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Enhancing methods for evaluating the comparative safety of medical interventions

English title Enhancing methods for evaluating the comparative safety of medical interventions
Applicant Salanti Georgia
Number 166656
Funding scheme Project funding (Div. I-III)
Research institution Institut für Sozial- und Präventivmedizin Universität Bern
Institution of higher education University of Berne - BE
Main discipline Medical Statistics
Start/End 01.09.2016 - 30.11.2018
Approved amount 272'500.00
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All Disciplines (3)

Discipline
Medical Statistics
Methods of Epidemiology and Preventive Medicine
Public Health and Health Services

Keywords (4)

adverse events; correlated data; meta-analysis; rare outcomes

Lay Summary (German)

Lead
Methoden zur Meta-Analyse von Sicherheitsdaten
Lay summary

Wissenschaftler fassen systematisch die Ergebnisse aus mehreren Studien zusammen um z.B. zu entscheiden, ob ein Medikament wirksamer oder sicherer ist als ein anderes. Diese quantitative Zusammenfassung der Ergebnisse von mehreren Studien wird Meta-Analyse genannt. Diese Form der Analyse wird heutzutage von nationalen und internationalen Institutionen im Gesundheitswesen, wie beispielsweise der Weltgesundheitsorganisation WHO, routinemässig eingesetzt bei der Erstellung von Richtlinien und um ganz allgemein die Entscheidungsfindung im Gesundheitswesen zu unterstützen.

Meta-Analysen über die Sicherheit von Interventionen bringen spezifische technische Herausforderungen mit sich. Die notwendigen statistischen Methoden für solche Analysen sind noch nicht voll entwickelt insbesondere in Situationen in denen mehr als zwei Interventionen miteinander verglichen werden sollen oder verschiedene Arten von Nebenwirkungen in einer Analyse berücksichtigt werden.

Das Ziel dieses Projektes ist es, die aktuell verfügbaren Methoden zur Meta-Analyse von Sicherheitsdaten von medizinischen Interventionen weiterzuentwickeln und neue statistische Ansätze zu entwickeln. Die Methoden werden ausgehend von einer Vielzahl realer Datensätze aus den Bereichen  Orthopädie, Psychiatrie, Kardiologie etc. entwickelt. Das Projekt wird dazu beitragen, dass zukünftig geeignetere Methoden zur Beurteilung der Sicherheit von medizinischen Interventionen zur Verfügung stehen und dadurch letztendlich einen Beitrag zur Risikominimierung für Patienten und Patientinnen leisten.
Direct link to Lay Summary Last update: 31.03.2016

Responsible applicant and co-applicants

Employees

Publications

Publication
Second-generation antipsychotic drugs and short-term mortality: a systematic review and meta-analysis of placebo-controlled randomised controlled trials
Schneider-Thoma Johannes, Efthimiou Orestis, Huhn Maximilian, Krause Marc, Reichelt Leonie, Röder Hannah, Davis John M, Salanti Georgia, Leucht Stefan (2018), Second-generation antipsychotic drugs and short-term mortality: a systematic review and meta-analysis of placebo-controlled randomised controlled trials, in The Lancet Psychiatry, 5(8), 653-663.
Practical guide to the meta-analysis of rare events
Efthimiou Orestis (2018), Practical guide to the meta-analysis of rare events, in Evidence Based Mental Health, 21(2), 72-76.
A model for meta-analysis of correlated binary outcomes: The case of split-body interventions
Efthimiou Orestis, Mavridis Dimitris, Nikolakopoulou Adriani, TrelleSven, EggerMatthias, SalantiGeorgia (2017), A model for meta-analysis of correlated binary outcomes: The case of split-body interventions, in Statistical Methods in Medical Research.

Collaboration

Group / person Country
Types of collaboration
Center for Medical Biometry and Medical Informatics, University of Freiburg Germany (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
School of Social and Community Medicine, University of Bristol Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
26th Cochrane Colloquium, Edinburgh, September 2018 Poster A Mantel-Haenszel model for network meta-analysis of rare events 16.09.2018 Edinburgh, Great Britain and Northern Ireland Efthimiou Orestis;
Invited talk to the University of Ioannina Individual talk Methodological developments in evidence synthesis 24.07.2018 Ioannina, Greece Efthimiou Orestis;
Joint RSS South West Local Group and Exeter Health Statistics Event Individual talk Recent advances in evidence synthesis 20.07.2018 Exeter, Great Britain and Northern Ireland Efthimiou Orestis;
Annual meeting of the Society of Research Synthesis Methods Talk given at a conference A Mantel-Haenszel model for network meta-analysis of rare events 17.07.2018 Bristol, Great Britain and Northern Ireland Efthimiou Orestis;
CFE-CM Statistics Talk given at a conference Meta-analysis of correlated binary outcomes: the case of split-body interventions 17.12.2017 London, Great Britain and Northern Ireland Efthimiou Orestis;


Associated projects

Number Title Start Funding scheme
179158 What works best? Methods for ranking competing treatments in network meta-analysis 01.06.2018 Project funding (Div. I-III)

Abstract

Background: A synthesis of 42 trials on the safety of rosiglitazone found an increased risk for myocardial infarction and cardiovascular death. This highly influential and publicized meta-analysis resulted in the withdrawal of rosiglitazone from the European market. However, the statistical methodology employed was debated and in subsequent analyses of the same data a son-significant association was suggested casting doubts about the validity of the conclusions drawn in the original analysis. Such conflicting evidence arises because of important methodological gaps in the synthesis of data about the safety of medical interventions. Treatment adverse events (AEs) are typically rare and this poses technical constraints rendering standard statistical methodology impertinent. Although meta-analytic methods for sparse data are available, their applicability is limited in practice because they pertain to the case of a single outcome (univariate meta-analysis) and a single treatment comparison (pairwise meta-analysis). Studies typically report on multiple AEs measured on the same set of patients data so that data is often highly correlated; correlations might also arise as the result of study design, e.g. in cross-over trials. Also, in most medical areas there are multiple available treatments for the same disease, but the currently available methods for the synthesis of sparse data cannot be readily used for network meta-analysis, a statistical technique compiling evidence about multiple competing interventions. Aim: The aim of this project is to advance the methods for synthesizing evidence from randomized trials on the safety of interventions by developing and exploring meta-analytical models for correlated rare events and network meta-analysis of AEs. Methods: Developments will be stimulated by a range of datasets in a variety of clinical research areas. Our starting methodological point will be previous work performed in the area of rare outcomes meta-analysis. We will consider methods that do not assume a normal approximation to dichotomous or count data and do not need the much-criticized continuity correction for studies bearing arms with zero events. These include, but are not limited to, logistic regression, using the arcsine difference, Poisson models and a Beta-Binomial regression model. We will extend such methods for the case of two correlated outcomes by employing their multivariate versions. Most multivariate approaches (e.g. using the multinomial distribution) need detailed input data such as the number of patients with events for both considered outcomes while other multivariate models (e.g. the multivariate Poisson model) need input about the sample correlations. As such information is rarely reported in articles, we will employ a bivariate-Binomial model and we will consider previous work that utilizes external information to bypass the problem of unreported correlations. We will undertake a simulations study to evaluate the properties of the multivariate methods and their advantages compared to univariate approaches. All developments will be programmed in open-source software and routines will be made available. Significance: Multivariate approaches are expected to increase precision in the estimation of the treatment effects. Such an increase in precision is highly desirable in the area of safety where events are rare. Our project is expected to enrich the meta-analytic arsenal with useful tools to assist decision makers in establishing the risk-benefit profile of many competing interventions.
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