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In the ever-evolving world of healthcare, the need for accurate, standardized data has never been more urgent.   From clinical trials to regulatory submissions, the management and sharing of clinical data play a pivotal role in the development of life-saving treatments and therapies.   One organization that has been instrumental in transforming the clinical research landscape is the Clinical Data Interchange Standards Consortium, or CDISC.   Through its global data standards, CDISC has become a cornerstone of the healthcare industry, making clinical trials more efficient and helping bring new drugs to market faster.   In this post, we’ll dive into what CDISC is, what it does, why it is vital for healthcare, and how SAS Institute is helping further this mission.   What is CDISC?   CDISC (Clinical Data Interchange Standards Consortium) is a global nonprofit organization that develops standards to ensure the quality, consistency, and efficiency of clinical research data. Founded in 2000, CDISC aims to facilitate the exchange, integration, and analysis of clinical data across different stakeholders such as pharmaceutical companies, regulatory agencies, and academic research institutions.   By implementing these standards, CDISC ensures that data from clinical trials can be easily shared and analyzed, promoting collaboration and speeding up the development of medical treatments.   CDISC’s standards are widely accepted by regulatory bodies like the U.S. FDA and the European Medicines Agency (EMA) and are used by companies and organizations around the world to ensure that clinical trial data is standardized, accurate, and interpretable.     Key CDISC Standards   CDISC has developed several key data standards, each designed for a specific part of the clinical trial process.   These standards are meant to harmonize the data collected across different stages, ensuring it is both structured and ready for regulatory review. Here are some of the key CDISC standards:   Study Data Tabulation Model (SDTM) SDTM is a data standard for organizing and formatting clinical trial data for submission to regulatory agencies. It ensures that clinical trial data is structured in a consistent way, making it easier for regulators to review and interpret. SDTM includes standardized formats for data related to adverse events, laboratory tests, demographics, etc. Analysis Data Model (ADaM) ADaM is designed to facilitate the statistical analysis of clinical trial data. It ensures that data is structured in a format suitable for statistical modeling and reporting, which is essential for evaluating the safety and efficacy of new treatments. Clinical Data Acquisition Standards Harmonization (CDASH) CDASH is a set of standards for designing Case Report Forms (CRFs), which are used by clinical trial sites to collect data from patients. CDASH ensures that these forms are standardized, making the data collection process more efficient and reducing the risk of errors. Controlled Terminology This standard provides a comprehensive list of terms that are used consistently throughout clinical trials. Controlled Terminology ensures that data is consistent, minimizing confusion and ensuring that terms are used accurately across different trials. Other Standards CDISC also develops standards for specific types of data such as biomarker data, lab data, and more. These standards ensure that all aspects of clinical research follow a consistent approach for easier analysis and integration.     Why is CDISC Important for Healthcare?   The importance of CDISC in healthcare, especially in clinical research, cannot be overstated.   Here are some reasons why CDISC standards are critical:   1. Improved Data Quality and Integrity Clinical trials generate massive amounts of data, which must be accurate and reliable for meaningful analysis. Without standardized formats, data from different sources can become inconsistent, making it difficult to draw valid conclusions. CDISC provides a framework for ensuring that clinical trial data is structured and complete, which increases the quality and integrity of the data being analyzed.   2. Speeding Up Drug Development CDISC helps accelerate the development of new therapies. By standardizing data collection and submission formats, CDISC makes it easier and faster for pharmaceutical companies to submit clinical trial data to regulatory authorities. Regulatory agencies such as the FDA or EMA prefer data submissions in CDISC formats, which speeds up the review process and helps get life-saving treatments to market more quickly.   3. Better Collaboration In clinical research, trials often involve multiple stakeholders, including researchers, pharmaceutical companies, and regulatory bodies. Standardized data makes it easier for all these parties to collaborate. With CDISC standards, data can be shared seamlessly across different systems, enabling researchers and regulators to work together more efficiently, ultimately improving patient outcomes.   