Gergő Merész 1, Gergely Csaba Gyurcsán2, Balázs Salfer 3 1 Syreon Research Institute, Budapest, Hungary

2 Self-employed, Budapest, Hungary

3 Healthware Consulting Ltd., Budapest, Hungary

INTRODUCTION

The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) is approaching its 10th anniversary which offers a suitable occasion to assess the impact of the organization. As the number of members increased (up to 8,000 in the year of 2014), the influence of the Board of Directors (BoD) on scientific discussion emerged in parallel. BoD members get elected for a two-year term, and among others, they have a role in supervising participation groups. Therefore, the activity of BoD members receive distinguished attention which increases the importance of their publications. The increased importance may induce more publications, and can also benefit the coauthors. In general, epidemiological and data mining techniques are rarely used in scientometric research, potentially because of the diversity in data sources. In this research, we attempt to overcome this barrier by using some elements of epidemiological study design and a computer algorithm to help data collection and analysis. The aim of this research is to present what effect being elected an ISPOR Director has on a researcher’s publication activity by using epidemiological methods and data mining techniques.

METHODS

Data Sources The researchers available for inclusion (former BoD members) were selected with the use of the list of the BoD members available on the ISPOR website1 for the years 1995-2012. BoD members of the years 2013-2014 were not considered, as these members did not finish their term up to the start of the present research, and therefore, the studied effect would somewhat differ among subjects. Data on number of publications by year, co-authors, titles, abstracts of former BoD members between 1995 and 2012 were obtained from the PubMed database2.

Data analysis The collected data was cleaned analysed by an algorithm developed by the authors in R3. This algorithm constructed search terms from user input data (e.g. author names), sent it to the PubMed database, and collected search results, which were cleaned and ordered for further analysis. A case-only study design4 was applied by matching the duration spent as a member of the BoD with the same period prior to and after finishing the directorate term. It was assumed that publication activity has a natural increasing trend over time, as the researcher becomes more experienced and acknowledged in a given field. Therefore, it was necessary to adjust the estimate of the studied effect for this natural increase. Incidence rate ratios (IRR) were estimated by fitting separate Poisson regression models to correct for the natural increase in publication activity. The average number of coauthors and probability of the director being the first author was also analysed.

RESULTS

Descriptive statistics A total of 636 identical publications authored under directorate were identified for the 51 BoD members between the years 1995 and 2012. The annual number of publications co-authored by BoD members was the highest in 2010 (54), and the lowest in 2004 (17), yielding an annual average of 36.06 for the entire term. The mean annual number of publications preceding, during, and succeeding directorate was 2.62, 3.97, and 4.32, respectively.

Estimation of incidence rate ratios In order to correct for the bias presented previously, the year of publication was included in the model as a covariate. The final model included a binary variable to indicate whether or not the publication was authored as a BoD member. The following model was estimated for the period preceding to directorate versus directorate period:

ln λ = 0.976 + 0.388 ∗ 𝑖𝑓 𝑑𝑖𝑟𝑒𝑐𝑡𝑜𝑟𝑎𝑡𝑒 𝑦𝑒𝑎𝑟 + 0.022 ∗ [𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 𝑦𝑒𝑎𝑟]

The following model was estimated for the directorate period versus the period succeeding directorate:

ln λ = 1.402 + 0.013 ∗ 𝑖𝑓 𝑠𝑢𝑐𝑐𝑒𝑒𝑑𝑖𝑛𝑔 𝑦𝑒𝑎𝑟 + 0.031 ∗ [𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 𝑦𝑒𝑎𝑟]

The IRR of the period preceding to directorate versus the directorate period was 1.59 (CI 95%: 1.34-1.91), yielding statistically significant association. The IRR of directorate period versus the period succeeding the directorate period was 1.01 (CI 95%: 0.88-1.13). The average number of co-authors was the highest after finishing the directorate term (5.94); the probability of the director appearing as the first author was the highest prior to directorate term (22.91%). Also, 12.69% of coauthors in the directorate term were not coauthoring a publication with the given director in the preceding term, whereas only 5.41% of coauthors were new in this sense in the follow-up term, versus directorate.

CONCLUSIONS

Based on the results of our study, it has been statistically proven that being an ISPOR Board of Directors member does not only provide leadership in a scientific organization, but can enhance the members’ career as a researcher. ISPOR Directors are more likely to coauthor publications, even after finishing their directorate term. In terms of study design, epidemiological methods were used to carry out the analysis. The case-only study (or more accuretly, the self-controlled case series) design was adapted to reduce confounding by using the exposure history of each case as its own control. By this method, subjects will have constant characteristics, and – as relatively high quality follow-up data is available – the effect of exposure (being elected as a director) can be evaluated. Choosing a different control group would have been difficult: the control could be a random researcher having the same number of publications in the given year(s); the candidates who were not elected; or even the coauthors of the directors. The next step for this research could be analysing the content of publications.

REFERENCES 1 ISPOR Board of Directors http://www.ispor.org/board/index.asp [last accessed: 10th September, 2014] 2 PubMed Health [Internet]. Bethesda (MD): National Library of Medicine (US) [last accessed 10th September 2014] Available from: http://www.ncbi.nlm.nih.gov/pubmedhealth/ 3 R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ 4 Suissa S. The case-time-control design. Epidemiology. 1995 May;6(3):248-53.

The Poisson regression model used for this analysis can be written as the following:

ln λ = 𝛼 + 𝛽𝑖 ∗ 𝑋𝑖

And therefore, the exponential of 𝛽𝑖 can be interpreted as the incidence rate ratio of the exposure 𝑋𝑖.