International Statistical Institute Research Articles

Rate and Predictors of the Conversion of Abstracts Presented at the Canadian Cardiovascular Congress Scientific Meetings to Full Peer-Reviewed Publications

The rate of conversion of abstracts presented at scientific meetings into peer-reviewed published manuscripts is an important metric for medical societies, because it facilitates translation of scientific knowledge into practice. We determined the rate and predictors of conversion of scientific abstracts presented at the Canadian Cardiovascular Congress (CCC) from 2006 to 2010 into peer-reviewed article publications within 2 years of their initial presentation. Using a previously validated computer algorithm, we searched the International Statistical Institute Web of Science to identify peer-reviewed full manuscript publications of these abstracts. A multivariable logistic regression was used to identify independent factors associated with successful publication. From 2006 to 2010, 3565 abstracts were presented at the CCC. Overall 24.1% of presented abstracts were published within 2 years of the conference. Mean impact factor for publications was 5.2 (range, 0.4-53.2). The type of presentation (for poster vs oral; odds ratio, 0.71; 95% confidence interval, 0.60-0.83; P < 0.001) and category of presentation (P < 0.001) were significantly associated with successful publication. Late breaking abstracts and those related to cancer and clinical sciences were more likely to be published, compared with prevention, vascular biology, and pediatrics. In conclusion, the publication rate at the CCC is only marginally lower than that reported for large international North American and European cardiology conferences (30.6%). Efforts should focus on several identified barriers to improve conversion of abstracts to full report publication.

The association of SIPA1 gene polymorphisms with breast cancer risk: evidence from published studies

Previous studies have focused on the association of signal-induced proliferation associated 1 gene (SIPA1) with carcinogenesis of many cancers, including breast cancer. It has been suggested that SIPA1 polymorphisms are associated with susceptibility to breast cancer. In the present study, we performed a meta-analysis to systematically summarize the possible association between SIPA1 and the risk for breast cancer. We conducted a search of case-control studies on the associations of SPIA1 with susceptibility to breast cancer in PubMed, Embase, International Statistical Institute Web of Science, Wanfang Database in China, and Chinese National Knowledge Infrastructure databases. Data from eligible studies were extracted for meta-analysis. Breast cancer risk associated with SIPA1 was estimated by pooled odds ratios and 95% confidence intervals. Four studies on SIPA1 and breast cancer were included in our meta-analysis. Our results showed that rs746429 was associated with the risk of breast cancer. However, rs931127 and rs3741378 were not found to be associated with breast cancer in our analysis. This meta-analysis suggests that rs746429 is associated with the risk of breast cancer. Well-designed studies with larger sample size and more ethnic groups are required to further validate the results.

Spatial statistics for environmental studies

This special issue follows the conference “Spatial Data Methods for Environmental and Ecological Processes—2nd Edition” which started at the University of Foggia, Italy, and continued in the beautiful scenery of Baia delle Zagare on the 1st and 2nd of September 2011. The conference was the 2011 European Regional Conference of The International Environmetrics Society (TIES) and a satellite of the 58th World Statistics Congress of the International Statistical Institute (ISI). Importantly, the conference was structured on the basis of a largely interdisciplinary project with the aim of creating a space for the exchange of experiences and ideas among researchers from different scientific backgrounds working on spatial and spatiotemporal environmental problems. The theme of the workshop provided ample space for contributions covering a wide range of territorial, ecological and environmental topics. The Conference’s Scientific Committee tailored the program in such a way as to provide the fruitful interaction of various different fields, under the common banner of ‘spatial analysis’. The seven papers of this special issue have a strong motivation on clearly stated environmental problems and consider advanced spatial modeling issues. Three of them are related to advanced spatiotemporal modeling of air quality data at regional scale. Another two papers consider spatial epidemiology, in particular plant infections and malaria. Moreover, one work faces spatial modeling of rainfall extremes at

