Lead Investigator: Armando Turchetta, McGill University
Title of Proposal Research: Forecasting recruitments in multicenter clinical trials via the time-dependent Poisson-Gamma model
Vivli Data Request: 8767
Funding Source: My research is funded by the Fonds de Recherche du Québec Nature et technologies (FRQNT).
Potential Conflicts of Interest: I am currently employed at F. Hoffmann-La Roche Ltd, i.e. the sponsor of the trials I am requesting access to, however, this research project is only part of my PhD and is fully funded by the Fonds de Recherche du Québec Nature et technologies (FRQNT). Roche (via internal discussions) has kindly agreed to share with me the data of some of their trials to help me complete this project, but it will not fund this research. Therefore, no conflict of interest is expected to occur. A member of Roche will be included in the paper as a co-author (if they wish so) and the funding and affiliation information will be disclosed in the manuscript.
Summary of the Proposed Research:
One of the key challenges in the planning and monitoring phases of multicenter clinical trials is the assessment of the recruitment time as this informs the feasibility of the proposed study. Yet, although an inaccurate estimation of this time may result in a substantial loss of resources, most of the traditional techniques used in recruitment planning are either deterministic or rely on assumptions that are often unrealistic, such as constant recruitment intensity over time. Building on an existing recruitment forecasting model, the Poisson-Gamma model, we developed a flexible generalization of this methodology called the time-dependent Poisson-Gamma model (tPG), which is suited to estimate a wide range of recruitment behaviors over time. We have already validated this model using recruitment data from a cohort study, showing a significant improvement over the standard Poisson-Gamma model.
A realistic estimate of the time needed to achieve the target sample size is an important piece of information that can drive significant financial and practical decisions in multicenter studies, such as the opening or closure of recruitment centers and the allocation of resources. The main goal of this project is to show the applicability of the time-dependent Poisson-Gamma model for forecasting enrollments in multicenter clinical trials, as well as its advantages over the commonly used methodology based on constant rates over time. In turn, this study will help with the monitoring phase of future clinical trials, as the resulting paper will also outline the functionalities of the R package we built to implement this methodology and how to use them in practice.
Requested Studies:
A Randomized Three-Arm, Multicenter Comparison of 1 Year and 2 Years of Herceptin Versus No Herceptin in Women With HER2-Positive Primary Breast Cancer Who Have Completed Adjuvant Chemotherapy
Data Contributor: Roche
Study ID: NCT00045032
Sponsor ID: BO16348
A Randomized Multicenter, Double-Blind, Placebo-Controlled Comparison of Chemotherapy Plus Trastuzumab Plus Placebo Versus Chemotherapy Plus Trastuzumab Plus Pertuzumab as Adjuvant Therapy in Patients With Operable HER2-Positive Primary Breast Cancer
Data Contributor: Roche
Study ID: NCT01358877
Sponsor ID: BO25126
A Randomized, Double-blind Study of Safety and Reduction in Signs and Symptoms During Treatment With Tocilizumab Versus Placebo, in Combination With Methotrexate, in Patients With Moderate to Severe Rheumatoid Arthritis
Data Contributor: Roche
Study ID: NCT00106548
Sponsor ID: WA17822
A Randomized, Double-blind Study of the Effect of Tocilizumab on Reduction in Signs and Symptoms in Patients With Moderate to Severe Active Rheumatoid Arthritis and Inadequate Response to DMARD Therapy
Data Contributor: Roche
Study ID: NCT00106574
Sponsor ID: WA18063