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Principal Investigator
Name
Marco Bonetti
Degrees
PhD
Institution
Bocconi University
Position Title
Professor of statistics
Email
About this CDAS Project
Study
HIPB (Learn more about this study)
Project ID
HIPB-17
Initial CDAS Request Approval
Jun 30, 2025
Title
A novel mixed model for the analysis of adherence to screening invitations
Summary
Our goal is to add to the set of tools available for the analysis of non-adherence to breast cancer screening. In particular, we will develop and implement a novel mixed model for binary longitudinal outcomes, focusing on the adherence to the screening invitations within the experimental arm of the HIP breast cancer screening trial.
The mixed nature of the models will allow the prediction of the individual risk of non-adherence above and beyond measured risk factors, so that the more non-adherence-prone women may be identified as targets for compliance-inducing interventions, as well as for the further study of the determinants of their behavior.
The new models will represent an alternative to traditional binary GLMM models, and will allow for closed form analytical expressions and computationally efficient and accurate estimation algorithms. As part of this project we will compare the two modeling approaches.
The questionnaires' data will be used as risk modifiers for non-adherence, and any information on diagnoses and deaths will be used to allow the identification of structural reasons for non-adherence.
We will ensure that all necessary privacy safeguards be in place, and that only authorized researchers have access to the data.
Aims

1. Implementation of the authorization process for the HIP study data transfer, both with NCI (through the Cancer Data Access System) and with the Ethics board of our local institution. Transfer of the data files and first descriptive analyses, also with reference to existing publications based on the same data.
2. Fine-tuning of the R software being developed for fitting the new class of models, and fitting of the models until convergence (through model selection) on a parsimonious yet informative model for the individual adherence process.
3. Prediction of the subject specific risk of non-adherence.
4. Comparison of the new models to binary GLLMs with respect to computational effort, stability, interpretation, and prediction of risk of non-compliance.
5. Preparation of manuscript for publication, and dissemination at scientific conferences.

Collaborators

Edoardo Ratti, University of Milan-Bicocca, Milan, Italy