Statistical science is very effectively used in these times to find solutions for human health and disease and to meet national public health goals. Biostatistics provides answers to issues in likelihood methods for inference, epidemiology, clinical trials, survival analysis, and statistical genetics. Indegene facilitates clinical trials with its senior statisticians armed with experience in all kinds of clinical trials and safety review processes.
Our expertise lies in:
Review of the protocol: Statisticians provide inputs during protocol development in various sections such as trial design, calculating sample size, primary and secondary endpoints, data capture and processing, and in the statistical section.
Determining the number of displays needed: On the basis of the study protocol, statisticians provide the number of displays needed in the trial.
Review of macro timelines: In team meetings with client and other departments, the study lead decides the timelines for each and every task of the trial.
Generating randomization schedule: Our statisticians use software such as SAS to generate the randomization schedule.
Review of Case Report Form and Data Management Plan: Statisticians review the Case Report Form and Data Management Plan documents to see if data are being collected at all necessary time points; the date, time, amount, and duration of each dose are being recorded; the date and time of the first dose is being adequately collected in the CRF; the date of informed consent is appropriately collected; and the subject completed or discontinued the study.
Developing draft SAP: When the study protocol is finalized, the statisticians develop the Statistical Analysis Plan (SAP) that elaborates on the principal features of the analysis described in the protocol and includes detailed procedures for executing the statistical analysis of the primary and secondary variables and other data. During submission, SAP is submitted with other documents to the regulatory department.
Developing mock shells of Tables/Listings/Graphs (TLGs): Our statisticians develop the mock shells of the TLGs according to SAP and other guidelines. The SAP and mock shells are sent to the client for approval.
Participating in DB lock and unblinding of treatments: Before the database lock, our statisticians confirm its suitability – whether all relevant data exist and if the errors identified in the query process have been rectified or not. The allocation code is broken so that the investigator, clinical staff, and statisticians become aware of the intervention for a person participating in a trial – this is unblinding.
Reviewing CSR: A clinical study report (CSR) is a regulatory document for marketing application for a drug, biologic, or device. The CSR is co-authored by study statisticians and medical writers to ensure clarity and accuracy in conveying statistical findings. The CSR elaborates a clinical trial with tables, listings, and figures (TLFs). The statisticians help in interpreting the results and in addressing any statistical questions.
Data are key to the drug development process. The FDA-recommended CDISC standard makes for streamlined and efficient collection, analysis, and regulatory submission of data.
Indegene provides all data management services as per CDISC standards for a smooth drug development program. Our team of programming experts and statisticians understand the analytical requirements of even the most complex clinical trials. They apply the most efficient and systematic process to ensure consistent, accurate, high-quality, and timely CDISC deliverables to support regulatory submissions.
Our software development ability allows us to develop commercial grade applications that enable efficient delivery of our clinical trial services. The combination of technology and a global team of CDISC skilled resources enables us to offer high-quality datasets at a cost saving of 20%-40% over competitive service providers.
At Indegene, we offer our clients:
End-to-end CDISC standard implementation through PRM (Protocol Representation Model), ODM (Operational Data Model), CDASH (Clinical Data Acquisition Standards Harmonization), SDTM (Study Data Tabulation Model), ADaM (Analysis Data Model), LAB (Laboratory Data Model), SEND (Standard for Exchange of Nonclinical Data), Trial Design Model (TDM), Case Report Tabulation Data Definition Specification (CRTDDS) (defifine.xml), and Biomedical Research Integrated Domain In Group (BRIDG)
Design and mapping for CDASH, STDM, and ADaM
Data conversion for STDM, FDA-approved standard e-tabulation format for clinical research data, and ADaM, which supports efficient generation, replication, review, and submission of analysis results from clinical trial data
CRTDDS for xml and define.pdf generation
The Indegene edge
Clients have benefitted from our conversion service to CDISC standards since it:
Cuts cost: Our scalable, unit-based pricing helps match supply and demand for data conversion efforts minimizing rework, manual maintenance tasks, and miscommunications. Aligning to CDISC standards at the beginning of the trial cuts resource requirements by 60% overall and by 70%-90% in the start-up stages of trials.
