CDA - Leveraging Analytics Clinical Trials White Paper

Addressing the Challenges in Clinical Development with

Data-Driven StrategiesA Guide for Pharmaceutical Executives

2 White Paper: Addressing the Challenges in Clinical Development with Data-Driven Strategies

Table of Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

The Changing Imperatives of Clinical Development . . . . . . . . . . . . . . . . . 3

Applications of Clinical Development Analytics . . . . . . . . . . . . . . . . . . . . . 4

Clinical Sciences & Study Feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Study Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Strategic Operations Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Key Capabilities for Clinical Development Analytics . . . . . . . . . . . . . . . . . . 5

Data Infrastructure & Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Data Governance for the Integrated Data Hub . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Roles and Skills Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Data and Analytics Use Cases for Integrated Clinical Trial Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Why Saama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Addressing the Challenges in Clinical Development with Data-Driven Strategies: White Paper 3

SummaryClinical development is a pillar of the pharmaceutical enterprise, and clinical research studies, which lead to the approval of new treatments, are the gold standard for treatment safety and efficacy. Traditionally, sponsors have engaged in the accepted approach of numerous pre-clinical candidates that subsequently dwindle down to a single promising compound approved for a single indication. Throughout this process, sponsors have had to overcome the exhaustive process of trial decision and design, study conduct, and manage the complex network of stakeholders that add to the immense time and cost of clinical trials. With the emergence of advanced technologies in big data and data science, the pharmaceutical enterprise is pivoting towards technology solutions for the advanced monitoring of clinical trials. Ultimately the objective of adopting data-driven solutions is to drive the timely and cost effective completion of studies and advancement of candidates that avoid phase III attrition.

Information systems today can retrieve and process disparate data, allowing pharmaceuticals to focus on the business intelligence needed for clinical development. More recently, these approaches have become main-stream with the use of data warehouses and reporting on Key Performance or Risk Indicators with incremental valuable benefits to display. Given the increasing dominance of scalable/distributed systems for data compute and store (Hadoop) as well as the refinement of processes to the tracking and refinement of studies (risk based monitoring), the collection and derivation of data into information is occurring at an unprecedented rate. Pharmaceuticals are now driven to achieve the next round of advancements in the clinical development space by designing studies with a successful pre-dis-position, proactively addressing trends of their open studies, and transforming the processes by which the many entities work together with next-generation Clin-ical Development Analytics. Pharmaceutical companies are recognizing this paradigm shift and are investing in analytic capabilities that will facilitate the overall success of their portfolios and pipelines, help them comply with regulatory agency guidance, and increase the efficiency of clinical trial operations.

The Changing Imperatives of Clinical DevelopmentRecent advances in clinical research have led to huge innovations by pharmaceutical manufacturers in creating new drugs used to treat a variety of complex medical conditions. In turn, this has caused a fairly dramatic increase in the complexity and cost of clinical studies needed to prove efficacy and safety of the drugs and gain regulatory approval. The traditional clinical development approach has been falling short for a variety of reasons:

1. Growing complexity: The increased complexity of clinical trials means that the traditional “brute force” approach of manually verifying every data point is becoming less feasible.

2. Rising costs: There is increased pressure on pharma manufacturers to manage rising costs. This is bringing greater scrutiny to trial monitoring costs which have been rising fast. Pharma manufacturers are challenging their clinical operations teams to do more with less.

3. The need for a better approach: There is a growing body of evidence that despite a high level of effort in data management and monitoring, the problem is beyond the simple calculation of metrics. With increased pressure to produce drugs that can be commercialized and stand differen-tiated in an ever-increasing pool of generics, the stakes are too high to continue with the high-cost and low yield of traditional clinical development.

4. Regulatory pressure: Agencies have come on board with the need to change how clinical study monitoring is done. Both, the FDA in the US and the EMA in Europe have issued guidance for sponsors to adopt a comprehensive quality management approach to clinical trial monitoring, including integrating a risk based analysis, centralized statis-tical methods and on site monitoring into the trial management cycle.