Józef Buzek (1873-1936), statystyk, ekonomista, prawnik, polityk

This article presents the professor’s Józef Buzek comprehensive activity. This lawyer and politician was well-deserved for the rebirth of the Polish state after 1918, and the prominent statistician – the organizer of the CSO, the head of the Office until 1929. The paper discusses in detail the role of the professor, as the creator of the CSO, the organizer of Polish research statistics and initiator of the organized, for the first time in Warsaw, Session of the International Statistical Institute in 1929. The article, printed in the 94th anniversary of the appointment of Professor Józef Buzek as the first director of the CSO, discusses his scientific activity, and brings his broad interest in the field of economics and law.

Development of ICD 11: changes and challenges

Hospitals are data hungry organization, and store tons of patient level data in the patient’s clinical notes. This data cannot be used in the day to day decision making because it is not standardized. That was the rationale that started the movement to standardize the clinical documentation which resulted in the development of clinical coding standards based on disease classification. Disease classification has its roots in the classes of causes of death, which started as early as mid 18 century. The classes for the causes of death was formalized by the International Statistical Congress that later evolved into the International Statistical Institute. International Statistical Institute later collaborated with Health Organization of the League of Nations, the precursor of the World Health Organization to incorporate the classification of morbidities, and later on the role for updating and maintenance was wholly taken over by the WHO. The last revision (10 Revision) of the International Classification of Disease was carried out in 1989 and is famously known as ICD 10. ICD 10 has served its purpose very well, but with the rapid change in the technologies and way the data is managed in hospitals, it is fast becoming obsolete. Therefore WHO has decided to revise the current version of the ICD 10, and come up with ICD 11 that fulfills all the requirements a modern healthcare system. ICD 11 will be a comprehensive classification that can be used by all the different stakeholders in the healthcare environment, can be integrated into the digital hospital information system and other terminologies such as SNOMED CT. For that purpose WHO has come up with a comprehensive program to involve the different stakeholders in the development of the ICD 11 that hopefully, will be commissioned in 2015. Author details International Training Center for Casemix and Clinical Coding, UKM Medical Centre, Cheras Kuala Lumpur, Malaysia. Department of Community Health, UKM Medical Centre, Cheras Kuala Lumpur, Malaysia.

Stochastic modeling and optimization (in honor of András Prékopa’s 80th birthday)

The area of optimization of stochastic models has been experiencing rapid development in the last several decades. Experts in probability and statistics, optimization theory, simulation, and stochastic control have collaborated to create new models, new analytical techniques, and new numerical methods, with an associated multitude of applications. Andras Prekopa is one of the founders of this area, but he is also an active contributor and organizer. His remarkable scientific career, spanning over six decades, is impossible to summarize here. He has published more than 15 books and 350 scientific papers. His 1995 book Stochastic Programming is a comprehensive treatise on the area, and became a standard reference. An overview of his scientific contributions is provided in the special article following this introduction. Professor Prekopa supervised more than 50 doctoral students and is currently directing a doctoral program at Rutgers University. He is a member of the Hungarian Academy of Sciences, a foreign member of the National Academy of Engineering of Mexico, a fellow of the Econometric Society, a member of the International Statistical Institute, and the honorary president of the Janos Bolyai Mathematical Society and the Hungarian Operations Research Society. He is the founder of the sequence of international conferences on stochastic programming (held every three years since the 1981 meeting in Kőszeg, Hungary) and of