4. Ensuring Compliance with Regulatory Requirements Regulatory agencies worldwide, such as the FDA and EMA, require clinical trial data to be submitted in standardized formats to ensure consistency and transparency. CDISC standards provide the frameworks that help ensure compliance with these regulations. By adhering to these standards, pharmaceutical companies reduce the risk of delays or issues with regulatory submissions.   5. Global Impact on Healthcare CDISC's global data standards facilitate the exchange of clinical trial data across borders. With the use of standardized formats, researchers can pool data from diverse populations and regions to gain more comprehensive insights into the safety and efficacy of treatments. This is crucial for ensuring that medical treatments are safe and effective for people around the world.     SAS Institute and CDISC Standards   SAS Institute is a global leader in analytics software, and it plays a significant role in the implementation and adoption of CDISC standards.   SAS's powerful tools help clinical research organizations manage, analyze, and report data in CDISC-compliant formats, ultimately enabling faster and more efficient drug development processes.   SAS Institute authored a comprehensive guide on how to implement CDISC using SAS technology. An excerpt can be seen here.   How SAS Supports CDISC Standards   Software Solutions for Data Management and Analysis SAS provides software that allows clinical trial data to be transformed into CDISC-compliant formats such as SDTM and ADaM. These tools automate the process of data conversion, analysis, and reporting, helping organizations comply with CDISC standards without spending valuable time on manual data preparation. Faster Regulatory Submissions SAS's tools help pharmaceutical companies streamline the process of regulatory submissions. By ensuring that clinical trial data is organized and formatted according to CDISC standards, SAS facilitates quicker submission to regulatory authorities, ultimately speeding up the approval process for new drugs and treatments. Training and Resources SAS offers training programs and support for organizations looking to implement CDISC standards. These resources are designed to help clinical research teams understand the best practices for data management and analysis in compliance with CDISC. Collaboration with Regulators SAS works closely with regulatory agencies like the FDA, EMA, and other health organizations to ensure their solutions are aligned with current regulatory requirements. This collaboration ensures that clinical trial data, once analyzed using SAS tools, meets all regulatory expectations, helping to avoid delays in approval.     Case Studies: The Real-World Impact of CDISC   Case Study 1: Accelerating Cancer Drug Approval A major pharmaceutical company conducting a phase III clinical trial for a new cancer drug faced challenges with harmonizing data from multiple trial sites across the globe. The use of CDISC standards, particularly SDTM and ADaM, allowed the company to organize, analyze, and submit data quickly and efficiently. As a result, they were able to present their findings to the FDA faster, leading to quicker approval and the drug reaching the market months earlier than originally expected.   Case Study 2: Streamlining Diabetes Drug Development Another pharmaceutical company working on a diabetes treatment faced delays in analyzing and submitting clinical trial data. By adopting SAS's CDISC-compliant tools, they were able to quickly convert their raw clinical data into SDTM and ADaM formats, reducing the time spent on data cleaning and making the submission process more efficient. This enabled a faster review process and a shorter time to market for the new treatment.     Conclusion   In the complex world of clinical trials, CDISC plays a vital role in ensuring that clinical data is standardized, organized, and ready for regulatory review.   By adopting CDISC standards, pharmaceutical companies and researchers can improve data quality, accelerate drug development, and ensure compliance with regulatory requirements.   With the support of SAS Institute, the implementation of CDISC standards becomes more streamlined, enabling clinical trials to move forward more efficiently and bringing life-saving treatments to market faster.   The future of clinical research is rapidly evolving, and CDISC's data standards are helping to pave the way for a more collaborative, efficient, and patient-focused healthcare ecosystem.   CDISC Website (https://www.cdisc.org/) Implementation CDISC Using SAS: An End-to-End Guide, Revised Second Edition (https://support.sas.com/content/dam/SAS/support/en/books/implementing-cdisc-using-sas-revised-second-edition/73038_excerpt.pdf)     Find more articles from SAS Global Enablement and Learning here. ... 