Applications of Clinical Development Analytics

Clinical Sciences & Study FeasibilityClinical studies produce a wealth of information about the investigational products they evaluate and the various mechanisms by which they are measured for safety or efficacy. Typically, these studies are faced with the task of tracking information from new trials, comparing it to historical trials and forming new knowledge around the investigational products under their charter. Enhanced access and to the data sets of old and new studies along with improved analytic mechanisms can foster strategic approaches to the clinical development. Historical study data stand as good data points for comparing novel investigational products and help determine go/no-go decisions on which products or portfolios to invest in; resulting in a lower-risk pipeline.

Benefits of employing Data-Driven Clinical Sciences

1. Centralized study data organized in a canonical model facilitates accelerated time to insight of processes for Clinical Development Decision Support as, Improved Study Design, and Investiga-tion/Translation. When teams are freed up from the process of ingesting, organizing, and aggregating data into information, they are free to perform high-impact activities.

2. Clinical Development Decision Support (CDDS) can highly impact the pharmaceutical enterprise. Each study is one step forward in accruing more data on an investigational product and this data can be used to determine if the drug will fare well in up-coming phases. When a current ongoing study is compared to a historical study of a similar design, analysis of the underlying data can point out of the current study will effectively meet its endpoints.

3. The foundational mechanisms of CDDS can also facilitate better study design by better study feasi-bility. When trending of outcomes and measures between studies is established, study populations for target indications and their outcomes are char-acterized. This characterization can help isolate (via

inclusion and exclusion criteria) the ideal popula-tion of subjects that are more likely to positively respond to an investigational drug while keeping their safety and tolerability in check. This can lead for the development of more targeted protocols that detail therapies more likely to succeed without amendments. In turn, the sites that are high performing for enrollment of these types of study are prime candidates for recruiting the character-ized patients.

Study MonitoringModern approaches to clinical trial monitoring employ a combination of “centralized” monitoring activi-ties paired with the traditional “on-site” monitoring. Broadly, RBM (Risk Based Monitoring) can be thought of as the risk management approach sitting on top of the combined manual and centralized monitoring strategy.

Centralized monitoring refers to the capability to analyze this data on an ongoing basis to better inform study monitors on critical trial risks and issues. Advances in data science, statistical capabilities and advanced analytics algorithms have made it possible to process the vast amount of data being collected to

Data/Analytics Drivers

Clinical Sciences & Study Feasibility

Strategic Operations

Management

StudyMonitoring

Applications of Clinical Development Analytics


identify patterns which could indicate various types of risks which can be investigated further.

The key benefits of employing RBM are:

1. Central analysis of data leads to better identifica-tion and management of critical trial risks which can be better monitored and mitigated. Applying techniques such as trending and outlier detection on site level trial data leads to an improved ability to detect data quality issues which cannot be surfaced through source data verification alone. Thus,

2. RBM expends effort proportionally in areas with highest criticality rather than uniformly across the trial. RBM is inherently adaptive. It creates a feed-back loop which allows the trial manager mitigate issues in cycle time, timeliness, quality, and cost.

3. RBM utilizes leading indicators to enable the trial manager to be predictive rather than reactive in the use of metrics for monitoring.

Strategic Operations ManagementIn any given clinical trial, there are numerous enti-ties partaking in the complex process. The sponsor (pharmaceutical) engages with contract research organizations (CROs), Institutional Review Boards (IRBs), the Food & Drug Administration (FDA), internal clinical advisory boards, internal clinical develop-ment teams, internal clinical operations teams, and biostatistics teams. Each of these entities forms the network of teams that appropriate checks, balances, and approvals of the clinical trial process. The stages of study startup, conduct, and closeout are riddled with key milestones, deliverables and deadlines. Each day a clinical trial is delayed, the cost to the sponsor is estimated to around $8 million dollars. Enacting the same data-driven approaches aforementioned can help sponsors cut costs and drive productivity.

Key benefits of employing Strategic Operations Management

1. CROs have long understood their crucial role has protocol executors and data generators. Their performance as contractual entities has

low bearing on their financial incentivization. This has caused sponsors to just pay the demanded price for services and bear the costs of any delays or lower quality services. CRO oversight through central monitoring initiates a collaborative partner-ship between the sponsor and CRO. This alliance can be established in the shared benefits and risks model that allows both parties to benefit from high-performance. When studies are executed on time (i.e. meeting enrollment targets on time), CRO’s can be financially incentivized to continue this performance. In turn, the sponsor benefits from lower operating costs for their studies with the partnering CRO.

