Data Safety Monitoring Board 
DRE 
Disease-Related Event 
EC 
Ethics Committee 
eCRF 
Electronic Case Report Forms 
FDA 
Food and Drug Administration 
FDAAA 
Food and Drug Administration Amendments Act of 2007 
FFR 
Federal Financial Report 
GCP 
Good Clinical Practice 
GLP 
Good Laboratory Practices 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 22 
 
GMP 
Good Manufacturing Practices 
GWAS 
Genome-Wide Association Studies 
HIPAA 
Health Insurance Portability and Accountability Act 
IB 
Investigator's Brochure 
ICH 
International Council on Harmonisation 
ICMJE 
International Committee of Medical Journal Editors 
IDE 
Investigational Device Exemption 
IND 
Investigational New Drug Application 
IRB 
Institutional Review Board 
ISM 
Independent Safety Monitor 
ITT 
Intention-To-Treat 
LSMEANS Least-squares Means 
MedDRA Medical Dictionary for Regulatory Activities 
MOP 
Manual of Procedures 
NCT 
National Clinical Trial 
NIH 
National Institutes of Health 
NIH IC 
NIH Institute or Center 
OHRP 
Office for Human Research Protections 
PI 
Principal Investigator 
QA 
Quality Assurance 
QC 
Quality Control 
SAE 
Serious Adverse Event 
SAP 
Statistical Analysis Plan 
SMC 
Safety Monitoring Committee 
SOA 
Schedule of Activities 
SOC 
System Organ Class 
SOP 
Standard Operating Procedure 
UP 
Unanticipated Problem 
US 
United States 
 
 
10.4 Protocol Amendment History 
Version 
Date 
Description of Change  
Brief Rationale 
 20 November, 
2025 
 20 November, 
2025 
 See page 1 
 Better study alignment with 
endpoints and personnel 
 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 23 
 
11 References 
 
1. 
Global Market Insights. Health economics & outcomes research services market to hit 
$1.7Bn by 2026, Says GMI.  2021  [cited 2021 February 24]; Available from: 
https://www.globenewswire.com/news-release/2020/01/27/1975229/0/en/Health-Economics-
Outcomes-Research-Services-Market-to-hit-1-7Bn-by-2026-Says-GMI.html 
2. 
Neumann P, Thorat T, Shi J, Saret C, Cohen J. The changing face of the cost-utility 
literature,1990–2012. Value Health 2015;18:271-277. 
3. 
Neumann PJ, Sanders GD. Cost-Effectiveness Analysis 2.0. N Engl J Med 2017 Jan 
19;376(3):203-205. 
4. 
Neumann PJ, Cohen JT, Ollendorf DA. Drug-Pricing Debate Redux - Should Cost-
Effectiveness Analysis Be Used Now to Price Pharmaceuticals? N Engl J Med 2021 Nov 13. 
5. 
Neumann PJ, Cohen JT. Measuring the Value of Prescription Drugs. N Engl J Med 2015 
Dec 31;373(27):2595-2597. 
6. 
Young RC. Value-Based Cancer Care. N Engl J Med 2015 Dec 31;373(27):2593-2595. 
7. 
Bach PB. New Math on Drug Cost-Effectiveness. N Engl J Med 2015 Nov 
05;373(19):1797-1799. 
8. 
National Institute for Health and Care Excellence. Taking a proportionate approach to 
technology appraisals--the Pathway approach to technology appraisals.  2024  [cited 2024 
February 5]; Available from: https://www.nice.org.uk/about/what-we-do/proportionate-approach-
to-technology-appraisals#:~:text=Pathway%20approach%20to%20technology%20appraisals 
9. 
Conti RM, Frank RG, Gruber J. Regulating Drug Prices while Increasing Innovation. N 
Engl J Med 2021 Nov 18;385(21):1921-1923. 
10. 
Emerson J, Bacon R, Kent A, Neumann PJ, Cohen JT. Publication of Decision Model 
Source 
Code: 
Attitudes 
of 
Health 
Economics 
Authors. 
Pharmacoeconomics 
2019 
Nov;37(11):1409-1410. 
11. 
Shafrin J, Lakdawalla D, Doshi J, et al. A strategy for value-based drug pricing under the 
inflation reduction act. Health Affairs Forefront 2023(May 4). 
12. 
Drummond M, Sorenson C. Use of Pharmacoeconomics in Drug Reimbursement in 
Australia, Canada, and the United Kingdom: What Can We Learn from International Experience? 
In: Arnold R, ed. Pharmacoeconomics: from theory to practice. Boca Raton: CRC Press (Taylor 
& Francis Group LLC); 2010: 175-196. 
13. 
Eddy DM, Hollingworth W, Caro JJ, et al. Model transparency and validation: a report of 
the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making 2012 
Sep-Oct;32(5):733-743. 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 24 
 
