Success stories

The following success stories demonstrate how IDDI innovative Biostatistics and eClinical solutions are key in the successful conduct and outcome of your clinical trials.

Case study: The REALI pooled analysis of the European post-marketing studies for the TOUJEO® product

Success stories

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.


  • Statistical Analyses Plan
  • SDTM Mapping for the pooling
  • Statistical Programming
  • Statistical Analyses


  • 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 

Success stories

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.


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

Success stories

Full integration between EDC and IRT system

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

Success stories

Independent AnalysesOur 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)

Success stories

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

circulating tumor cellsThe 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.

A Double Masked Minimization in Ophthalmology Study

Success stories

Phase II study conducted with a dose-escalation minimization

Ophthalmology studyA phase II ophthalmology study was conducted with a dose-escalation minimization to evaluate the safety, tolerability and efficacy of a treatment in reduction of intraocular pressure in subjects with ocular hypertension or primary open-angle Glaucoma. Double Masked Minimization in Ophthalmology Study: first the kits were randomized to eye (OD or OS) and then the randomization system (ID-net) performed a stratified minimization on treatment (active or placebo). The dose escalation was managed by using different cohorts automatically closed by the system once the number of expected patients was reached. Our randomization system is flexible and can adapt to studies with a very complex design. It is supported by a 24/7 Help Desk which allows an optimal interaction with Sponsors and Sites during the study.

Developing New Biomarkers And Diagnostic Tests

Success stories

Biostatistical Expertise For Developing New Biomarkers And Diagnostic Tests

Biostatistical ExpertiseIDDI has a long-term collaboration with a Sponsor to provide biostatistical expertise for developing new biomarkers and diagnostic tests based on Sponsor’s proprietary micro-array technology and to perform statistical analyses of clinical studies of diagnostic tests, using the “Nearest Centroïd Prediction Rule”and the “Leave-One-Out Cross-Validation” method.

Bayesian Study Design

Success stories

A Bayesian Study Design For Seamless Transition Between Phase II And Phase III

Bayesian study designData collected by a sponsor suggested a possibility of a differential treatment in subpopulations defined by levels of a biomarker. A Bayesian study design has been developed, which allows the sponsor to conduct a randomized Phase II trial to corroborate the finding and to seamlessly continue to Phase III in the subpopulation, which benefits from the treatment. The study design was consulted and accepted by a regulatory agency.

Flexible Study Design

Success stories

A Flexible Study Design with Randomization to Placebo

Flexible study designThe maximum tolerated dose of a new drug, supposed to alleviate some adverse effects of anti‑cancer chemotherapy, was to be established in a phase I trial. To assess the safety of the drug, information on the background toxicity rate in the enrolled sample had to be collected. By using state‑of‑the‑art methodology, a flexible study design was proposed for the trial. It combined the use of the continual reassessment method on a continuous dose scale with a concurrent randomization to placebo. The properties of the design were investigated by conducting a simulation study, which allowed to fine-tune the final form of the trial design. The trial has been completed successfully.

Discovering New Prognostic Biomarkers For Breast Cancer

Success stories

Prognostic Biomarkers

New prognostic biomarkers for breast cancer: A range of biological markers were analyzed by polymerase chain reaction and immunohistochemistry in tissue microarrays from patients treated in several past and ongoing clinical trials for breast cancer. The obtained marker measurements were combined with the clinical data and analyzed using survival analysis methodology, including some advanced modeling techniques, to investigate whether any of the markers had prognostic value. The investigation was successful for at least one of the potential markers analyzed. The project is ongoing to validate further biologically interesting candidate biomarkers.