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  SAS Analytics plays a crucial role in the Health and Life Sciences (HLS) sector by providing powerful data analytics tools that help organizations make informed, data-driven decisions. These organizations include healthcare providers, pharmaceutical companies, medical device manufacturers, researchers, and regulatory agencies. SAS (Statistical Analysis System) enables these entities to improve patient outcomes, optimize operations, streamline R&D, and ensure compliance with regulatory standards. Below are key areas where SAS Analytics is making a significant impact, along with real-life examples.   1.    Clinical Trials and Drug Development One of the most critical areas where SAS is used in health and life sciences is in the design, execution, and analysis of clinical trials. SAS provides tools for data management, statistical analysis, and reporting that are required by regulatory bodies like the FDA and EMA.   Examples:   AstraZeneca: The pharmaceutical company used SAS analytics to streamline its clinical trial processes, improve the efficiency of data analysis, and ensure compliance with regulatory standards. SAS tools helped AstraZeneca identify key biomarkers in clinical trials, accelerating the development of drugs such as its COVID-19 vaccine.   https://www.sas.com/en_us/news/press-releases/2023/september/ai-analytics-partnership-life-sciences-astrazeneca.html     Rapidly Emerging Antiviral Drug Development Initiative (READDI): The biotechnology company READDI has partnered with SAS since 2021 to apply the most advanced technologies including SAS Viya to transform the drug development process. The goal is developing broad-spectrum, small molecule antiviral drugs before they’re needed, instead of starting from scratch when a new virus emerges.   https://www.sas.com/en_us/customers/readdi.html     2.    SAS Advanced Analytics on hosted cloud for scalability and cost saving SAS hosted advanced analytics in the cloud helps international pharmaceutical companies enhance operations and efficiency. SAS provides A secure analytics foundation and scalable framework for drug development and submission.   Example:   Chiesi Group: The pharmaceutical group uses SAS® Viya® to analyze information in a collaborative platform, streamline processes and efficiently deliver trial results to regulatory authorities We also have improvements in the way of working – efficient data exchange with suppliers, standardization of processes, as well as how we document the flow of data to be in full compliance with regulatory requirements. From the audit point of view, we feel much more protected.   https://www.sas.com/en_us/customers/chiesi-farmaceutici.html     3.    Health System Integration and Data Interoperability and Prediction for disease prevention SAS supports healthcare organizations in integrating disparate data sources, such as EHRs, claims data, and social determinants of health, into unified systems. This integration enables better decision-making and the creation of holistic patient care plans.   Example:   New Zealand Ministry of Health: The Ministry of Health in New Zealand wanted to standardize care for diabetes across the country. Knowing the who, informs the what. The team linked six data sources and integrated those sources with a patient's assigned heath number. They used SAS Analytics and Identified cohorts with increased prevalence rate. The outcome is that they can now predict who may be at risk to ensure appropriate resources are available pre-emptively and consistently.   https://www.sas.com/en_us/customers/new-zealand-ministry-health.html     4.    Fraud Detection and Compliance In the healthcare industry, fraud is a significant issue, particularly in billing and insurance claims. SAS’s machine learning and anomaly detection algorithms can identify fraudulent activity by analyzing large amounts of transactional data. Additionally, SAS helps healthcare organizations stay compliant with regulatory standards (like HIPAA in the U.S. and GDPR in Europe).   Example:   BUPA: Bupa UK Insurance uses SAS analytics to help deliver excellent, affordable health care and prevent insurance fraud. For more than 20 years, Bupa UK Insurance has driven its core data analytics processes – from anti-money laundering activities to financial planning and performance monitoring – using SAS analytics. During this time, the Analytics and Data team developed a broad range of models and dashboards to track key operational metrics.   https://www.sas.com/en_us/customers/bupa.html     5.    Operational Efficiency and Cost Management SAS helps healthcare organizations optimize operational workflows, improve resource allocation, and reduce costs. The platform supports analytics related to patient flow, staffing, supply chain management, and predictive analytical maintenance.   