About the Authors

About the Authors

Special Issue on Spatial Multivariate Methods

This special issue is dedicated to spatial multivariate methods and addresses the emergence of the latest practical and theoretical aspects on this topic. The papers herein present a wide spectrum of recent advances in methods and applications in diverse areas. It grew out of an invited paper session of the International Statistical Institute meeting that was held in Durban, South Africa, in 2009. This special issue, comprised of seven papers, a teaching aid and short note, aims to broaden interest and offer insights into current trends and future directions of spatial multivariate methods. The issue begins with Bailey and Krzanowski’s overview of the existing approaches for the analysis of geostatistical multivariate data. These approaches are divided into classes of factor models or spatial random field models; and their differences are discussed. Their paper introduces and discusses the minimum/maximum autocorrelation factors (MAF), which is a common theme throughout this special issue. The next paper by Christensen and Sain considers an alternate view of multimodel ensembles for use with the North American Regional Climate Change Assessment Program. Numerical models of atmosphere–ocean circulation are widely used to understand past climate and to project future climate change. The authors propose a spatially-correlated latent variable model in order to facilitate the exploration of similarities between regional climate models and what factors best predict observed locations of model convergence. In their paper, Mueller and Ferreira contrast the MAF method with the Gauss iterations (U-WEDGE) method and present a comparison with a full cosimulation of the attributes. Du and Ma propose an efficient approach in constructing variogram matrix functions in order to measure the dependence of a vector random field with secondorder increments. This paper further demonstrates that various dependence structures

Międzynarodowa współpraca Polskiego Towarzystwa Statystycznego

The author describes forms of international cooperation in the years 1912–2011 of the Polish Statistical Association (PTS). In the first period of activity, the main form of cooperation was the participation of PTS members in the congresses of the International Statistical Institute and the Econometric Society. After 1989, the PTS organized and sponsored international conferences and scientific seminars in Poland. Particular attention is devoted to four statisticians, who played a prominent role in international activities and the development of the Society as well as Polish statistics, i.e. Professors Jerzy Neyman, Jan Piekałkiewicz and Jan Czekanowski and also to dr. Jan Wiśniewski. The study highlighted the role of a magazine published in English, ”Statistics in Transition” (later ”Statistics in Transition – new series”) in PTS international cooperation.

About Our Authors

About Our Authors

A History of Japanese Developments in Econometrics

In Japan the systematic collection of economic data, including the prices of such commodities as rice, started for policy purposes in the sixteenth century. Yet such external impacts as the Tokyo meeting of the International Statistical Institute (1930) and the visit of two founding members of the Econometric Society (established 1930) to Japan were instrumental in promoting statistical studies and inspiring Japanese students to study abroad. Eiichi Sugimoto was trained in Berlin and drew a shifting demand curve for rice over time in a three-dimensional space in 1935. After World War II, Shinichi Ichimura, Michio Hatanaka, and Takeshi Amemiya were trained in the United States and introduced to empirical studies. Ichimura became responsible for building macroeconometric models and input-output tables for the Japanese and other East Asian economies. Hatanaka and Amemiya contributed to a more advanced econometrics thanks to the abundant use of U.S. economic data.

Methodological challenges of register‐based research

Administrative data have become more and more important for both official statistics and academic research. However, the, more administrative registers are used, the more methodological issues arise (Bakker, 2009; Daas, Kuijvenhoven and Zeelenberg, 2010a). This was apparent at the 2011 congress of the International Statistical Institute, when more than ten sessions focused explicitly on the quality, linking and analyses of administrative data and many others discussed studies based on administrative data. Moreover, the increasing use of administrative data is one of the core transitions in research projects funded in Europe. In both the European Statistical System networks (ESSnets), launched by Eurostat, and the Framework Programmes, funded by the European Commission, the primary focus is no longer solely on data collected by questionnaires. Both Eurostat and the European Commission promote the development of new theory and applications in the area of the use of administrative data. Examples of recent initiatives are the ESSnet on ‘Data Integration’, the ESSnet ‘On the Use of Administrative and Accountants Data’ and the seventh framework Programme BLUE Enterprise and Trade Statistics. Each of these projects has a strong component that focuses on key methodological issues in the use of administrative data and registers for research purposes. However, the theory of register-based research is scarcely out of the egg. After the first mention of this problem in the mid-1990s (Hoffmann, 1995; Statistics Denmark, 1995), the rudiments of such a theory were merely sketched in the first decade of this century (UNECE, 2007; Wallgren and Wallgren, 2007). It will take a serious period of time to develop it further and take it to an acceptable level; just as it took time to develop and accept the use of probabilistic ideas in to the