2. Based on accrued interactions between the stakeholders in the network, advanced analysis of the data can pinpoint areas most prone to delay or in-need of focus. Extending the analyses to predictive modeling can determine estimated effect of delays on costs and downstream milestones. Further refinement into prescriptive analytics can utilize modeling to recommend the best course of action based on the scenario.

Key Capabilities for Clinical Development AnalyticsTransforming the clinical development process into an advanced data-driven and integrated approach is a big shift for pharmaceutical enterprises. Compa-nies should ensure that the following key capabilities are in place:

1. Data Infrastructure & Analytics Incorporating an integrated, risk based approach to

clinical monitoring requires a robust data infra-structure in order to be feasible. The key word here is “integrated”, and in order to have truly integrated risk management, there needs to be integration of the various siloed data sources that contain the study data.

The data infrastructure must include the following key components:

A. Source ingestion:


Clinical trial data is typically spread across a variety of systems and platforms. E.g., opera-tional data resides in a Clinical Trial Manage-ment System (CTMS), Case Report Forms (CRFs) are in an Electronic Data Capture (EDC) format, Adverse Events / Serious Adverse Events (AEs) are often reported through a separate safety system, budgets and payments are in a finan-cial ERP system, Clinical Research Organization partners may report data through their own interfaces, and so on. Additional data can be pulled from sources such as Clinical Trials.gov, the Food and Drug Administration, and other publically available sources that can add dimensionality to the analytics downstream. The aforementioned data required to fulfill clinical sciences, risk based monitoring, and strategic operations are key to discovering insights, indi-cators, and performance respectively. Hence, a key capability which must be present in the data infrastructure is the ability to connect to these various disparate sources and gather data into a central location for analysis.

B. Clinical Data Landing and Standardization:

The data across all the above systems must be landed into a central location at an appro-priate frequency. As it lands, the data must be profiled and assessed for data quality gaps and errors. Records that do not satisfy specified quality and integrity criteria must be reported and processes set up to rectify them. Standard-ization of data elements must happen across different system conventions, which should be done in a flexible and configurable way. Master data management capabilities are needed to match and merge data referring to the same entity (e.g. trial subjects, sites, compounds, therapeutic area etc.) across the various source systems. All of these capabilities are essential in a data infrastructure to enable efficient and reliable data processing for analysis.

C. Integration

One of the most critical elements of the clinical data infrastructure is the Data Model which brings together the key data elements from various systems into an integrated and consol-idated format which is essential for analysis and reporting. The data is modeled and trans-formed from an operational format to an

The figure below describes a conceptual data infrastructure for implementing integrated clinical trial monitoring:

Data Infrastructure Enabling Integrated Clinical Trial Monitoring

Electronic Data

Capture

AdverseEvents

TrialFinancials

CTMS

IVRS

TrialMaster

Landing DataStandardization

Profiling

Error Process-

ing

Data Quality Rules

Master DataMgt

MetaDataVocab

ClinicalOperationsIntegrated

Data Model

Aggregates

KPIsand Risk

Indicators

BusinessRules

DashboardVisualiza-

tions

Reports

Exploration

StatisticalModeling

MetricDictionary

Sources Landing / Standarization Integration Analytics Consumption


analytics-friendly format around the key entities and relationships which form the core of the clinical operations data. The ability of a data framework to do this in a flexible and extensible way is a critical capability for success. Even more so, the ability to handle multiple standards as data models is crucial to ensuring flexibility for current and emerging use cases. Recommended data standards include CDISC, SDTM, ADAM, and Operational Data Models that work with the majority of data system vendors.

D. Analytics

The most important business requirement for integrated clinical operations monitoring is the availability of risk indicators and metrics for centralized risk assessment, analysis and decision making. The analytics layer must be configurable, using business rules to define data aggregates which are defined by clinical opera-tions management. Some of the key capabilities needed include:

Centralized access for metrics and aggre-gates for analysis

Multiple levels of aggregation to define metrics (e.g. Program, Study Portfolio, Study, Country and Site level metrics are all used to monitor risks and performance at those levels).