Example:   Siemens Healtineers: The predictive service and maintenance component of the system, designed using SAS, helps make sure throughput is uninterrupted and productivity remains as high as possible. The effectiveness of the SAS system helped the siemens team manage 58 system and process components and monitored proactively. They achieved 36% less system downtime compared to the reactive service; Optimized service technician deployment, because they know in advance which parts are needed at a particular location.   https://www.sas.com/en_us/customers/siemens-healthineers.html     6.    Single trusted version of the truth in the cloud SAS can analyze data from electronic health records (EHRs) and other sources to monitor patient outcomes, identify trends, and optimize treatment protocols. By leveraging analytics, healthcare providers can offer higher-quality care, improve patient satisfaction, and reduce unnecessary hospitalizations.   SAS also assists organizations in the life sciences sector by helping them comply with regulatory requirements. This includes generating reports for regulatory bodies, ensuring data integrity, and maintaining audit trails for compliance purposes.   Example:   Santen: SAS helped Santen use cloud-based analytics from SAS to achieve help developing new ocular therapies faster Team members around the world can easily access the same version of data, thereby enhancing collaboration, drug studies and development. Individual tasks and data access can be granted based on job role and function. Management can track the workflow and maintain a history of it. Compliance data packages for new drug submissions are prepared efficiently.   https://www.sas.com/en_us/customers/santen.html     Conclusion   SAS Analytics in health and life sciences plays a pivotal role in transforming how organizations approach patient care, drug development, operational efficiency, and regulatory compliance.   The use of advanced analytics helps the sector make significant strides in improving outcomes, reducing costs, and advancing innovation.   Through real-world examples such as those presented in this paper, and from what can be found in HealthCare here and from Life Sciences here, it's clear that SAS is not only streamlining processes but also saving lives and improving the quality of healthcare worldwide.     Find more articles from SAS Global Enablement and Learning here. ... View more

So long, so expensive!   The drug approval process.   There is an old joke in the pharmaceutical industry.  I paraphrase:   What’s a drug’s favorite game? "Hide and Seek"—because it takes years to find its way to the pharmacy!   There are many minor daily mishaps such as missing your bus or spilling coffee on yourself before that important meeting which can feel frustrating but often don't have lasting consequences.   The detrimental unintended consequences of a new drug’s effect on humanity will be the opposite of a minor daily mishap; this will be life or death.   That is why the drug approval process has so much rigor built into it.   Let’s explore!   Figure 1 Drug approval process - Follow Link 1. in Supporting material below   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   The Data and Analysis pieces are what we are interested to optimize using SAS. These are in order:   The Investigation New Drug (IND) process:     Investigational New Drug (IND) application is submitted to regulatory agency based on the results from initial testing to determine whether drug/biological product can be tested on humans.   SAS can make this quicker by helping create:   Single version of data truth and workflow, permissions and regulatory reporting and management (SAS special talent) Explanatory Analytics (SAS special talent) Exploratory Analytics (SAS special talent) Predictive and future based analytics (SAS special talent) Optimization analytics (SAS special talent) Visualization, Dissemination and workflow of data single version of the truth and all analytics (SAS special talent).   Phase 1 - First In Human Study (FIH)     This is the first-in-human (FIH) study. It focuses on a drug’s side effects, optimal dosage ranges, and how it is metabolized in the body.​   SAS can make this quicker by helping create:   Single version of data truth and workflow, permissions and regulatory reporting and management (SAS special talent) Explanatory Analytics (SAS special talent) Exploratory Analytics (SAS special talent) Predictive and future based analytics (SAS special talent) Optimization analytics (SAS special talent) Visualization, Dissemination and workflow of data single version of the truth and all analytics (SAS special talent).   Phase 2 - Validity testing.  