Decreases cycle times: In some cases, the cycle time decreases by as much as 8 months. Agency rejections for submission-ready compliance have dropped from 50% to 5%.
Reduces rework: Our tools identify any discrepancies that could affect submission readiness. This helps companies reprocess components in time avoiding rework and enhancing submission quality.
Streamlines queries: Regulatory or safety queries are best answered when there's a standardized environment backing submissions. With an efficient system, almost 80% of the cost and effort goes down while finding, retrieving, and shaping data to answer queries.
Interim Analysis: Clinical trials involve human beings. Such scientific experiments have to be approved by external ethical committees and must follow a defined protocol. Due to the sensitivity involved, investigators often resort to an interim analysis conducted by an independent data monitoring committee (IDMC). This helps assess data compiled during patient enrollment or follow-up stages or before the recruitment for a trial is complete. An interim analysis gives a clear idea of center performance, the quality of the data collected, or treatment effects.
Final Analysis: Whether it is a large randomized trial or a small investigator-supported trial, it is imperative to pre-determine what kind of analysis the project requires from the word go. Indegene's statisticians realize that number crunching yields not just charts and graphs but also solid insight into the trial. They put in effort to make numbers meaningful for non-statisticians by interpreting these results in understandable analysis reports after database lock.
This is the process of randomly allocating patients to treatment groups – a crucial step in a clinical trial to minimize bias.
Indegene's dedicated team of statisticians and programmers design randomization plans and control their error-free implementation. Our experts generate lists and algorithms for all types of randomization (stratified permuted block, covariate-adaptive, response-adaptive) that help pharmaceutical companies achieve treatment group balance, eliminate selection bias, and limit the predictability of treatment allocations.
Our consultants help clients meet specific needs arising in their studies. Our in-house statistical experts also help them choose from a huge selection of validated algorithms or create custom algorithms for them. They choose from a comprehensive spectrum of validated randomization methods and use a centralized emergency code break via IVR and IWR. The services provided by our team include simulations and consulting to optimize randomization methods, real-time study recruitment reporting, and randomization methodologies for adaptive trial designs.
Each clinical trial has unique goals in terms of sample size – the number of participants or patients taking part in the trial. This has to be planned on a rational basis so that an optimum number of participants are included in the trial. The "power" in a clinical trial is the probability that the trial will have a significant (positive) result.
The sample size has to be specified before recruitment starts. If the investigator does not plan this in advance, it might be difficult for an independent monitor to be sure if the data and statistical methods actually lead to the "demonstrated" result. It is also necessary to control the probability with which a real effect can be identified as statistically significant.
For instance, if a very small sample size is used while a pharmaceutical company is trying to introduce a new drug, it might fail to demonstrate efficacy or non-inferiority relative to other drugs. Similarly, testing the drug on too many patients would also be equally uneconomical and unethical. In descriptive and retrospective studies, the sources and the scope of the data must be planned in advance.
The power is calculated with the P-value, which should be less than the specified significance level (usually 5%) to make for a positive trial.
Calculating sample size requires expert medical knowledge and, therefore, a multi-disciplinary collaboration between experienced biostatisticians and physician-researchers.
A statistical analysis plan (SAP) tries to minimize bias by clearly stating how to deal with discrepancies in the study such as protocol deviators, early withdrawals, and missing data. The SAP outlines solutions for these anticipated problems and any other issues the study might encounter unexpectedly.
An elaborate SAP generally includes technical details of the key features of the protocol and the procedures for statistical analysis of the data. It has sample layouts for tables and listings to be produced. Therefore, preparation of a SAP is a key component in the conduct of a rigorous clinical trial and requires a statistician with both formal statistical training and significant experience in the pharmaceutical industry.
The SAP document is among the key documents that need to be submitted to the regulatory authorities.
A clinical trial evolves from the early stage of enrollment through the study phases, culminating in submission of documents and archiving of all data. The smooth flow of this chain of events needs strategic planning that ensures all the cogs of the wheel fit well – from accommodating large participants' data to taking care of the needs of individual therapeutic areas. Statistical programming also factors in the requirements of the various departments involved such as Clinical Operations, Clinical Research, Data Management, and Regulatory.
