This document discusses open access from the perspective of a researcher. It describes the researcher's publishing process and costs of open access fees. While open access ensures dissemination, the current system favors publishers over researchers. The researcher argues for alternative open access models that cover research costs while providing open dissemination of publications, data, and software at no additional expense.

1. Open access: a researcher’s perspective
Antonio Gasparrini
London School of Hygiene and Tropical Medicine, London, UK
Open Research and Data
Open Access Week
22 October 2012 - Birbeck College, London

2. My background
Graduated in biology in Italy, then 4 years working as
epidemiologist in a cancer research center in Florence
MSc + postgraduate school (still in Italy) + PhD (in UK) in
medical statistics,
Worked at LSHTM in the last 5 years, mainly in statistical
methodology and software development

3. My MRC fellowship
Awarded a Research Methodology fellowship from MRC (Dec
2011 – Nov 2014)
Project developed on my previous research
Success of this project critical for next funding application
Need to comply with the MRC regulations on open access
My budget for open access costs: 6000£ in total

4. Outline
Some points:
My perspective: as scientist and junior academic
Publishing: steps and costs
My publications as a case study
Open research: beyond publications

5. The scientist’s perspective
I favour a system which:
guarantees high-quality research
allows the independent assessment of research findings
ensures the dissemination of the such findings

6. The academic’s perspective
I favour a system which:
covers the costs of my research
delivers a fast and effective peer-review process
provides tools for disseminating my work

7. Publishing a research paper: steps
Literature review
Drafting the manuscript
Choice of the journal and submission
Review and acceptance
Copyright agreement
Open access fee
Publication
Actors: the researcher, the institution, the research community,
the funder, the journals/publishers
An efficient and fair system?

8. A first article
Published online in Statistics in Medicine (2012):
The choice of the journal
Copyright transferred
Open access fee ∼2250£
Impact factor 1.99