Retesting results from Phase 1 for validity     Preliminary data is obtained on whether the drug works in people with a certain disease or condition. Safety and short-term side effects are evaluated.​   SAS can make this quicker by helping create:   Single version of data truth and workflow, permissions and regulatory reporting and management (SAS special talent) Explanatory Analytics (SAS special talent) Exploratory Analytics (SAS special talent) Predictive and future based analytics (SAS special talent) Optimization analytics (SAS special talent) Visualization, Dissemination and workflow of data single version of the truth and all analytics (SAS special talent).   Phase 3 and pre NDA meeting - More Data, more complex testing, more NDA Compliance     Phase 3 is a larger-scale phase that gathers additional regarding the safety and efficacy of the drug and studies different populations. Furthermore, regulatory agency gathers in a Pre-NDA/BLA meeting to analyze results, combined and compared with the previous phases. This is where the single version of the truth comes into its own.   SAS can make this quicker by helping create:   Single version of data truth and workflow, permissions and regulatory reporting and management (SAS special talent) Explanatory Analytics (SAS special talent) Exploratory Analytics (SAS special talent) Predictive and future based analytics (SAS special talent) Optimization analytics (SAS special talent) Visualization, Dissemination and workflow of data single version of the truth and all analytics (SAS special talent).   The NDA Submission - And all of that culminates into the New Drug Application (NDA) to the regulatory agency(s).     New Drug Application (NDA) is submitted to the regulatory agency to seek approval to market the drug.​   The drug sponsor submits the NDA, or the New Drug Application, to the regulatory agency to formally ask for the agency’s approval to market the new drug.   The NDA includes all the animal and human data collection and analyses as well as information about how the drug behaves in the human body and how it is manufactured. ​ ​   It is important to note here that as a SAS Clinical Programmer, you are responsible for gathering all the data from phases 1, 2, and 3 and generating all the results that are to be submitted as a part of the NDA.   This requires submission of all the results from the 3 phases.   This is why you need SAS; all you have done up to this point leads you to a submission.   If you do this with SAS, the single data version allows you to re-use and build on previous phases, so things happen quickly.   The analysis and preparation allow you to cut analytical mistakes, to show a yellow brick road of metadata that led you to all conclusions, and the regulating agencies accept SAS as a way to look at results already, so you don’t need a lot of reformatting to submit.   Phase 4 - And just when you thought you were done.....  Long term study     Since it is not possible to predict all of a drug’s effects during clinical trials, safety issues after the drug goes on the market are monitored during this phase. The sponsor, typically the manufacturer, is required to submit periodic safety updates to the regulatory agency. The role of the agency’s post-marketing safety system is to detect serious unexpected adverse events and take definitive action when needed.   One system through all the stages.   The detrimental unintended consequences of a new drug’s effect on humanity will be the opposite of a minor daily mishap; this will be life or death.   Using rigorous data management practices and rigorous analytical processes and rigorous regulatory compliance preparation, you can err on the side of life, in a quicker and cheaper way.   SAS Life Science Analytics framework.   Supporting Material   https://conquer-magazine.com/issues/special-issues/january-2021-a-patient-guide-to-recent-fda-approved-oncology-drugs/2021-fda-drug-approval-process-infographic            Find more articles from SAS Global Enablement and Learning here. ... View more

So what did we do?   Researchers often encounter difficulties in comprehending and integrating information from disparate protocols, hindering the progress of scientific inquiry.   This use case is aimed at creating a Unified Studies Definition Model (USDM) by translating diverse human-readable protocols into a standardized format.   The envisaged solution combines the versatility of Excel spreadsheets and the efficiency of Natural Language Processing (NLP) techniques to bridge the gap between heterogeneous study designs.   The primary objective of this use case is to   enhance the clarity, accessibility, and interoperability of research protocols   This should lead to development of a standardized, machine-readable representation USDM.   