Methods and characteristics of published network meta-analyses using individual patient data: protocol for a scoping review.
BACKGROUND
Individual patient data (IPD) meta-analysis (MA) offers advantages over aggregate MA of using standardised criteria for patient characteristics across trials, and allowing reliable investigation of subgroup effects of interventions. Network meta-analysis (NMA) allows for the comparison of multiple treatments in a comprehensive analysis and the determination of the best treatment among several competing treatments, including those that have never been compared in a head-to-head study. Including IPD in NMA may enable the prevention of misleading inferences due to several biases, such as aggregation bias. Application of IPD-NMA methods in healthcare have begun to appear in medical journals. Our objective is to conduct a scoping review of existing IPD-NMA methods, and summarise their properties. We also aim to describe the characteristics of empirical IPD-NMAs, and examine how their results are reported.


METHODS
We will search relevant electronic databases from inception until October 2014 (eg, MEDLINE), grey literature, and Google. The scoping review will consider published and unpublished papers that report completion of an IPD-NMA, describe a method, or report the methodological quality of IPD-NMA. We will include IPD-NMA of any quantitative study (eg, experimental, quasiexperimental, observational studies). Two reviewers will independently screen titles, abstracts and full-text articles, and will complete data abstraction. The anticipated outcome will be a collection of all the IPD-NMAs completed to date, and a description of their methods and reporting of results. We will create summary tables providing the characteristics of the included studies, and the various methods. Quantitative data (eg, number of patients) will be summarised by medians and IQRs, and categorical data (eg, type of effect size) by frequencies and percentages.


BACKGROUND
Ethical approval is not required as our study will not include confidential participant data and interventions. We will disseminate our results through an open access, peer-reviewed publication.
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.
Areti Angeliki
AA
Veroniki
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada Institute of Health Policy Management & Evaluation, University of Toronto, Toronto, Ontario, Canada.
Charlene
C
Soobiah
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.
Andrea C
AC
Tricco
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.
Meghan J
MJ
Elliott
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada Department of Geriatric Medicine, University of Toronto, Toronto, Ontario, Canada.
Sharon E
SE
Straus
D015982:Bias; D006801:Humans; D033181:Information Dissemination; D015201:Meta-Analysis as Topic; D011643:Publishing; D012107:Research Design; D000098584:Scoping Reviews as Topic
individual patient data; knowledge translation; mixed treatment comparison; multiple treatments meta-analysis; network meta-analysis; research methods