Project

Back to overview

Predicting the real-world effectiveness and safety of medical interventions

Applicant Efthimiou Orestis
Number 180083
Funding scheme Ambizione
Research institution Institut für Sozial- und Präventivmedizin Universität Bern
Institution of higher education University of Berne - BE
Main discipline Medical Statistics
Start/End 01.10.2018 - 30.09.2022
Approved amount 894'241.00
Show all

All Disciplines (2)

Discipline
Medical Statistics
Methods of Epidemiology and Preventive Medicine

Keywords (5)

biostatistics; evidence-based medicine; prediction modelling; meta-analysis; personalized medicine

Lay Summary (Italian)

Lead
Titolo del progetto di ricercaPredire l'efficacia e la sicurezza degli interventi medici nel mondo realePer molte malattie, ci sono più trattamenti disponibili tra cui scegliere quando si deve trattare un paziente. Un trattamento che funziona molto bene per il paziente medio potrebbe non essere molto efficace o sicuro per un paziente con specifiche caratteristiche. La scelta del trattamento giusto per ciascun paziente è di fondamentale importanza e gli attuali approcci per personalizzare la scelta del trattamento non hanno ancora completamente utilizzato dati raccolti di routine, come la storia della malattia e le caratteristiche del paziente. Questo progetto mira quindi a colmare questa lacuna.
Lay summary

Soggetto e obiettivo

L’obiettivo di questa ricerca è di sviluppare nuovi metodi per personalizzare la scelta del trattamento, in base alle caratteristiche individuali, alle esigenze e alle preferenze dei pazienti che vengono seguiti nella pratica clinica quotidiana. Per far ciò utilizzeremo le evidenze di più studi clinici randomizzati, e di dati raccolti nel mondo reale della pratica clinica.

In particolare, ci proponiamo (i) di sviluppare e testare una gamma di metodi che possono essere utilizzati per predire l’efficacia e la sicurezza degli interventi medici in pazienti con specifiche caratteristiche e (ii) di sviluppare strumenti online di facile utilizzo nei quali i vari metodi verranno implementati, al fine di migliorare il processo decisionale dei clinici nella loro pratica clinica quotidiana

 

Contesto socio-scientifico

Attraverso l’utilizzo di metodi basati sull'evidenza per la scelta del trattamento migliore per ciascun particolare paziente, il nostro lavoro aiuterà la pratica clinica ad andare oltre l'approccio one-size-fits-all per il trattamento dei pazienti. Prevediamo che l'output di questo progetto sarà di grande importanza per ricercatori e metodologi, ma anche per operatori sanitari, pazienti, per coloro che sviluppano linee guida e per le industrie farmaceutiche.

Direct link to Lay Summary Last update: 22.08.2018

Lay Summary (English)

Lead
For many diseases, there are multiple available treatments to choose from when treating a patient. A treatment that performs very well for the average patient might not be very effective or safe for specific types of patients. Choosing the right treatment for each patient is of crucial importance, and current approaches to personalizing the choice of treatment have not yet fully utilized routinely collected data, such as disease history and patient characteristics. This project aims to fill this gap.
Lay summary

Aims of the research project

We aim to develop new methods for personalizing the choice of treatment, based on the individual characteristics, needs and preferences of patients in every-day clinical practice. We will do so by utilizing evidence from multiple randomized clinical trials, as well as data collected under real-world clinical conditions. 

Our objectives are (i) to develop and test a range of methods for making patient-specific predictions about the effects of medical interventions and (ii) to develop easy-to-use online software tools that will utilize all developed methods in order to enhance the decision-making process in every-day clinical settings.


Scientific and societal context of the research project

Our work will help clinical practice move away from the one-size-fits-all approach to treating patients, by using evidence-based methods to choose the best treatment for each particular patient. The output of this project is expected to be of high importance to research scientists and methodologists, but also to health-care professionals, patients, guideline developers and the pharmaceutical industry.

