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Challenges in Clinical Data Management: Findings from the Tufts CSDD Impact Report

Posted by Brook White on Fri, Feb 09, 2018 @ 12:24 PM
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Derek Lawrence, Senior Clinical Data ManagerDerek Lawrence, Senior Clinical Data Manager, has 9 years of data management and analysis experience in the health care/pharmaceutical industry.  Derek serves as Rho's Operational Service Leader in Clinical Data Management, an internal expert responsible for disseminating the application of new technology, best practices, and processes.

The most recent Impact Report from the Tufts Center for the Study of Drug Development presented the results of a study including nearly 260 sponsor and CRO companies into clinical data management practices and experience. A high-level summary of the findings included longer data management cycle times than those observed 10 years ago, delays in building clinical databases, a reported average of six applications to support each clinical study, and a majority of companies reporting technical challenges as it pertained to loading data into their primary electronic data capture (EDC) system.

These findings represent the challenges those of us in clinical data management are struggling with given the current state of the clinical research industry and technological changes. EDC systems are still the primary method of data capture in clinical research with 100% of sponsors and CROs reporting at least some usage. These systems are experiencing difficulties in dealing with the increases in data source diversity. More and more clinical data are being captured by new and novel applications (ePRO, wearable devices, etc.) and there is an increased capacity to work with imaging, genomic, and biomarker data. The increases in data changing EDC paradigmvolume and data velocity have resulted in a disconnect with the EDC paradigm. Data are either too large or are ill-formatted for import into the majority of EDC systems common to the industry. In addition, there are significant pre-study planning and technical support demands when it comes to loading data into these systems. With 77% of sponsors and CROs reporting similar barriers to effective loading, cleaning, and use of external data, the issue is one with which nearly everyone in clinical research is confronted.

EDC integrationRelated to the issues regarding EDC integration are delays in database build. While nearly half of the build delays were attributed to protocol changes, just over 30% resulted from user acceptance testing (UAT) and database design functionality. Delays attributed to database design functionality were associated with a LPLV-to-lock cycle time that was 39% longer than the overall average. While the Tufts study did not address this directly, it would be no great stretch of the imagination to assume that the difficulties related to EDC system integration are a significant contributor to the reported database functionality issues. With there already being delays associated with loading data, standard data cleaning activities that are built into the EDC system and need to be performed before database lock would most certainly be delayed as well.

Clinical data management is clearly experiencing pains adapting to a rapidly-shifting landscape in which a portion of our current practices no longer play together nicely with advances in data-mining.jpgtechnology and data source diversity. All of this begs the question “What can we do to change our processes in order to accommodate these advances?” At Rho, we are confronting these challenges with a variety of approaches, beginning with limiting the impulse to automatically import all data from external vendors into our EDC systems. Configuring and updating EDC systems requires no small amount of effort on the part of database builders, statistical programmers, and other functional areas. Potential negative impacts to existing clinical data are a possibility when these updates are made as part of a database migration. At the end of the day, importing data into an EDC system results in no automatic improvement to data quality and, in some cases, actually hinders our ability to rapidly and efficiently clean the data. In developing standard processes for transforming and cleaning data external to the EDC systems, we increase flexibility in adapting to shifts in incoming data structure or format and mitigate the risk of untoward impacts to the contents of the clinical database by decreasing the prevalence of system updates.

The primary motivation for loading data received from external vendors into the EDC system is to provide a standard method of performing data cleaning activities and cross-checks against the clinical data themselves. To support this, we are developing tools to aggregate that data from a variety of sources and assemble them for data cleaning purposes. Similar to the ways the banking industry uses machine learning to identify ‘normal’ and ‘abnormal’ spending patterns and make real-time decisions to allow or decline purchases, similar algorithms can identify univariate and multivariate clusters of anomalous data for manual review. These continually-learning algorithms will enable a focused review of potentially erroneous data without the development of the traditional EDC infrastructure. This will save time performing data reviews and also identify potential issues which we would normally miss had we relied on the existing EDC model. With the future state resulting in an ever-broadening landscape of data sources and formats, an approach rooted in system agnosticism and sound statistical methodology will ensure we are always able to provide high levels of data quality.

An Interactive Suite of Data Visualizations for Safety Monitoring

Posted by Brook White on Thu, Feb 23, 2017 @ 01:42 PM
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This is the fourth in a series of posts introducing open source tools Rho is developing and sharing online. Click here to learn more about Rho's open source effort, here to read about our interactive data visualization library, Webcharts, and here to learn about SAS graphing tools we've developed.