Definition of metrics using business rules. The Metrics Champions Consortium (MCC) has a published set of industry standard metrics and TransCelerate has a similar listing of Key Risk Indicators for reference. The data infrastructure should be able to implement and customize these metrics “out of the box”.

Ability to report on baseline, actual and vari-ances for monitoring insights.

Comparators (median, mean, industry average etc.) and thresholds to classify performance and provide alerts.

Rules to pre-process information for predic-tive modeling purposes

Dynamic Rules to accept user defined decisions for scenario based modeling and prescriptive analytics

E. Consumption

The metrics and aggregates in the Analytics layer must be accessible through a variety of tools appropriate for different use cases. Examples include:

Dashboards using powerful visualizations of data are important to highlight the insights relevant to stakeholders. Dashboards must be configurable for each type of user, using alerts and workflow to drive action from data insights.

Reporting capabilities provide detailed insights at a more granular level than visual-izations and are invaluable to users looking to investigate when to terminate a study, how to design a new one, monitor open studies, or oversee their partner CRO’s.

Data Exploration capabilities ease the process of “ad hoc” analysis. Despite the availability of high level insights and risk indi-cators, there will always remain the need for ad hoc exploration and human driven data drill downs to investigate underlying root cause.

Statistical modeling capabilities are needed for centralized monitoring, predictive and prescriptive analytics:

Prediction capabilities to extrapo-late leading indicators into future trial outcomes.

Outlier detection and trending to identify anomalies for further identification.

Mean, median and variance computation for analysis.

Pre-built regression models and other advanced techniques for predictive analytics.


2. Data Governance for the Integrated Data Hub

Integrated and standardized calls for appropriate data governance strategies. As these are common practice for enabling cross-functional user groups for enterprise, the focus should be around legacy processes that need transformation or augmen-tation through more data-driven supported processes. When adopting a big data ecosystem for the use of assimilating enterprise data, down-stream provisioning of assets can create numerous ‘data ponds’ that may be duplicative and difficult to maintain. The fundamental approach towards establishing a data governance necessitates a focus on metadata and master data management, mitigation of controlled medical terminology, and role-based access/security.

Meta-Data Management & Master Data Management

Source systems have a wealth of informa-tion about the data residing within them. When these systems are ingested into the integrated environment, proper capture of this information along with the meta-data of the processing pipelines is crucial for enabling audit and lineage receipts of the data-life cycle. A meta-data management capability can also facilitate the over all Master-Data Management component. In order to formulate the single source of truth for all integrated data (e.g. within a standard-ized data model), meta-data and respective data dictionaries can form the backbone of data stewardship, custodianship, and management functions. Once discrete data is mastered, the users accessing the data can have full confidence in its accuracy, and understand it’s true meaning with reference meta-data and dictionaries.

Controlled Medical Terminology

Clinical data sets, produce many observa-tional data on subjects during the study. Adverse events, medical history, adminis-tered procedures, concomitant medication

are all mentioned in different forms within source systems. Vocabularies such as SNOMED-CT, LOINC, and MedDRA facilitate the appropriate annotation of these terms to a standard that’s understood by end users. Vocabulary leveling isolates incoming terms and associates them to the best position and term within a specific standard (e.g. leveling EMR and CRO medical history data to the respective position of preferred terms of the MedDra hierarchy). When users combine this with knowledge of meta-data/dictionaries they can easily isolate the data in the form of their interest.

3. Roles and Skills Alignment The traditional approach to clinical development

analytics relies on requirements to gather the needs of each business unit The focus for this type of engagement can get derailed with numerous stakeholders requesting distinct and similar sets of data and analytic results. Extending the analytic needs to more advanced use cases only adds to the complexity. Assessment frameworks that isolate data and needs in the following methods will allow for straightforward transition into operationalized and integrated analytics.

Question Information Decision Action Impact (QIDAI)

Capture business objectives for each unit and map it to their daily needs, and organized by use case

What question(s) is the business unit answering?

What information is needed to answer this question?

What decision will be made based on the answer?

What impact will be made based on this decision?

Business Roles Information Analysis (BRIA)

Capture the specific levels of roles within a business unit and the granular information needs


What business role is requesting this use case?

What level of information does this role need to contribute to the use case?

What specific metrics, calculations, or modeling will be conducted with this information?