Indegene's programmers handle this vital process of statistical analysis, generating data listing, tables, and figures. They also support clients to install and validate statistical software packages. Our technology-savvy programmers are familiar with the life sciences domain and the demands of clinical trial design and analysis.
Our robust programming infrastructure allows us to support a whopping number of clinical code lines without any errors. A strong clinical solution code foundation also allows us to make these lines scalable and flexible for our clients. Our programming team is armed with cutting-edge technology, the flexibility to incorporate new technology, codes with a long-term adaptability, and room for enhancement.
Since massive amounts of data are involved, the statistical program needs good practices that enhance reusability, readability, flexibility, performance, and scalability of code. We maintain best coding practices in the programming environment. Our programming codes adhere to good practices and are in compliance with FDA regulatory guidelines, GCP, CDISC, and 21 CFR Part 11. For specific technologies such as SAS, we maintain infrastructure for SAS datasets and SAS codes' project files to ensure safety, efficacy, and convenience of the programming elements. Managing statistics effectively means cutting down drastically on manual intervention for an error-free coded system.
Clinical data records intended for long-term storage are archived in a readable/PDF format without data editing capabilities/rights as per the CDISC standards – Operational Data Model (ODM).
Indegene archives the meta-data including information about the eCRFs, the questions, and the validation logic for all clinical trials.
We understand the importance of archiving the critical data and site-specific snapshots of its meta-data, and to ensure this we do an ODM export before any change is made to the data. An ODM Navigator allows our clients to:
Query the data to extract subsets
Review data on a site-by-site basis
Clinical research is time consuming, often challenging, and needs whopping amounts of data that might not always be a joy to handle. The goal at Indegene is simple – to make the data handling process absolutely efficient, crisp, process-driven, and thus enjoyable.
A complex regulatory environment and mounting research and development costs for new drugs are key challenges that drug development companies are facing today. It is, therefore, essential in these challenging times to produce accurate and source-verified clinical trial data faster than ever before to save time and cost.
Indegene ensures that clinical trial data are handled with the care and precision it deserves.
Clinical Data Management (CDM) Services
Quality data are the number one pre-requisite for clinical trials. Indegene’s CDM services make sure that data available for clinical trials are continuously improved and updated through feedback, state-of-the-art systems, training, and teamwork. A dynamic team of data management experts sets up, collects data, and maintains the database, cleans data periodically, and stores it in strict compliance with regulatory requirements.
Our experts talk to companies through their unique needs and business requirements, and tailor-make solutions to efficiently manage their clinical trials data, keeping in mind time and budget constraints. Our team also routinely enhances external vendor systems and helps clients integrate drug safety and clinical applications – both its process and the system.
What our CDM services stand for
If a clinical trial is a success, most of the credit goes to efficient data management. The quality of data generated and how well it is analyzed could make or mar the trial. Indegene’s data management service is proud to adhere to the following gold standards for its data to make sure it is a ‘Yay!’ for every clinical trial:
Transferability across platforms
Analysis and reporting
"Change is the only constant," aptly so in clinical trials. With that in mind, Indegene weaves into its systems ample flexibility for data migration, integration, and mid-study protocol correction.
Migration: Indegene adopts a fully or partly automated approach using software tools for mapping and migrating the data from the legacy systems to the EDC platform of your choice.
In addition to validating the system, the clients have an advantage to perform maximum runs during the transition from legacy system to the new system. Indegene’s data mapping and data migration services have helped the clients in saving cost and time. This approach enables availability of data for an indefinite period to meet regulatory and compliance requirements.
Integration: Indegene understands the dynamics of modern day clinical trials and is experienced in integrating Clinical Trial Management Systems (CTMS), Drug Supply Management Systems (DSMS), central laboratories systems, leading EDC software, and other data analysis and management systems.
Indegene adheres to the CDISC standards with an experienced and efficient team for data integration. We work in all formats (eg, SAS, xml) required for data transfer, addressing the clients’ data integration needs in a timely and orderly fashion.