The following steps are used in the use case:   Used contextual information extraction from a couple of clinical trial protocols (early and late phase) and stored the relevant info into the USDM (excel) workbook ... in the right place (in the right field). Key steps included contextual information extraction from clinical trial protocols and ensuring scalability to handle various protocols. Employing SAS Natural Language Processing (NLP) techniques and Large Language Models (LLM's); automatically translate human-readable study protocols into a structured format. This involves extracting key information such as study objectives, methodologies, inclusion/exclusion criteria, and outcomes. A robust approach has been developed, combining natural language processing (NLP), text analytics and a large language model (LLM) to handle clinical trial protocols.  Whenever LLMs are involved, it's essential to discuss cost, security, and privacy. However, as a life sciences advisor, the primary concern is: "How can I trust a generative LLM to deliver reliable, non-hallucinated results? How can I establish guardrails to ensure this? How do I address the security and privacy of my data?"    Evaluating Gen AI and Text Analytics for creating a USDM   Some initial results after pre processing, highlights the extractions of inclusion and exclusion criteria by LITI rules and LLMs.      Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.     There are several challenges to overcome, such as overfitting in LITI rules and the need for extensive pre-processing.   The use of various Python packages for splitting protocols into chunks and extracting tables and images were investigated.    Preliminary Outcomes: The use of LLMs like LLAMA-V2 and RAG-for-LLMs proved beneficial, though resource-intensive. Leveraging LITI rules as pre-filters, for confidence scoring, to reduce costs by pre-filtering data and thereby leading to quicker and accurate results seems beneficial.    A bit more on LITI rules for confidence scoring: Confidence Scoring in SAS Viya VTA – LITI    The same corpus used for information extraction with the LLM is scored against the LITI rule to verify the presence of the information.   A confidence score, ranging from 0 to 1, is calculated based on the number of relevant terms matched by the LITI rule.   Higher scores indicate better extraction quality, while lower scores suggest hallucinations or inaccuracies.   This method provides a robust metric to evaluate different LLM models or prompts.  (5 steps to improve information extraction using trustworthy generative AI - The SAS Data Science Blog)    Benefits of integrating SAS NLP and Gen AI for creating a USDM   Avoiding hallucinations: The NLP and text analytics pre-filtering process assimilates the most relevant source data from various documents, ensuring the outputs are more accurate and reliable.  Enhancing time to value: By pre-filtering the data, a smaller LLM can handle GenAI tasks more efficiently, leading to quicker results. Providing more focused context to an LLM significantly enhances output quality, especially for weaker models.  Ensuring privacy and security: Using a local vector database for fine-tuning generative models is possible. This gives users only relevant embeddings to the LLMs via APIs or localized instances of the LLM, ensuring the privacy and security of sensitive data.  Reducing costs: Text analytics and NLP significantly reduce the amount of information sent to the LLMs. In some cases, only 1 – 5% of the overall data is used for answers. This eliminates the need for excessive external API calls and reduces the computational resources required for localized LLMs. Processing documents with GPT-4 involves significant costs. For documents ranging from 3,500 to 8,000 tokens, processing 1,000 documents costs between $105 and $240 for input alone. The output cost is often 2-3 times higher per token than the input. With GPT-4 pricing at $30 per million tokens, these costs can add up quickly.  Traceability: SAS® Viya® enables end-to-end verification and traceability of results, helping users to verify information and trace it back to the statements from which the summaries were derived, potentially thousands of statements. This traceability feature enhances transparency and trust in the generated outputs.  Establishing guard rails: establish guard rails to control the information that’s sent to the LLMs and also analyse the output – quality checks​. Adopting a “trust but verify” approach ensures that LLMs’ extractions, which can impact downstream tasks, are checked and validated to prevent unchecked errors.    So what? - A few open ended conclusions   The work highlights the potential of advanced data management techniques in transforming clinical trial protocols.  By embracing innovative technologies and collaborative efforts, the industry can achieve greater efficiency and accuracy in clinical research.  