Which system contains this information (fields, tables, files, etc.)?

Through the cycle of this process, the needs of business units can be translated to data pipelines with the least amount of duplicity and the highest efficiency for catering to analytic needs. Stakeholders can be aligned and skills can be adopted to fill in gaps observed (statistical analysis, advanced modeling, etc.)

Data and Analytics Use Cases for Integrated Clinical Trial Monitoring

Integrated RBM enables the following use cases:

A. Initial Risk Assessment

Analytics on past trial experience and historical data are key inputs for the study monitor to inform the initial risk assessment. As described earlier, this risk assessment is used to identify critical data and processes for a study. Program and Protocol level risks are informed by data on other studies within the same program.

B. Centralized monitoring and analysis

The study monitor needs extensive analytics capabilities to perform the expanded monitoring function necessary in centralized monitoring and analysis. Some of the key use cases include:

i. Subject level analysis to ensure the integrity of inclusion / exclusion criteria, adverse event identification and classification and consistency of subject data / diagnosis.

ii. Site level analysis of subject data to monitor site performance, ensure Good Clinical Practice (GCP) and regulatory compliance.

iii. Monitoring of site variances to identify sites with higher potential risks related to protocol

deviations, issue management and adverse events which could point to site level process or infrastructure issues.

iv. Monitoring of trends and outliers in trial and subject data to identify patterns or tolerance variance levels which could be indicative of site level fraud. Key processes to monitor include enrollment, screening, issues / query frequency and response timeliness, adverse events frequency and response timeliness.

C. Thresholds and Alerts

A key capability which is enabled by the integrated data infrastructure is for the Study manager to be able to set up a configurable system of thresh-olds and alerts at the Program, Portfolio, Study, Country, and Site levels which can point to current operational challenges as well as be predictive early indicators of underlying risks or issues in the trial management. Simple red/yellow/green indi-cators are very commonly used to abstract away the complexity in the underlying data and focus in on the highest risk areas for the study manager. Having these alerts at a portfolio level (across trials) or across different subject areas and systems is a huge value add for study managers, who otherwise have to sift through reports from each individual system to piece this together for themselves.

Some of the areas of focus for alerts would typically include:

i. Safety – volume and frequency of SAE/AE, and response timeliness.

ii. Issue Management – volume and frequency of issues reported, response timeliness.

iii. Enrollment – recruitment rate, screening rate, screen failure rate, discontinuation rate.

iv. Financials – budget and variance, as well as projected overruns are key to managing risk.

v. Schedule – Key trial milestones, planned, actuals and variance, overall schedule slippage.

vi. Risk scores – Could be defined for the trial overall, or for one of the key areas above.


D. Consolidated tracking of trial milestones and metrics

Routine tracking of completion of trial milestones is available through most CTMS systems. However, milestone variance in the absence of other parame-ters such as safety, budget, etc. shows only a partial view. The promise of a strong analytics capability is the ability to analyze a trial across all of the relevant safety, financial, schedule and operational metrics to get a complete picture for Study Manager and Monitor to driving appropriate action.

E. Executive leadership reporting

With traditional clinical trial management and moni-toring, executive leadership is typically inundated with a variety of reports which are often incon-sistent and incomplete since they show different views of disconnected data. With the data infra-structure now having a standard and integrated view of all the trial data across systems, executives have powerful tools to define and visualize metrics at the program and portfolio level which are able to drive insights and actions across individual studies.

Example of consolidated study level analytics dashboard


Why Saama:Saama’s approach to clinical development analytics focuses on three key value drivers:

1. Integrated and Ready to Scale Saama’s solution is based on a next generation

Big Data architecture, which leverages the latest technology breakthroughs applied for use in clinical development.

ConfigurableDataingestion: Pre-built connec-tors to core systems such as Clinical Trial Manage-ment Systems (CTMS) for operational metrics, Electronic Data Capture (EDC) systems for case report forms, Interactive Voice Recognition (IVR) for recruitment progress, financial systems for payment and budgets fuel our data ingestion pipelines and eschew traditional extract/transform/load processes. With the use of these connectors,

complex structured/unstructured data can be retrieved and stored as in source with meta-data captured and profiled.

