Case study: The REALI pooled analysis of the European post-marketing studies for the TOUJEO® product
The purpose of the REALI pooled analysis is to advance the understanding of the effectiveness and real world safety of insulin Gla-300 based on a large European patient database of postmarketing interventional and observational studies.
This study aims to identify and understand the variation in patients’ experiences when treated with Gla-300, and to gauge selected patient characteristics that may be of interest to describe subsets of European populations with diabetes. To achieve these goals, the Sponsor uses two complementary statistical approaches, which enhance the chance of correctly identifying subgroups of patients with specific effectiveness and real-world safety patterns. Highlighting the profiles of patients who achieve greater glycaemic control will allow clinicians to provide personalized treatment plan to patients with diabetes.
IDDI TASKS :
- Statistical Analyses Plan
- SDTM Mapping for the pooling
- Statistical Programming
- Statistical Analyses
IDDI SOLUTIONS :
- IDDI produced statistical reports at overall and at subgroups level enabling the Sponsor to compare the results with the local CSR.
- IDDI performed two sets of programs quality control and data homogenization at Data Management and at Statistical level in order to identify missing data and work on equivalent variables
- IDDI provided a substantial support in terms of statistical data review and data equivalence improving the quality of analysis.
READ CASE STUDY HERE: The REALI pooled analysis of the European post-marketing studies FOR the TOUJEO product
Overcoming the challenges of Collaborative Global Trials to deliver Harmonized SAS SDTM Database
Successful Delivering of Harmonized SAS SDTM Database
A Prospective, Randomized, Double-Blind, Phase III clinical trial in women with breast cancer to determine whether the addition of an anti-PD-L1 to chemotherapy improves pathologic complete response and survival.
SITUATION: ONE STUDY, TWO SPONSORS, MULTIPLE SYSTEMS
This collaborative study is run by two cooperative groups, with financial support from a multinational healthcare company. The study protocol was slightly different for each Sponsor.
Due to the collaborative nature of the trial, the study set-up incorporates two electronic data capture (EDC) systems and three different Interactive Response Technology (IRT) systems (two for randomization and one for drug supply).
IDDI is reponsible for:
- The randomization list used for the IRT systems
- The Study Data Tabulation Model (SDTM) SAS programming for each EDC database
- Combining the databases into one final database used for the statistical analyses
- The statistical analysis of the whole trial
- Providing reports for Independent Data Monitoring Committee (IDMC)
- Complex Set-up: The cooperative model of this trial meant that multiple systems needed to be used, as each of the two cooperative groups preferred to use their own system. Two separate EDC systems and three IRT systems (two for randomization and one for drug supply) were required– the latter built and managed by an existing external vendor of the supplier of the study medication.
- One Single Harmoized SAS SDTM Database: IDDI was tasked with building one SDTM SAS DATABASE out of two different EDC systems. The data were handled in different EDC and IRT systems, making the harmonization of the two study case report forms (CRFs) complex, due to a non-fully CDASH compliant CRF imposed by Sponsors, and the slight CRF differences because of two slightly different protocols.
- Consistent Programming: As the data were entered into two separate EDC systems, IDDI needed to program from two separate database extracts and ensure consistent SDTM SAS programming at the end.
- Quality Control: Data quality issues due to two different databases.
- Project coordination: The project entailed intense communication with Sponsors at different levels (Management and Operations), with each Sponsors individually and with both Sponsors together.
- Transfer of Data: Additional challenges were encountered around the transfer of data between the IRT systems. Although, the preferred option is to integrate systems via Web services, the Sponsor opted for transferring data through sFTP protocol, increasing the risk of transfer failures.
IDDI has been working with one of the sponsors for over 10 years’ and so they were confident that we would help them meet these challenges. We provided the client with an enhanced plan to address the study challenges. IDDI teams’ extensive experience, flexibility and scientific background, combined with methodological and operational excellence, allowed to handle the complexity.
- IDDI put in place a comprehensive communication between all stakeholders and ensured expert oversight and responsive project management.
- Ensuring data equivalence within the final SAS database for operational and end-of-study analysis purposes was mission critical to all stakeholders. IDDI harmonized case report forms for both EDC systems and integrated all systems seamlessly.
- IDDI performed two sets of programs quality control on each separate database and one final QC after combining the two databases.
- IDDI built additional safeguards for sFTP data transfers.
DOWNLOAD CASE STUDY: Overcoming the challenges of Collaborative Global Trials to deliver Harmonized SAS SDTM Database
A Single Front-End For EDC and Randomization
Full integration between EDC and IRT system
In a double-blind randomized placebo-controlled Phase II study, IDDI set up a complete integration between its EDC system (ID-base™ powered by XClinical) and its IRT system (ID-net). This was a single front-end for the site. Full integration means that the randomization is performed by the site using the EDC system, which calls in the background ID-net randomization web services to perform randomization and treatment allocation. The investigator could therefore randomize patients directly from the EDC system without using the randomization system (ID-net).
In this study, a huge number of subjects had to be screened to ensure a total of 200 fully eligible subjects for treatment. IDDI decided to set up two instances in the EDC system, one for the automatic upload of the data coming from the pre-screened subjects and the other instance for the data collection of the eligible and randomized subjects. This particular set up meant a huge save of time in the study conduct.
Independent Analyses To Inform Go/No-go Decisions
Our client in this case was an investment organization specialized in biotechnology companies, and they required an urgent analysis of clinical data that would be presented on the following week to key decision-makers in the acquisition of a new biotechnology product. The manufacturer of the product had conducted a phase III trial and published the results, but the contract research organization that performed the original statistical analysis was no longer able to provide support to our client. Moreover, there was a need to verify the original results, given the fact that they were negative in the overall trial population, but provided signals of activity in subgroups. By repeating the analysis in only six days and questioning some of the original results presented by the manufacturer in their publication, IDDI was able to better inform their decision-making process.
Clinical Validity Of Circulating Tumor Cells (CTC)
Assessment of Clinical Validity of Circulating Tumor Cells (CTC) Quantification
Clinical Validity Of Circulating Tumor Cells (CTC) In Patients With Metastatic Breast Cancer: A Pooled Analysis of Individual Patient Data
The purpose was to assess the clinical validity of circulating tumor cells (CTC) quantification for prognostication of patients with metastatic breast cancer by undertaking a pooled analysis of individual patient data. IDDI performed this statistical analysis.
These data confirmed the independent prognostic effect of CTC count on progression-free survival and overall survival. This is the first study to show that CTC count also improves the prognostication of metastatic breast cancer when added to full clinicopathological predictive models, whereas serum tumour markers do not.