These examples merely hint at the vast potential unlocked when combining the precision of linguistic methods in SAS NLP with LLMs. These techniques not only tackle quality issues in text data but also integrate subject matter expertise, granting organizations significant control over their corpora. In some instances, the corpus size for fine-tuning can be reduced by up to 90%. By curating higher-quality data for fine-tuning, you can achieve more accurate responses from LLMs, reduce the occurrence of hallucinations, and establish a method for validating responses.    References:   *Presented at the 2024 Europe CDISC+TMF Interchange and SAS Innovate 2024, Jasmine Kestemont from Innovion and Stijn Rogiers from argenx presented their groundbreaking work on translating human-readable protocols into machine-readable formats using the Unified Study Data Model (USDM).    *SAS Generative AI explained (GenAI)     Find more articles from SAS Global Enablement and Learning here. ... View more

Why Trust?   The one issue that persist with any manipulated data output is trust.    In an interesting article I read recently, published by Caltech Science Exchange, the question of Trust came in in context of AI. (Can we trust artificial intelligence)   According to this author, one of the big impactful events influencing trust is Data and amongst other issues, the Bias it contains.  And their fix, make sure every data set used to answer any question is scrutinized to determine its ‘motivation, composition, collection process, recommended uses and so on’ So What?   Essentially, moving back into the safety of single version of the data truth will show us the picture of whence our intelligence is derived. Results are easily manipulated if there is no single version of data for everyone. Results can be skewed where there is no oversight, no process, no checks and balances to ensure auditability and traceability. In other words, if you want trust, we need to build a world where even under data torture and/or duress, this singular data version will provide repeatable, reliable ‘truths’ if the same questions are asked by whoever.   Enter SAS Clinical Acceleration Repository (CAR).   SAS CAR lets you easily integrate large and diverse data sets such as electronic data capture (EDC) systems, in-house clinical data management systems (CDMS), labs and contract research organizations (CROs).   The CAR Solution from SAS is helping you trust your version of the truth. Some of its primary features includes but are not limited to: Centralized global repository. It consolidates clinical information into a single, secure, centralized global repository Data Tracing. It traces data pedigree back to the origin. Data Security. Defines data check-in/check-out processes. Maintains audit trails (see number 4 below). Provide you with Electronic Signatures, so the individuals that touch data must make themselves known. Supports versioning; and allows for role-based privileges (I’ll show how easy this is to set up a bit later). Audit Trails. Let’s you readily determine what audit changes were made, when and by whom for all the content stored in the repository. Secure Logins. Controls all information and research team access via secure logins. Regulatory Compliance. Enables compliance with the FDA’s Title 21 CFT part 11 requirements, as well as other industry regulations. CDISC compliance. Complies with CDISC and its initiatives – dataset-JSON and CDISC CORE. Medical Device Reporting. Supports integration with MDR. Cloud Native too?   Yes, SAS Clinical Acceleration Repository (CAR) is open and cloud native. Lets Wrap SAS CAR up. With all said and done, if you remember only three things about SAS CAR it is this:   Data Integrity   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Open Repository   Easy to use   Next Steps Come and ask for a Demo, give us a try, this may be the game changer you have been seeking.   SAS Clinical Acceleration Main Page: https://www.sas.com/en_us/software/clinical-acceleration.html   Request a Demo: https://www.sas.com/en_us/software/how-to-buy/request-price-quote.html   ... View more

  Is that a good site?   This is a question most frequently asked when designing an Enrollment plan for clinical trials, and determining the answer without analytics to help may be a daunting task for sure.   Before we dive too deeply into the solution, lets figure out what we are trying to solve for.   Clinical trials ultimately aim to answer two simple questions: Is the drug or device safe? and Does it do what it’s supposed to do? This can take decades and cost astronomical amounts…take the case of Tamoxifen for Breast Cancer   A clinical trial started in January of 1994 and is projected to complete only in September of 2024 being conducted by the UK’s Queen Mary University of London.   The trial enrolled an astonishing 7,154 women aged 35-70 years across nine countries to investigate if tamoxifen, a hormonal drug, can prevent the development of breast cancer in high-risk women.   