Extensible Clinical Operations Data Lake: Saama’s solution opts for the implementation of a Hadoop-based ecosystem. The corresponding architecture includes a data lake where all the key clinical development data is landed in a data lake. This data is also intelligently organized by levels of hierarchy from the portfolio down to the patient level. With extensions of modifiable data pipelines and an intuitive business rules editor, key metrics may be derived for the use of clinical operations (MCC KPI’s & Transcelerate KRI’s) or biostatistics (SDTM & AdAM data models). As a result, the inherent scalability and extensibility is efficient and effective over the traditional data warehousing approach.

Typical Executive leadership view with metrics across a group of studies


Extensibility: Traditional approaches to clinical operations analytics utilize archaic data ware-housing approaches that exhibit substantial over-head for supporting new data standards, emerging business use cases, and general modifications to support new sources of information. Large data sets, patient level models, and next-generation Risk Based Monitoring present challenges to the systems by which data is transformed to informa-tion and knowledge. Big data approaches facilitate the integration and analysis of extremely large data sets at scale. Primarily, Genomic and medical device information can be consumed and opera-tionalized rapidly over the distributed storage and analysis paradigm of the Hadoop framework. Next, Patient level information can be fed into models can predict likelihood to achieve study end-points via the use of innovative simulation and modeling frameworks as found in Spark. Finally, the next level of Risk Based Monitoring can be achieved with prescriptive insights for mitigating severe trial risks for all business unit processes involved via the use intuitive analytic applications configured for the current and evolving processes. Given the advances in machine learning, artificial intelligence cut down on phase-III attrition by driving the decision on which investigational products to pursue or leave behind

Scalability: Saama’s big data approach can be scaled as needed to accommodate ever growing sources, variety and volume of clinical operations data. As pharmaceutical companies grow, acquire and merge with other companies, the asks from the data integration platform are ever growing to help manage these changes. A scalable, distributed big data platform future proofs the investment of data aggregation; shifting the focus from rudimentary study monitoring to targeted outcomes for studies that meet their endpoints closer to their planned durations.

Cost: Traditional data warehouses using a platform such as Teradata or Oracle become prohibitively expensive as larger and larger datasets are pulled

into scope. The cost of a traditional warehouse may be $3-5M, depending on the infrastructure and technology components. The up-front imple-mentation cost for a big data solution is rapidly approaching the levels of traditional relational systems. Procuring next-generation systems that are orchestrated with a data fabric will add to the savings to total cost of ownership with compounded efficiencies for on-going support and maintenance.

Prebuilt data models and metrics: Saama’s solu-tion brings an out of the box, optimized data model for clinical development data. This data model incorporates 17 key subject areas, 60 entities, ~80 milestones and 50 KPIs which are optimized for all the commonly applicable clinical operations and risk based monitoring use cases. Saama’s metrics library is based upon industry-standard definitions created by MCC and Transcelerate and are ready for implementation out of the box.

2. Actionable Intelligence Unlike a traditional data warehousing solution,

Saama’s value proposition is centered around enabling advanced analytics on clinical operations data. The focus is on driving forward looking deci-sion making which is directly action-oriented.

The weaknesses of both traditional and risk based monitoring approaches are in their actionability. Traditional monitoring relies on pure reporting of metrics staying within business defined thresholds and nothing more. A next generation approach, correlates various metrics and associated factors by which this risk may be mitigated. With decision trees mapped out by human experience and intuition, operationalized models may recommend the best course of action for a given scenario.

Saama’s approach to clinical development analytics focuses on business outcomes. Our methodology brings together the diverse sets of expertise around data integration, data science, business process and management consulting in order to deliver truly decision oriented and actionable intelligence.


As an example, consider a report which shows a key metric such as Screen Failure Rate for a trial. As a standalone metrics, this is barely informative, and not diagnostic at all.

In order to make this metric predictive and actionable, the Saama’s reporting solution is able to:

Show the screen failure rate at different levels of granularity (study/country/site) along with enroll-ment ratio, time between informed consent / randomization, time between subject to subject, length of time the site has remained active.

Compare with historical and industry averages for similar types of trials, as well as the baseline expectation.

Observe trends between sites in order to asso-ciate problems that may be regional, opera-tional, or regulatory in nature.