Frequent and careful monitoring of patient safety is one of the most important concerns of any clinical trial. For the medical monitors and safety monitoring committees responsible for supervising patient well-being and ensuring product safety, this obligation requires continuous access to a variety of critical study data.

For trials with large participant enrollment, severe diseases, or complex treatments, study monitors may be tasked with reviewing thousands of data points and safety markers. Unfortunately, traditional reporting methods require monitors to comb through scores of static listings and summary tables. This method is inefficient and poses the risk that clinically-relevant signals will be obscured by the sheer volume of data common in clinical trials.

To improve safety monitoring, we created a suite of interactive data monitoring tools we call the Safety Explorer. Although the safety explorer can be configured to include a variety of charts specific to each study, the standard set-up includes 6 charts (click the links to learn more):

  • Adverse Events Explorer - dynamically query adverse event (AE) data in real time to go from study population view to individual patient records
  • Adverse Events Timeline - view interactive timelines for each participant showing when AEs occurred in a trial
  • Test Results Histogram- explore interactive histograms showing distribution of labs, vital signs, and other safety measures with linked data tables
  • Test Results Outlier Explorer - track patient trajectories over time for lab measures, vital signs, and other safety endpoints in line charts
  • Test Results Over Time - explore population averages for labs, vital signs, and other safety endpoints in box or violin plots
  • Shift Plot - monitor changes in lab measures, vital signs, and other safety endpoints between study events in a dot plot

The safety explorer utilizes common CDISC data standards to quickly create consistent charts for any project. Within a given chart, users can use filters to dynamically sort, highlight, and drill down to data points of interest using controls familiar to anyone who has used a website.

Interactive Histogram with Linked Table

interactive histogram safety data

Explore the distribution of test results (click here for interactive version)

Graphical representations of data grant reviewers a systematic snapshot of the data that helps tell the story of the information. By adding interactive elements, reviewers can quickly examine the charts for patterns of interest and drill down to subject-level data instantly. This ability to quickly distinguish signal from noise, gives monitors greater insight into their data and allows them to work much more efficiently.

It is common practice for us to create safety explorers for all full service projects and studies where Rho provides medical monitoring. All of the charts described here are open source and free to use, so please let us know if you have any feedback, or would like to contribute!

Interactive Box Plot Showing Results Over Time

interactive box plot showing results over time

Track changes in population test results through a study (click here for interactive version)

View "Visualizing Multivariate Data" Video

Ryan Bailey, Senior Clinical ResearcherRyan Bailey, MA is a Senior Clinical Researcher at Rho.  He has over 10 years of experience conducting multicenter asthma research studies, including theInner City Asthma Consortium (ICAC) and the Community Healthcare for Asthma Management and Prevention of Symptoms (CHAMPS) project. Ryan also coordinates Rho’s Center for Applied Data Visualization, which develops novel data visualizations and statistical graphics for use in clinical trials.

5 Tips for Creating a Request for Proposal (RFP) for Clinical Trial Services

Posted by Brook White on Tue, Feb 14, 2017 @ 11:13 AM
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RFPs tips that allow apples to apples comparisons of clinical trial servicesIf you’re looking for a contract research organization (CRO) to provide clinical trial services, chances are you’ll need to create a request for proposal (RFP). In the complicated world of outsourcing clinical trials, using RFPs to gather comparable bids from CROs can be incredibly challenging. The good news is, with a little planning and time, you can create RFPs that will reduce inconsistencies among bidders and ultimately help you identify the CRO that is truly the right partner for the job.

Here are five tips for creating RFPs that will help you compare “apples to apples” and help the CROs better understand your needs, values, and selection criteria for your clinical trial services:

  1. Provide background information on your compound and program.  Information about other clinical studies completed or in progress, outcomes from preclinical work, regulatory strategy and even funding and marketing plans can provide context that will help a CRO understand your needs and give you a proposal that best addresses all of your concerns.
  2. Provide a protocol or protocol synopsis.  Details about the study, such as number of clinical trial sites, number of subjects, and type and frequency of procedures and assessments are important cost drivers and providing them will help ensure a more accurate proposal.  Also, an experienced CRO should also be able to make valuable recommendations based on your protocol.
  3. Provide detailed RFP information to get consistent costs. Be specific. Some examples might include:
    • Project specifications – What are the important details of your program? (Use our RFP specifications tool)
    • Project timelines – By when do you expect certain milestones to be met?
    • Responsibilities (CRO, sponsor, other vendors) – For which segments of your program do you need a CRO to provide clinical trial services?
  4. Provide additional details. The more details you can provide the better.  It’s also OK to ask questions of prospective CROsask the CRO to make recommendations. You can tell a lot about a CRO by the recommendations they make and how they make them.  However, if you ask CROs to make recommendations be prepared for potential inconsistencies in the assumptions made and pricing offered between different CROs. The following are some additional details that might be helpful to bidders:
    • Provide site locations if you have already determined which sites you want to use.  If you aren’t sure, ask for recommendations based on your target enrollment and timelines.
    • If you’ve already determined which sites you’ll be using, it is helpful to know whether they will use a central lab or local lab and also will they use a local or central IRB. This can have an impact on timelines and costs.
    • Make note of any additional vendors you need such as specialty labs, Electronic Patient Reported Outcomes (ePRO), translations, meeting planners, or imaging services.
    • Will you be using paper or EDC? The vast majority of trials are now using EDC, but there may be some small studies or specific circumstances where paper still makes sense.
    • Do you want your data output in CDISC format? Based on the FDA’s guidance, new studies must be submitted in CDISC format, so it is strongly recommended.
    • If you are planning an interim analyses or will need support for a DSMB, make sure to include this information.
    • Will you use automated subject randomization (IVRS or IWRS)?
    • What are your plans for clinical supplies and distribution (IP management)?
    • Are you interested in risk-based monitoring strategies?  If so, include this information in your RFP. Incorporating remote monitoring or targeted SDV strategies could impact the budget.What are your plans for clinical supplies and distribution (IP management)?
    • Do you want the CRO to be responsible for the TMF?  If so, ask about whether they use an eTMF and if so which one.
    • If you know you want to use specific vendors (i.e. you know you want to use Medidata RAVE for EDC), be sure to include that information.
  5. Other items to request from CROs:
    • Project team CVs including the project manager, lead CRA, lead data manager, medical monitor, and lead statistician
    • Summary of team therapeutic experience and experience running similar trials
    • Relevant company information
Download: RFP Specifications Tool

 

Using SAS to Create Novel Data Visualizations

Posted by Brook White on Tue, Feb 07, 2017 @ 12:59 PM
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Ryan Bailey, Senior Clinical ResearcherRyan Bailey, MA is a Senior Clinical Researcher at Rho.  He has over 10 years of experience conducting multicenter asthma research studies, including theInner City Asthma Consortium (ICAC) and the Community Healthcare for Asthma Management and Prevention of Symptoms (CHAMPS) project. Ryan also coordinates Rho’s Center for Applied Data Visualization, which developsnovel data visualizations and statistical graphics for use in clinical trials.

Shane Rosanbalm, Senior BiostatisticianShane Rosanbalm, MS, Senior Biostatistician, has over fifteen years of experience providing statistical support for clinical trials in all phases of drug development, from Phase I studies through NDA submissions.  He has collaborated with researchers in several areas including neonatal sepsis, RA, oncology, chronic pain, hypertension, and Parkinson’s disease.  He is the lead SAS developer on Rho’s Center for Applied Data Visualization, where he develops tools and publishes on best practices for visualizing and reporting data.

This is the third in a series of posts introducing open source tools Rho is developing and sharing online. Click here to learn more about Rho's open source effort.

In our last post, we introduced Webcharts, one of our many interactive web-based charting tools that uses D3. In addition to the many web-based tools that Rho has on GitHub, we also maintain a number of SAS®-based graphics repositories. In fact, our strong reputation for clinical biostatistics and expertise with SAS (and SAS graphing tools) long predated our development of web graphics.

A sampling of some of our SAS tools is provided below, but we invite you to visit GitHub and check out our full offering of SAS tools. You can use the Find a repository... Search bar to search for "SAS". All of our SAS repositories begin with "sas-".

Codebook

sas codebook

SAS codebook

The SAS codebook macro is designed to provide a quick and concise summary of every variable in a SAS dataset. In addition to information about variable names, labels, types, formats, and statistics, the macro also produces a small graphic showing the distribution of values for each variable. This report is a convenient way to provide a snapshot of your data and quickly get to know a new dataset.