14. 
Watkins JB, Sullivan SD, Sampsel E, et al. Evolution of the AMCP Format for Formulary 
Submissions. J Manag Care Spec Pharm 2020 Jun;26(6):696-700. 
15. 
Tufts Medical Center. Center for the Evaluation of Value and Risk in Health Open-Source 
Model 
Clearinghouse. 
 
2017 
 
[cited 
2021 
December 
22]; 
Available 
from: 
https://cevr.shinyapps.io/Clearinghouse/ 
16. 
Icahn School of Medicine at Mount Sinai. Mount Sinai Launches Department of Artificial 
Intelligence and Human Health.  October 11, 2021  [cited 2022 August 17]; Available from: 
https://www.mountsinai.org/about/newsroom/2021/mount-sinai-launches-department-of-
artificial-intelligence-and-human-health 
17. 
Arnold R, Ekins S. Ahead of our time: collaboration in modeling then and now. 
Pharmacoeconomics 2017;35(9):975-976. 
18. 
Arnold RJ, Ekins S. Time for cooperation in health economics among the modelling 
community. Pharmacoeconomics 2010 Aug 1;28(8):609-613. 
19. 
Pouwels X, Sampson C, Arnold R. Opportunities and barriers to the development and use 
of open source health economic models: a survey. Value in Health 2022;24(4):473-479. 
20. 
International Society for Pharmacoeconomics and Outcomes Research. Open Source 
Models Special Interest Group. 2020. 
21. 
Commons C. Creative Commons.  2020  [cited 2020 September 2]; Available from: 
https://creativecommons.org/licenses/by/4.0/legalcode 
22. 
Duparc E, Möller F, Jussen I, Stachon M, Algac S, Otto B. Archetypes of open-source 
business models. Electronic Markets 2022 2022/06/01;32(2):727-745. 
23. 
Matthews PC. Fairness in scientific publishing. F1000Res 2016;5:2816. 
24. 
Henderson R. A review of online health economic model repositories on behalf of the 
ISPOR Open Source Model Special Interest Group. In: Arnold R, ed.; 2023. 
25. 
Briggs SEW, Law P, East JE, et al. Integrating genome-wide polygenic risk scores and 
non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: population based 
cohort study. BMJ 2022;379:e071707. 
26. 
Collins R, Fenwick E, Trowman R, et al. A systematic review and economic model of the 
clinical effectiveness and cost-effectiveness of docetaxel in combination with prednisone or 
prednisolone for the treatment of hormone-refractory metastatic prostate cancer. Health Technol 
Assess 2007 Jan;11(2):iii-iv, xv-xviii, 1-179. 
27. 
Deibel A, Deng L, Cheng CY, et al. Evaluating key characteristics of ideal colorectal cancer 
screening modalities: the microsimulation approach. Gastrointest Endosc 2021 Aug;94(2):379-
390.e377. 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 25 
 
