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.
A Double Masked Minimization in Ophthalmology Study
Phase II study conducted with a dose-escalation minimization
A 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
Biostatistical Expertise For Developing New Biomarkers And Diagnostic Tests
IDDI 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.