Violin Plot

violin plot

The SAS violin plot macro is designed to allow for a quick assessment of how the distribution of a variable changes from one group to another. Think of it as a souped-up version of a box and whisker plot. In addition to seeing the median, quartiles, and min/max, you also get to see all of the individual data points as well as the density curves associated with the distributions.

Sankey Bar Chart

sankey bar chart

The SAS Sankey bar chart macro is an enhancement of a traditional stacked bar chart. In addition to showing how many subjects are in each category over time, this graphic also shows you how subjects transition from one category to another over time.

Other SAS graphics tools include a Beeswarm Plot (a strip plot with non-random jittering) and the Axis Macro for automating the selection of axis ranges for continuous variables. We are adding new SAS repositories frequently. We invite you to try the tools, share your feedback, and contribute to the development of the tools.

Visit Rho's Center for Applied Data Visualization

Webcharts: A Reusable Tool for Building Online Data Visualizations

Posted by Brook White on Wed, Jan 18, 2017 @ 01:39 PM
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This is the second in a series of posts introducing open source tools Rho is developing and sharing online. Click here to learn more about Rho's open source effort.

When Rho created a team dedicated developing novel data visualization tools for clinical research, one of the group's challenges was to figure out how to scale our graphics to every trial, study, and project we work on. In particular, we were interested in providing interactive web-based graphics, which can run in a browser and allow for intuitive, real-time data exploration.

Our solution was to create Webcharts - a web-based charting library built on top of the popular Data-Driven Documents (D3) JavaScript library - to provide a simple way to create reusable, flexible, interactive charts.

Interactive Study Dashboard

interactive study dashboard--webcharts

Track key project metrics in a single view; built with Webcharts (click here for interactive version)

Webcharts allows users to compose a wide range of chart types, ranging from basic charts (e.g., scatter plots, bar charts, line charts), to intermediate designs (e.g., histograms, linked tables, custom filters), to advanced displays (e.g., project dashboards, lab results trackers, outcomes explorers, and safety timelines). Webcharts' extensible and customizable charting library allows us to quickly produce standard charts while also crafting tailored data visualizations unique to each dataset, phase of study, and project.

This flexibility has allowed us to create hundreds of custom interactive charts, including several that have been featured alongside Rho's published work. The Immunologic Outcome Explorer (shown below) was adapted from Figure 3 in the New England Journal of Medicine article, Randomized Trial of Peanut Consumption in Infants at Risk for Peanut Allergy. The chart was originally created in response to reader correspondence, and was later updated to include follow-up data in conjunction with a second article, Effect of Avoidance on Peanut Allergy after Early Peanut Consumption. The interactive version allows the user to select from 10 outcomes on the y-axis. Selections for sex, ethnicity, study population, skin prick test stratum, and peanut specific IgE at 60 and 72 months of age can be interactively chosen to filter the data and display subgroups of interest. Figure options (e.g., summary lines, box and violin plots) can be selected under the Overlays heading to alter the properties of the figure.

Immunologic Outcome Explorer

immunologic outcome explorer using webcharts


Examine participant outcomes for the LEAP study (click here for interactive version)

Because Webcharts is designed for the web, the charts require no specialized software. If you have a web browser (e.g., Firefox, Chrome, Safari, Internet Explorer) and an Internet connection, you can see the charts. Likewise, navigating the charts is intuitive because we use controls familiar to anyone who has used a web browser (radio buttons, drop-down menus, sorting, filtering, mouse interactions). A manuscript describing the technical design of Webcharts was recently published in the Journal of Open Research Software.

The decision to build for general web use was intentional. We were not concerned with creating a proprietary charting system - of which there are many - but an extensible, open, generalizable tool that could be adapted to a variety of needs. For us, that means charts to aid in the conduct of clinical trials, but the tool is not limited to any particular field or industry. We also released Webcharts open source so that other users could contribute to the tools and help us refine them.

Because they are web-based, charts for individual studies and programs are easily implemented in RhoPORTAL, our secure collaboration and information delivery portal which allows us to share the charts with study team members and sponsors while carefully limiting access to sensitive data.

Webcharts is freely available online on Rho's GitHub site. The site contains a wiki that describes the tool, an API, and interactive examples. We invite anyone to download and use Webcharts, give us feedback, and participate in its development.