28. 
Pouwels X, Ramaekers BLT, Geurts SME, et al. An economic evaluation of eribulin for 
advanced breast cancer treatment based on the Southeast Netherlands advanced breast cancer 
registry. Acta oncologica (Stockholm, Sweden) 2020 Sep;59(9):1123-1130. 
29. 
Prakash MK, Lang B, Heinrich H, et al. CMOST: an open-source framework for the 
microsimulation of colorectal cancer screening strategies. BMC medical informatics and decision 
making 2017 Jun 5;17(1):80. 
30. 
Lieberman D. Challenges for colorectal cancer screening decision modeling. 
Gastrointestinal endoscopy 2021;94(2):391-393. 
31. 
Dasbach EJ, Elbasha EH. Verification of Decision-Analytic Models for Health Economic 
Evaluations: An Overview. Pharmacoeconomics 2017 Apr 29. 
32. 
Dunlop WC, Mason N, Kenworthy J, Akehurst RL. Benefits, Challenges and Potential 
Strategies of Open Source Health Economic Models. Pharmacoeconomics 2017 Jan;35(1):125-
128. 
33. 
Karnon J. Model Validation: Has it's Time Come? Pharmacoeconomics 2016 
Sep;34(9):829-831. 
34. 
Sampson CJ, Wrightson T. Model Registration: A Call to Action. PharmacoEconomics - 
Open 2017;1(2):73-77. 
35. 
Hatswell AJ, Chandler F. Sharing is Caring: The Case for Company-Level Collaboration 
in Pharmacoeconomic Modelling. Pharmacoeconomics 2017 Aug;35(8):755-757. 
36. 
Cohen JT, Neumann PJ, Wong JB. A Call for Open-Source Cost-Effectiveness Analysis. 
Ann Intern Med 2018 Apr 3;168(7):529. 
37. 
McQueen RB, Padula WV, Campbell JD. A Call for Open-Source Cost-Effectiveness 
Analysis. Ann Intern Med 2018 Apr 3;168(7):528-529. 
38. 
Sampson CJ. A Call for Open-Source Cost-Effectiveness Analysis. Ann Intern Med 2018 
Apr 3;168(7):528. 
39. 
Cohen JT, Neumann PJ, Wong JB. A Call for Open-Source Cost-Effectiveness Analysis. 
Ann Intern Med 2017 Sep 19;167(6):432-433. 
40. 
Sampson CJ, Arnold R, Bryan S, et al. Transparency in Decision Modelling: What, Why, 
Who and How? Pharmacoeconomics 2019 Nov;37(11):1355-1369. 
41. 
Bruggeman T. What Kind of Companies Offer Royalties?  2023  [cited 2024 January 16]; 
Available from: https://www.maqro.com.au/blog/royalties/what-kind-of-companies-offer-royalties-
/ 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 26 
 
42. 
Vemer P, Corro Ramos I, van Voorn GAK, Al MJ, Feenstra TL. AdViSHE: A Validation-
Assessment Tool of Health-Economic Models for Decision Makers and Model Users. 
PharmacoEconomics 2016 April 01;34(4):349-361. 
43. 
Feenstra T. Discussion of use of Assessment of the Validation Status of Health-Economic 
decision models (AdViSHE) validation-assessment tool in initial model assessment of robustness. 
2023. 
44. 
National Cancer Institute. Cancer Intervention and Surveillance Modeling Network 
(CISNET).  2023  [cited 2023 August 24]; Available from: https://cisnet.cancer.gov/colorectal/ 
45. 
Evans P, Wolf B. Collaboration rules. Harv Bus Rev 2005 Jul-Aug;83(7):96-104, 192. 
46. 
Mauskopf 
J, 
Annemans 
L. 
Budget 
impact 
analysis. 
In: 
Arnold 
RJ, 
ed. 
Pharmacoeconomics: from Theory to Practice. 2nd ed. Boca Raton: CRC Press (Taylor & Francis 
Group LLC); 2021: 137-152. 
47. 
Marketwatch. Health Economics and Outcomes Research (HEOR) services market 
projected to reach US$ 2,699.56 million by 2027 and to grow at a CAGR of 11.9%.  2021  [cited 
2021 February 25]; Available from: 
https://www.marketwatch.com/press-release/health-
economics-and-outcomes-research-heor-services-market-projected-to-reach-us-269956-million-
by-2027-and-to-grow-at-a-cagr-of-119-analysis-group-avalon-health-economics-2021-02-
19#:~:text=Press%20Release-,Health%20Economics%20and%20Outcomes%20Research%20(
HEOR)%20Services%20Market%20projected%20to,Analysis%20Group%2C%20Avalon%20He
alth%20Economics&text=The%20MarketWatch%20News%20Department%20was,the%20creat
ion%20of%20this%20content. 
48. 
Innovation and Value Initiative. Open-Source Value Platform.  2019  [cited 2019 July 16]; 
Available from: https://www.thevalueinitiative.org/open-source-value-project/ 
49. 
Policy Analysis I. heRo3.  2019  [cited 2019 July 16]; Available from: 
https://heroapps.io/#contact 
50. 
Jansen JP, Incerti D, Linthicum MT. Developing Open-Source Models for the US Health 
System: Practical Experiences and Challenges to Date with the Open-Source Value Project. 
Pharmacoeconomics 2019 Aug 7. 
51. 
Arnold RJ, Kim R, Tang B. The cost-effectiveness of argatroban treatment in heparin-
induced thrombocytopenia: the effect of early versus delayed treatment. Cardiol Rev 2006 Jan-
Feb;14(1):7-13. 
52. 
Benard A, Duroux T, Robert G. Cost-utility analysis of focal high-intensity focussed 
ultrasound vs active surveillance for low- to intermediate-risk prostate cancer using a Markov 
multi-state model. BJU Int 2019 Jul 12. 
53. 
Evans M, Wolden M, Gundgaard J, Chubb B, Christensen T. Cost-effectiveness of insulin 
degludec compared with insulin glargine for patients with type 2 diabetes treated with basal insulin 
- from the UK health care cost perspective. Diabetes Obes Metab 2014 Apr;16(4):366-375. 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 27 
 