9. Submitted and published versions
Research Article
Statistics
in Medicine
Received XXXX
(www.interscience.wiley.com) DOI: 10.1002/sim.0000
Multivariate meta-analysis for non-linear and
other multi-parameter associations
A. Gasparrinia∗†, B. Armstrongb, M. G. Kenwarda
In this paper we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates
of multi-parameter associations obtained from different studies. This modelling approach extends the standard
two-stage analysis used to combine results across different sub-groups or populations. The most straightforward
application is for the meta-analysis of non-linear relationships, described for example by regression coefficients
of splines or other functions, but the methodology easily generalizes to any setting where complex associations
are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is
implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose
a two-stage analysis for investigating the non-linear exposure-response relationship between temperature and non-
accidental mortality using time series data from multiple cities. Multivariate meta-analysis represents a useful
analytical tool for studying complex associations through a two-stage procedure. Copyright c 2011 John Wiley &
Sons, Ltd.
Keywords: meta-analysis; multivariate analysis; multivariate meta-analysis; non-linear; splines
1. Introduction
Meta-analysis is a standard, well-grounded statistical procedure for combining the evidence from independent studies
that address the same research hypothesis [1]. This methodology was developed originally for pooling the results from
published observational or experimental studies, for which individual data were not available. Recently, meta-analysis has
been described more broadly as a research synthesis method, with the aim of estimating an average association across
studies and to explore the degree and sources of heterogeneity [2]. The analytical approach adopted in this context may be
described as a two-stage hierarchical procedure: in the first stage, study-specific estimates of the association of interest are
derived from individual data, controlling for individual-level covariates; in the second stage, these estimates are combined
across studies, optionally exploring the association with study-level predictors. The two-stage approach, a specific form
of individual patient data (IPD) meta-analysis, has been shown to be a flexible and computationally efficient method [3],
and has been adopted in different contexts: to pool estimates from multiple randomized controlled trials [4]; to combine
results from survival models on time-to-event data in multi-centre cohorts [5]; and to synthesize associations from Poisson
time series models in multi-city analyses [6].
The common approach to two-stage meta-analysis consists of summarizing the association in a single parameter
estimate from the first stage, optionally controlling for individual-level confounders. This procedure allows standard meta-
analytic techniques to be applied. However, complex associations, such as non-linear exposure-responses, are usually
described with functions defined by multiple parameters, and require more sophisticated meta-analytical approaches,
capable of handling the multivariate nature of the summary estimates. Multivariate meta-analysis, a method originally
a
Department of Medical Statistics, London School of Hygiene and Tropical Medicine
b
Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine
∗
Correspondence to: Antonio Gasparrini, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
†
E-mail: antonio.gasparrini@lshtm.ac.uk
Contract/grant sponsor: Medical Research Council (UK), grants G0701030 and G1002296
Statist. Med. 2011, 00 1–18 Copyright c 2011 John Wiley & Sons, Ltd.
Prepared using simauth.cls [Version: 2010/03/10 v3.00]
Research Article
Received 9 August 2011, Accepted 11 May 2012 Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/sim.5471
Multivariate meta-analysis for
non-linear and other multi-parameter
associations
A. Gasparrini,a*† B. Armstrongb and M. G. Kenwarda
In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize
estimates of multi-parameter associations obtained from different studies. This modelling approach extends
the standard two-stage analysis used to combine results across different sub-groups or populations. The most
straightforward application is for the meta-analysis of non-linear relationships, described for example by
regression coefficients of splines or other functions, but the methodology easily generalizes to any setting
where complex associations are described by multiple correlated parameters. The modelling framework of
multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As
an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response
relationship between temperature and non-accidental mortality using time-series data from multiple cities.
Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a
two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd.
Keywords: meta-analysis; multivariate analysis; multivariate meta-analysis; non-linear; splines
1. Introduction
Meta-analysis is a standard, well-grounded statistical procedure for combining the evidence from inde-
pendent studies that address the same research hypothesis [1]. This methodology was developed origi-
nally for pooling the results from published observational or experimental studies, for which individual
data were not available. Recently, meta-analysis has been described more broadly as a research synthesis
method, with the aim of estimating an average association across studies and to explore the degree and
sources of heterogeneity [2]. The analytical approach adopted in this context may be described as a
two-stage hierarchical procedure: in the first stage, study-specific estimates of the association of interest
are derived from individual data, controlling for individual-level covariates; in the second stage, these
estimates are combined across studies, optionally exploring the association with study-level predictors.
The two-stage approach, a specific form of individual patient data (IPD) meta-analysis, has been shown
to be a flexible and computationally efficient method [3] and has been adopted in different contexts: to
pool estimates from multiple randomized controlled trials [4]; to combine results from survival models
on time-to-event data in multi-centre cohorts [5]; and to synthesize associations from Poisson time-series
models in multi-city analyses [6].
The common approach to two-stage meta-analysis consists of summarizing the association in a single
parameter estimate from the first stage, optionally controlling for individual-level confounders. This
procedure allows standard meta-analytic techniques to be applied. However, complex associations, such
as non-linear exposure–responses, are usually described with functions defined by multiple parameters
and require more sophisticated meta-analytical approaches capable of handling the multivariate nature
of the summary estimates. Multivariate meta-analysis, a method originally developed to pool multiple
aDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
bDepartment of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, U.K.
*Correspondence to: Antonio Gasparrini, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E
7HT, U.K.
†E-mail: antonio.gasparrini@lshtm.ac.uk
Copyright © 2012 John Wiley & Sons, Ltd. Statist. Med. 2012

10. A second article
Firstly submitted to Biostatistics:
Copyright transferred
Open access fee ∼2250£
Impact factor 2.145
Rejected, re-submitted to BMC Med Res Method:
Copyright retained
Open access fee ∼1475£ (∼1255£ with LSHTM discount)
Impact factor 2.67

11. A third article
Published in Journal of Statistical Software (2011):
Not automatically indexed in PubMed
Included ’manually’ through PubMed Central
Copyright retained
Open access fee: 0£
Impact factor 4.01

12. Open research: beyond publishing
Open data: research data collected with public funding available
to other researchers
Open source and free software
Reproducible research: open and thorough assessment of
research findings

13. A similar case
Statistical software is mainly based on commercial programs
(e.g. Stata, SAS, SPSS)
Substantial fees to be paid by research institutions
However, implementation of novel methodologies provided by
researchers
Same story: researchers working (for free) for third parties...

14. An alternative model
An example: the R software
A project entirely based on a community of users and developers
Comparison with commercial programs
Model also applicable to publishing

15. The third article again
The manuscript is freely available at journal’s web site and other
repositories
The code for the analysis is included as supplementary material
The software is implemented and fully documented in a free
statistical package
The data are stored online and freely available through the
software
All of this at no cost

16. The internet era
Different approach to search and dissemination: what role for
journals?
Drop in editorial and publication costs: do we really need
publishers?
Role of funders, institutions and research community is critical
Why so late?!

17. The open access era
Important changes: Wellcome and RCUK policies
Limitations of the Finch Report
Alternative models already available
Changes require a different approach from researchers