And herein lies the opportunity.   Question: How can I accelerate the speed and cut the cost of Clinical Trials.   Answer: Simply by using the analytics that is embedded in the SAS® Clinical Enrollment Simulation product to create your Enrollment Plans for your clinical trial.   But it will be daunting and complex to build an executable enrollment strategy.  The factors used as input are myriad and complex.   The graphic below details the factors that is typically considered when building a representative and executable Enrollment Plan.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.         These factors are the things that, when manipulated and fine tuned just the right amount, ensure that your Enrollment Plan is on budget and in time.   Why is manipulation of the input variables important?   Some estimations from Statista.com put the cost per clinical trial participant at upwards of $30,000.  Dependent on the clinical trial that is.  The graphic below shows some clinical trial costs running into the multiple hundreds of thousands dollars.  Phew!.   That means that if we take the lowest cost of $30,000 per patient, the Tamoxifen Breast Cancer study mentioned in the opening has cost 7,154 times $30,000, so $215 odd hundred million so far.  That is not a number, it is a SOUND... something like aaaaaaaghhhhhh!.           Two approaches to manipulating the input variables   There are two approaches ways to manipulate the factors that drives the success of a Enrollment Plan. Hint: only one of them is flexible and simple. Projecting Enrollment approach (a.k.a - Guesswork based on previous data) Simulate Enrollment approach (a.k.a – Get statistics to optimize the best potential success) Projecting Enrollment Approach       Key challenges from the traditional approach:   Working backwards form the goal is really trying to make a scenario that can justify the goal we are trying to hit. It does not always have a chance of actual execution and success. It uses actual value inputs. No room for variability.  Let’s say for instance group A will be ready in 5 months but really, we know it will be sometime between 4.5 and 7.  That variation is not able to be considered. Enrollment decay is not considered. What typically happens: Dr tells you she can plan on enrolling 15 patients per month.  You know that is optimistic, so you enter an actual value i.e., 12 to be safe. Again, there is not accounting for actual decay as the low figure is hard coded for the entire time. Never get anyone enrolled. This is probably an underestimate, and closer to 30% never enroll anyone.  The coordinator leaves the clinic, so no one runs the trial. A competing trial starts up and they send their patients to that one.  The site just has delays in paperwork and just gives up It can take a long time to create and compare multiple scenarios to determine the impact of changing country and/or site profiles using Excel, so Cross regional data and costs typically are siloed. This approach provides no measure of risk – for completing the trial on time or on budget. What-if scenarios. No insight or corrective measures regarding the actions needed to keep a trial on track. Simulate Enrollment Approach   SAS® Clinical Enrollment Simulation can accurately simulate and model the complex variabilities and probabilities associated with enrolling subjects in clinical trials, including country and site selection using analytics.   The three main benefits of using the SAS solution for running your Clinical Enrollment are detailed in the three graphics below.       The SAS Enrollment Simulator allow you to calculate the chance of success - what I really want to know is “am I going to hit my targets.” Simulation allows you specify what targets matter, then we can look at what we know about the sites and see how likely we are to achieve the study goals.         The SAS Enrollment Simulator allow you to compare various scenarios with the Milestone achievement capability. This allows us to see ‘what happens if’.  We can play with different scenarios to give us the biggest likelihood of achieving success in our clinical trial.       The SAS Enrollment Simulator allow you to intervene at any time and play out scenarios for intervention when you need to ‘rescue’ a clinical trial site.   For instance, in this scenario we can see that by adding two extra ‘rescue sites’ bumps my chances of getting back on target to 75% and adding three to 95%.   This allow me to make a business decision to see whether spending the money will help me be successful or not, and by what percentage point.   To learn more Education: SAS Clinical Enrollment Simulation education course Help Files: SAS Clinical Enrollment Simulation help files  Support: SAS Clinical Enrollment Simulation Documentation and Support Product Page: SAS Clinical Enrollment Simulation Product Page   ... View more