54. 
Langer J, Wolden ML, Shimoda S, Sato M, Araki E. Short-Term Cost-Effectiveness of 
Switching to Insulin Degludec in Japanese Patients with Type 2 Diabetes Receiving Basal-Bolus 
Therapy. Diabetes Ther 2019 Aug;10(4):1347-1356. 
55. 
Pokras S, Divino V, Ferrufino C, Arnold R, Huang H. The economic burden of dasatinib 
and nilotinib treatment failure in chronic myeloid leukemia (CML) patients: a real-world analysis 
Value in Health 2013;16:A137. 
56. 
Schlueter M, Finn E, Diaz S, Dilla T, Inciarte-Mundo J, Fakhouri W. Cost-effectiveness 
analysis of baricitinib versus adalimumab for the treatment of moderate-to-severe rheumatoid 
arthritis in Spain. Clinicoecon Outcomes Res 2019;11:395-403. 
57. 
Termrungruanglert W, Khemapech N, Tantitamit T, Havanond P. Cost effectiveness 
analysis of HPV primary screening and dual stain cytology triage compared with cervical cytology. 
J Gynecol Oncol 2019 Mar;30(2):e17. 
58. 
Yu T, Tradonsky A, Tang J, Arnold R. Cost-Effectiveness of adding endocuff to standard 
colonoscopies for interval colorectal cancer screening. ClinicoEconomics and Outcomes 
Research 2019;11:487-504. 
59. 
Yu TM, Dugel PU, Haller JA, Kaiser PK, Arnold RJ. Budget impact analysis of ocriplasmin 
for the treatment of symptomatic vitreomacular adhesion in the USA. J Comp Eff Res 2018 
Dec;7(12):1195-1207. 
60. 
Yu TM, Morrison C, Gold EJ, Tradonsky A, Arnold RJG. Budget Impact of Next-Generation 
Sequencing for Molecular Assessment of Advanced Non-Small Cell Lung Cancer. Value Health 
2018 Nov;21(11):1278-1285. 
61. 
Arnold RG, Pettit K, DiCesare J, Canafax D, Lewis R, Kaniecki D. C13. Application of 
medicare databases to a theoretical renal transplant markov model. Clinical Therapeutics 1996 
1996/01;18:28. 
62. 
Goldberg Arnold R, Kaniecki D, Tak Piech C, Puder K. An economic evaluation of HMG-
CoA reductase inhibitors for cholesterol reduction in the primary prevention of coronary heart 
disease.  11th International Conference on Pharmacoepidemiology. Montreal, Que; 1995. 
63. 
Arnold RJ, Kaniecki DJ, Frishman WH. Cost-effectiveness of antihypertensive agents in 
patients with reduced left ventricular function. Pharmacotherapy 1994 Mar-Apr;14(2):178-184. 
64. 
Jubran A, Gross N, Ramsdell J, et al. Comparative cost-effectiveness analysis of 
theophylline and ipratropium bromide in chronic obstructive pulmonary disease. A three-center 
study. Chest 1993 Mar;103(3):678-684. 
65. 
Podrid PJ, Kowey PR, Frishman WH, et al. Comparative cost-effectiveness analysis of 
quinidine, procainamide and mexiletine. Am J Cardiol 1991 Dec 15;68(17):1662-1667. 
66. 
Cher D, Arnold R, Frasco M, Polly D. Cost-Effectiveness of Minimally Invasive Sacroiliac 
Joint Fusion. ClinicoEconomics and Outcomes Research 2016;8:1-14. 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 28 
 