View "Visualizing Multivariate Data" Video

Jeremy Wildfire, MS, Senior Biostatistician, has over ten years of experience providing statistical support for multicenter clinical trials and mechanistic studies related to asthma, allergy, and immunology.  He is the head of Rho’s Center for Applied Data Visualization, which develops innovative data visualization tools that support all phases of the biomedical research process. Mr. Wildfire also founded Rho’s Open Source Committee, which guides the open source release of dozens of Rho’s graphics tools for monitoring, exploring, and reporting data. 

Ryan Bailey, MA is a Senior Clinical Researcher at Rho.  He has over 10 years of experience conducting multicenter asthma research studies, including theInner City Asthma Consortium (ICAC) and the Community Healthcare for Asthma Management and Prevention of Symptoms (CHAMPS) project. Ryan also coordinates Rho’s Center for Applied Data Visualization, which developsnovel data visualizations and statistical graphics for use in clinical trials.

Embracing Open Source as Good Science

Posted by Brook White on Wed, Nov 30, 2016 @ 09:37 AM
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Ryan Bailey, Senior Clinical ResearcherRyan Bailey, MA is a Senior Clinical Researcher at Rho.  He has over 10 years of experience conducting multicenter asthma research studies, including theInner City Asthma Consortium (ICAC) and the Community Healthcare for Asthma Management and Prevention of Symptoms (CHAMPS) project. Ryan also coordinates Rho’s Center for Applied Data Visualization, which developsnovel data visualizations and statistical graphics for use in clinical trials.

open source software in clinical researchSharing. It's one of the earliest lessons your parents try to teach you - don't hoard, take turns, be generous. Sharing is a great lesson for life. Sharing is also a driving force behind scientific progress and software development. Science and software rely on communal principles of transparency, knowledge exchange, reproducibility, and mutual benefit.

The practice of open sharing or open sourcing has advanced these fields in several ways:

We also feel strongly that the impetus for open sharing is reflected in Rho's core values - especially team culture, innovation, integrity, and quality. Given our values, and given our role in conducting science and creating software, we've been exploring ways that we can be more active in the so-called "sharing economy" when it comes to our work.

One of the ways we have been fulfilling this goal is to release our statistical and data visualization tools as freely-accessible, open source libraries on GitHub. GitHub is one of the world's largest open source platforms for virtual collaboration and code sharing. GitHub allows users to actively work on their code online, from anywhere, with the opportunity to share and collaborate with other users. As a result, we not only share our code for public use, we also invite feedback, improvements, and expansions of our tools for other uses.

We released our first open source tool - the openFDA Adverse Event Explorer - in June 2015. Now we have 26 team members working on 28 public projects, and that number has been growing rapidly. The libraries and tools we've been sharing have a variety of uses: monitor safety data, track project metrics, visualize data, summarize every data variable for a project, aid with analysis, optimize SAS tools, and explore population data.

Most repositories include examples and wikis that describe the tools and how they can be used. An example of one of these tools, the Population Explorer is shown below.

Interactive Population Explorer

interactive population explorer, clinical trial graphics

Access summary data on study population and subpopulations of interest in real time.

One of over 25 public projects on Rho's GitHub page - available at: https://github.com/RhoInc/PopulationExplorer

Over the next few months, we are going to highlight a few of our different open source tools here on the blog. We invite you to check back/subscribe to learn more about the tools we're making available to the public. We also encourage you to peruse the work for yourself on our GitHub page: https://github.com/RhoInc.

We are excited to be hosting public code and instructional wikis in a format that allows free access and virtual collaboration, and hope that an innovative platform like GitHub will give us a way to share our tools with the world and refine them with community feedback. As science and software increasingly embrace open source code, we are changing the way we develop tools and optimizing the way we do clinical research while staying true to our core purpose and values.

If you have any questions or want to learn more about one of our projects, email us at: graphics@rhoworld.com

Introducing Rho's Center for Applied Data Visualization

Posted by Brook White on Fri, Nov 21, 2014 @ 10:02 AM
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Our industry is driven by data. Every phase of our trials requires us to collect, monitor, analyze, and report data. While each of these steps is equally important, reporting is arguably the most impactful step. When we report data, we give them to key decision-makers and invite them to interpret the data and draw conclusions.
 