Indegene’s database is designed to facilitate CDM tasks in multiple studies. We conduct system validation programs to ensure data security. During this process, our experts evaluate system specifications, user requirements, and regulatory compliance. The database defines study details such as objectives, interval, visits, investigators, sites, and patient information. The eCRF layouts are designed and tested with dummy data before we attempt to capture real data.
The programming team works with skilled data managers and excels in beating timelines. The team’s goal is to ensure quick and top quality delivery of projects with optimum use of resources. For instance, our team gets through the start-up phase of a project within 4 to 5 weeks, unlike regular trials that take close to 7 weeks. The team meets Clinical Data Acquisition Standards Harmonization (CDASH) requirements that simplify the forms, helping save time and cutting down user errors and queries.
Oncology trial advantage: We design eCRFs for complex early-phase oncology trials keeping in mind the end-user requirements, analysis, and reporting expectations. This allows rapid and accurate review of data for safety, especially in dose-limiting toxicities.
Our team ensures a proper quality check and assurance before running the final data validation. If there are no discrepancies, the SAS datasets are finalized in consultation with the statistician. All data management activities are completed prior to database lock. After locking the database, the team extracts clean data for statistical analysis. This is followed by archiving of the data. Generally, no modification in the database is possible. But in critical cases or for other operational issues of significance, privileged users can modify locked data. This, however, requires meticulous documentation and an audit trail with ample justification on why the data needs tweaking.
Discrepancy management is critical to the CDM process. We realize the need to take utmost care while handling such discrepancy – this is vital to the process. From start to finish, our team handles this task systematically. It uses customized data management processes that emphasize transparency, integrity and accountability, speed, and accuracy. Our client data management team produces results that are source-verified, reproducible, and cost-efficient.
Indegene's hallmark service and project management methodologies ensure efficient work flow, faster problem solving, and reduced oversight time. Indegene adopts standardized metrics to encourage efficiency, effectiveness, and performance improvement such as:
Average Query Resolution Time
No. of Queries Closed
Missing Pages Report
We have flexible data management systems for Phase I and II oncology trials. Access to data, ability to clean data in real time, and expertise to handle unexpected assessments help us clean data super-fast during complex oncology trials.
A centralized team of experienced data managers oversees the team's work and identifies errors. The quality control team’s constant supervision ensures delivery of authentic, clean, and consistent data. This team improves and adds value to the existing processes.
We make Data Validation Plans (DVPs) with edit-check programs that help in cleaning up the data by identifying discrepancies. Alongside this, we work on custom function development.
Ours is a versatile team with experienced people from various therapeutic backgrounds. The team maintains an internal data repository of different therapeutic areas, which stand us in good stead when we create the electronic Case Report Forms (eCRFs). While exploring new scientific indications during trials or untangling complex data jumbles, this team has the expertise to ride the wave and maximize the potential of each study.
SAE review and reconciliation: Indegene’s safety data managers work in close coordination with other data management activities to ensure this process goes smoothly. The process of reconciling data between the databases continues till reconciliation is complete with no outstanding issues. Indegene attaches special significance to reviewing reconciliation for oncology clinical trials. For example, progression in disease that causes hospitalization but not death may not be reported as serious in oncology clinical trials. This is a protocol-specific determination that has to be established prior to beginning reconciliation of a study.
The tools we use to manage this process are:
Reconciliation spreadsheet (Excel)
Master spreadsheet: documents all discrepancies, the action required to notify appropriate team
Review by a safety data manager
Partnering with pharmacovigilance group
Listings from safety database
Calendar of study milestones
Data management activities
Listings from clinical database
Laboratory data review and reconciliation: We put to use robust procedures for collection, transfer, loading, and validation of laboratory data document.
Our best practices include automation of checks for data, reconciliation, streamlining the query resolution process, and implementation of issue trackers to keep an eye out for any problems. Our data managers anticipate delays that might spring up unexpectedly in processing data and identify them during reconciliation. They communicate these timelines to the vendor to ensure everyone is on the same page and to integrate laboratory data in time before the database is locked. Laboratory data are reviewed in batches during the study period so that generic issues can be identified and addressed in real time.