67. 
Arnold R, Layton A, Rustay N, Chen S. Cost-Effectiveness of Extended-Release 
Carbidopa-Levodopa Compared for Advanced Parkinson’s Disease. American Journal Pharm 
Benefits 2017;9(1):23-29. 
68. 
Avram CM, Yieh L, Dukhovny D, Caughey AB. A Cost-Effectiveness Analysis of Rooming-
in and Breastfeeding in Neonatal Opioid Withdrawal. Am J Perinatol 2019 Aug 1. 
69. 
Baik G, Brietzke SE. Cost Benefit and Utility Decision Analysis of Turbinoplasty with 
Adenotonsillectomy for Pediatric Sleep-Disordered Breathing. Otolaryngol Head Neck Surg 2019 
Aug;161(2):343-347. 
70. 
Bullard KA, Edelman AB, Williams SM, Rodriguez MI. Ulipristal acetate compared to 
levonorgestrel emergency contraception among current oral contraceptive users: a cost-
effectiveness analysis. Contraception 2019 May 16. 
71. 
Liles C, Dallas J, Hale AT, et al. The economic impact of open versus endoscope-assisted 
craniosynostosis surgery. J Neurosurg Pediatr 2019 May 31:1-8. 
72. 
Xu Y, Wei Y, Ming J, et al. Cost-Effectiveness Analysis of Non-invasive Prenatal Testing 
for Down Syndrome in China. Int J Technol Assess Health Care 2019 Jan;35(3):237-242. 
73. 
Kent S, Becker F, Feenstra T, et al. The Challenge of Transparency and Validation in 
Health Economic Decision Modelling: A View from Mount Hood. Pharmacoeconomics 2019 Jul 
26. 
74. 
Society for Medical Decision Making. The COVID-19 Rapid Response Grant Initiative.  
2020  [cited 2020 June 28]; Available from: https://smdm.org/news/detail/The-COVID-19-Rapid-
Response-Grant-Initiative 
75. 
The Linux Foundation Projects. R Consortium: successful R-Based package submission 
with shiny component to FDA.  2022 December 7, 2022 [cited 2023 August 14]; Available from: 
https://www.r-consortium.org/blog/2022/12/07/update-successful-r-based-package-submission-
with-shiny-component-to-fda 
76. 
Google. Google Cloud Pricing Calculator.  2021  [cited 2021 November 20]; Available 
from: https://cloud.google.com/products/calculator 
77. 
Linode. Linode Predictable Pricing.  2021  [cited 2021 December 1]; Available from: 
https://www.linode.com/pricing/ 
78. 
Sharma P, Montgomery RN, Graves RS, et al. CONSENSUS: a Shiny application of 
dementia evaluation and reporting for the KU ADC longitudinal Clinical Cohort database. JAMIA 
Open 2021;4(3):ooab060-ooab060. 
79. 
Duda S LJ. August Data Sharing and Reuse Seminar.  Harmonist/IeDEA: Designing 
Consortium Tools for Collaboration and Data Sharing. https://datascience.nih.gov/news/august-
data-sharing-and-reuse-seminar-2022: National Institutes of Health Office of Data Science 
Strategy; August 12, 2022. 

HModEx®, a cloud-based, centralized health economic model marketplace to reduce costs and enhance equity in cancer 
Protocol 1R43CA297808-01 
20 November, 2025 
Page | 29 
 
80. 
COMBINE. COMBINE, the computational modeling in biology network.  2020  [cited 2020 
August 21]; Available from: http://co.mbine.org/ 
81. 
Stanford Center for Biomedical Informatics Research at the Stanford University School of 
Medicine. Protégé and Web Protégé 2020. 
82. 
University of Maryland School of Medicine Institute for Genome Sciences. Disease 
Ontology.  2021  [cited 2021 December 22]; Available from: https://disease-ontology.org 
83. 
Komninos A. An introduction to usability.  2020  [cited 2020 July 13]; Available from: 
https://www.interaction-design.org/literature/article/an-introduction-to-usability 
84. 
Lowry P, Spaulding T, Wells T, Moody G, Moffit K, Madariaga S. A Theoretical Model and 
Empirical Results Linking Website Interactivity and Usability Satisfaction. Proceedings of the 
Annual Hawaii International Conference on System Sciences 2006 01/23;6. 
85. 
Waxman J, Faget K, Daniels B. Blockchain: A tool with a future in healthcare. Medical 
Economics. 2019 July 15. 
86. 
Simon B. Blockchain: Great Potential, Great Obstacles. Wall Street Journal. 2021 
December 24, 2021.