Is the trial being conducted correctly? Is participant enrollment on schedule? Are we protecting our participants' safety? Was the investigational product effective? Was our hypothesis confirmed? We rely on effective data reporting to answer these questions.
 
scatterUnfortunately, our industry doesn't always use the best tools or practices when it comes to data reporting. If you've ever had to make sense of 50 pages of data listings or spend hours creating figures using spreadsheet software, you know what we mean. If these methods feel outdated, it's because they are. We have been using the same basic technologies to report data for the past few decades with little improvement. The good news is that there are plenty of alternatives available to us and our industry is ripe for change.
 
Granted, some of the formats for reporting are mandated by formal regulations. We may not be able to do much about these reports, but many of the methods we use to report data are left up to us as clinical researchers. As such, we argue that clinical researchers have a beeswarmresponsibility to do the data justice and communicate them as clearly and effectively as possible.

What does this mean for our industry? It means looking for newer and better ways to communicate data. It means thinking carefully about how the method of reporting impacts perception and comprehension of data. It means researching novel technology tools for sharing data.
 
At Rho, we took these challenges to heart and created a new Center for Applied Data Visualization (ADV) to research and promote the best practices and tools for visualizing and reporting data. The ADV was founded by a team of senior biostatisticians, web programmers, and a study coordinator who have years of experience directly supporting sunburstclinical trials. This first hand experience with clinical research gave our team a unique perspective on the data reporting needs at all stages of clinical research from study design, to participant enrollment, monitoring, data collection, analysis, data exploration, to publication and reporting. Hence, the ADV marries clinical research experience with the technical skillset to create innovative, cutting-edge data visualizations in support of our research projects. Moreover, the ADV provides trainings throughout the company on graphics best practices and tool development.
 
In support of our projects, the ADV has developed dozens of novel graphics for both static reports and interactive web-based use. In both cases, the response from our clients and research partners has been overwhelmingly positive. Beginning this month, the ADV is expanding their focus to also provide resources external to Rho. Members of the ADV have been presenting their work and tools in public forums for years, but now we are moving toward releasing some of our tools, resources, and graphics research open source (free to use) on our new graphics sharing website: graphics.rhoworld.com. The site currently has two tools available, and additional graphics will be posted, and discussed here, on a regular basis.

Data visualization has tremendous potential to improve the way we communicate, understand, and interact with data. If you would like to learn more about Rho’s data visualization work, we would love to hear from you at: graphics@rhoworld.com

View "Visualizing Multivariate Data" Video

5 Tips for Creating a Request for Proposal (RFP) for Clinical Trial Services

Posted by Jamie Hahn on Tue, Jun 05, 2012 @ 11:46 AM
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tool for clinical trial servicesIf you’re looking for a contract research organization (CRO) to provide clinical trial services, chances are you’ll need to create a request for proposal (RFP). In the complicated world of outsourcing clinical trials, using RFPs to gather comparable bids from CROs can be incredibly challenging. The good news is, with a little planning and time, you can create RFPs that will reduce inconsistencies among bidders and ultimately help you identify the CRO that is truly the right partner for the job.

Here are five tips for creating RFPs that will help you compare “apples to apples” and help the CROs better understand your needs, values, and selection criteria for your clinical trial services:

1. Provide background info on your compound and program

2. Provide a protocol or protocol synopsis

3. Provide detailed RFP information to get consistent costs. Be specific. Some examples, might include:

  • Project specifications – What are the important details of your program? (Use our RFP specifications tool)
  • Project timelines – By when do you expect certain milestones to be met?
  • Responsibilities (CRO, sponsor, other vendors) – For which segments of your program do you need a CRO to provide clinical trial services?

4. Provide additional details. The more details you can provide the better.  It’s also OK to ask the CRO to make recommendations. You can tell a lot about a CRO by the recommendations they make and how they make them.  However, if you ask CROs to make recommendations be prepared for potential inconsistencies in the assumptions made and pricing offered between different CROs. The following are some additional details that might be helpful to bidders:

  • Paper or EDC?
  • Data output in CDISC format?
  • Provide site locations
  • Central or local labs?
  • Additional vendors (specialty labs, IRBs, translations, etc)?
  • Interim analyses, DSMBs, etc?
  • Automated subject randomization and clinical trial material re-ordering and shipping?

If you know you want to use certain specific vendors (i.e. you know you want to use Medidata RAVE for EDC), be sure to include that information.

5. Other items to request from CROs:

  • Project team CVs
  • Summary of team therapeutic experience and experience running similar trials
  • Relevant company information

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