Compare with historical and industry averages for similar types of trials, as well as the baseline expectation. Draw estimations of trial length or site enrollment failure based on Bayesian methods.

Extend to scenario based analysis in which various recommendations may be made (investi-gator contact, site termination, protocol amend-ment, and etc.)

Enable an algorithmic analysis of potential underlying causal factors.

Saama’s approach focuses on integrating this intelli-gence into the business processes of study managers, monitors and executives so as to seamlessly leverage these insights into their normal decision making and operational processes, thereby raising the overall value proposition of the solution.

3. Powerful Machine Learning and Advanced Analytics

Risk based monitoring relies on a strong foundation of capabilities for centralized statistical monitoring and pattern / trend analysis. These capabilities typically require access to the full range of granular

source data elements which are often not available in a traditional data warehouse based solution and would require a duplicative source data repository to be built for statistical analysis purposes.

Saama’s clinical operations data lake, stores the full range of raw source data as part of the solution approach. Saama’s Fluid Analytics for Life Sciences includes a robust set of data science and machine learning capabilities integrated as part of our Clinical Operations analytics suite. Hence, we are able to enable advanced statistical modeling and analytics capabilities within the solution itself.

Historically, traditional risk management methods relied on domain experts and threshold field data samples to identify and mitigate risks. Today with the advent of low cost, high performance compute engine and unsupervised machine learning methods, along with the huge volume of historical clinical trials data already available, it is possible to build a ‘learning system’ to predict risks and identify mitigating interventions before a clinical trial metric is compromised. This is truly the next frontier of intelligence in clinical trials monitoring that harnesses the power of technology to identify risk patterns in data and provide actionable alerts driving the most critical human interventions.

Closing thoughtsThe heightened focus on increasing safety and effi-ciently utilizing resources have created the need for an improved approach to monitoring clinical trials, using both on-site and centralized monitoring capa-bilities. These capabilities must be able to support a variety of approaches (both risk based and traditional) with the right set of risk and performance indicators to meet the different monitoring needs of varying trial situations.


References1. "A statistical approach to central monitoring of data

quality in clinical trials," Sage Journals, Jun 2012.

2. "Application of methods for central statistical moni-toring in clinical trials," Clin Trials 2013, Oct 2013.

3. "Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department,"J Am Med Inform Assoc, Jan 2015.

4. "Competencies for the Changing Role of the Clinical Study Monitor: Implementing A Risk-Based Approach to Monitoring," Applied Clinical Trials, Apr 2014.

5. "Four Disclosure Compliance Risks that Clinical Trial Sponsors Should Identify and How to Avoid Them," Pharmaceutical Compliance Monitor, Aug 2015.

6. “Guidance for Industry Oversight of Clinical Investi-gations: A Risk Based Approach to Monitoring,” U.S. Food & Drug Administration, Aug 2011.

7. "Position Paper: Risk-Based Monitoring Method-ology," Transcelerate BioPharma Inc., May 2013.

900 E Hamilton Ave.Suite 200Campbell, CA 95008www.saama.com

+1 888.205.3500+1 408.371.1900https://www.saama.com/[email protected]

Copyright © 2016 Saama Technologies

Sagar Anisingaraju is the Chief Strategy Officer at Saama Technologies, and the winner of Chief Strategy Officer of the Year award from Innovation Enterprise. Sagar has been instrumental in architecting advanced analytics solutions, used by life sciences companies to derive critical insights in healthcare delivery. He is a frequent contributor to a number of publications, such as Genetic Engineering & Biotechnology, on the use of big data in life sciences. Prior to joining Saama, Sagar was the founder and CEO of InfoSTEP Inc.

Nikhil Gopinath is a Senior Solutions Engineer & Life Sciences Lead for Saama Technologies. His focus is big data technologies and applications of data science in clinical research & development. He holds a B.S. in Biochemistry from North Carolina State University and is currently pursuing a Master’s in Biomedical Informatics at Rutgers University.

NekzadShroffis Vice President of Business Outcomes and Solution Strategy at Saama Technologies. Nekzad advises pharmaceutical manufacturers on analytics strategies for clinical operations, managed care contracting, payer access management, outcome-based reimbursement, distribution strategy, commercial sales operations and account management.

Authors

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