Direct link to Lay Summary Last update: 22.08.2018

Responsible applicant and co-applicants

Employees

Publications

Publication
Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis
Pillinger Toby, McCutcheon Robert A, Vano Luke, Mizuno Yuya, Arumuham Atheeshaan, Hindley Guy, Beck Katherine, Natesan Sridhar, Efthimiou Orestis, Cipriani Andrea, Howes Oliver D (2020), Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis, in The Lancet Psychiatry, 7(1), 64-77.
The dark side of the force: Multiplicity issues in network meta‐analysis and how to address them
Efthimiou Orestis, White Ian R. (2019), The dark side of the force: Multiplicity issues in network meta‐analysis and how to address them, in Research Synthesis Methods, 11(1), 105-122.
Drug-eluting or bare-metal stents for percutaneous coronary intervention: a systematic review and individual patient data meta-analysis of randomised clinical trials
Piccolo Raffaele, Bonaa Kaare H, Efthimiou Orestis, Varenne Olivier, Baldo Andrea, Urban Philip, Kaiser Christoph, Remkes Wouter, Räber Lorenz, de Belder Adam, van 't Hof Arnoud W J, Stankovic Goran, Lemos Pedro A, Wilsgaard Tom, Reifart Jörg, Rodriguez Alfredo E, Ribeiro Expedito E, Serruys Patrick W J C, Abizaid Alex, Sabaté Manel, Byrne Robert A, de la Torre Hernandez Jose M, Wijns William, Jüni Peter, et al. (2019), Drug-eluting or bare-metal stents for percutaneous coronary intervention: a systematic review and individual patient data meta-analysis of randomised clinical trials, in The Lancet, 393(10190), 2503-2510.

Collaboration

Group / person Country
Types of collaboration
Department of Clinical, Neuro- and Developmental Psychology, VU Amsterdam Netherlands (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Institute of Social and Preventive Medicine, University of Bern Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
MRC Clinical Trials Unit at University College London Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Department of Health Promotion and Human Behavior, School of Medicine, Kyoto University Japan (Asia)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Institute of Psychiatry, Psychology and Neuroscience, King’s College London Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Department of Primary Education, School of Education, University of Ioannina Greece (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Department of Psychiatry, University of Oxford Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Abstract

Background: Systematic reviews and meta-analyses of randomized controlled trials (RCTs) are key to comparative effectiveness research. Meta-analyses typically use study-level data and aim to identify which is the best treatment on average. However, a treatment that is best on average may perform less well (or may be harmful) for certain patients. Meta-analysis of individual patient data (IPD) from RCTs can address this issue, by utilizing information on patient-level characteristics. However, RCTs usually employ strict experimental settings and inclusion criteria thus hampering the generalizability of RCT findings in less controlled, ‘real-world’ clinical settings. Recently developed methods attempted to incorporate real-world (observational) studies in evidence synthesis, aiming to bridge this gap. However, these attempts only focused on estimating average treatment effects. To this date, there is a gap in methods for making personalized, patient-specific predictions about the real-world effects of medical interventions.Objectives: The overarching objective of this project is to develop new, reliable methods for jointly synthesising randomized and observational evidence in order to provide personalized predictions for the performance of medical interventions. The project will be implemented with a clear focus on predicting treatment effects in real-life clinical settings. The specific aims of the project are the following: (1) To develop methods for selecting patient characteristics to include in meta-analytical prediction models. (2) To develop methods for predicting the effects of medical interventions in real-world settings in terms of effectiveness and safety, so as to facilitate a personalized choice of treatments.(3) To develop methods for monitoring patient outcomes and adapting treatment decisions.(4) To develop easy-to-use online tools that will utilize all developed methods in order to facilitate and enhance the decision-making process in every-day clinical practice.(5) To provide proof of concept by evaluating the proposed framework in real clinical examples from depression.Methods: For the objectives of this project we will explore and further develop (i) machine-learning methods for identifying patient characteristics with strong predictive properties, at the meta-analysis level; (ii) IPD network meta-analysis methods that will combine randomized and real-world evidence regarding a range of alternative, competing interventions, in order to build prediction models for guiding real-world treatment decisions; (iii) methods for building meta-analytical prediction models for disease progression; (iv) tools for making all results available to patients and doctors through easy-to-use web-applications. All methods that will be developed for the purposes of this project will be showcased using real clinical examples. Impact: This project aims to help every-day clinical practice move away from the one-size-fits-all approach of treating patients, by using evidence-based methods to tailor treatment strategies to the individual characteristics, needs and preferences of patients. Overall, the project represents a novel approach to integrating different pieces of evidence to answer patient-specific, real-world-oriented questions. The scientific output of this project will be of high interest to research scientists, biostatisticians and methodologists, while the applications to real examples are expected to have a strong clinical impact.
-