Discussions

International Statistical Review (2011), 79, 2, 221–230 doi:10.1111/j.1751-5823.2011.00144.x Discussions Anastasios A. Tsiatis and Marie Davidian Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA E-mail: tsiatis@ncsu.edu We congratulate the authors (henceforth LSD) on a long overdue, detailed review of the connection between the AIPW estimators derived in the incomplete data context via semipara- metric theory by Robins, Rotnitzky, and colleagues and the survey calibration estimators used widely in survey sampling. Although this connection has been noted previously (e.g. Robins & Rotnitzky, 1998; Rotnitzky, 2009), the present article appears to be the first in the statistical literature to offer a more comprehensive account. We had only a passing familiarity with this connection, and we are grateful to the editors for the opportunity to offer this discussion, whose preparation required us to acquire a deeper understanding. In what follows, we hope to complement the presentation of LSD by highlighting some further relationships and differences between the two perspectives. We adopt the notation used by the authors and consider estimation of the population total r eg , which, as in Section 2.1 of LSD may be written T. We focus on the regression estimator T ), where equivalently as T N R i i (β) = T T x i β, π i i=1 a representation that may be more familiar to statisticians well-versed in the AIPW literature. We reiterate and expand upon some important differences between the missing data and survey sampling contexts noted by LSD. In survey sampling, the realizations (x 1 , y 1 ), . . . , (x N , y N ) that comprise the population are regarded as fixed, and inference on T = Ni=1 y i , or, equivalently, the population mean N −1 T, is the goal. This is based on data (x i , R i , R i y i ), i = 1, . . . , N, drawn from the population according to a fixed, known design, where n = Ni=1 R i , and Pr(R i = 1) = π i and Pr(R i = 1, R j = 1) = π ij for π i known and π ij known or unknown, i, j = 1, . . . , N. The (x i , y i ) may be viewed as realizations of random variables (X i , Y i ), i = 1, . . . , N, representing an independent and identically distributed (iid) sample from some super-population; however interest focuses on the fixed quantity T (S¨arndal et al., 2003). In contrast, in the incomplete data context, interest is in estimation of μ = E(Y ), a parameter associated with the super-population. Instead of observing a realization of i.i.d. (X i , Y i ), i = 1, . . . , N, we observe a realization of i.i.d. (X i , R i , R i Y i ), i = 1, . . . , N, where R i is an indicator of whether or not the value of Y i is observed or missing. Ordinarily, the probabilities of observing Y i for each i are not fixed by design; rather, missingness arises according to some unknown mechanism about which some assumption is made. A common assumption is that R i is conditionally independent of Y i given X i , the so-called “missing at random” (MAR) assumption, under which π(X i ) = Pr(R i = 1|X i , Y i ) = Pr(R i = 1|X i ). MAR cannot be verified from the observed data, so the C 2011 The Authors. International Statistical Review C 2011 International Statistical Institute. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

A user's guide to a short course on professional ethics and the work of a national statistical office

1. Background There is growing recognition of the important role that ethics can play in many aspects of the work of a National Statistical Office (NSO). This implies that the leadership and staff of NSOs should be aware of the common ethical concerns that arise in the course of their work and the ethical guidance provided by applicable national, regional, and international norms. (Although the term NSO is used here, in countries where the national statistical system is less centralized, the term “statistical agency” can be substituted without altering the meaning of what is being said.) In response to this interest the International Statistical Institute (ISI) organized a short course on Professional Ethics at the 57th session of the International Statistical Institute, held in Durban, South Africa, 16-22 August 2009. The course was divided into three sections: Part I of the course dealt with general issues, part II with professional ethics and the work of a national statistical office, and part III with research ethics in biomedical applications, presented by Ron Wasserstein, William Seltzer, and Norbert Victor, respectively. Subsequently, the author revised and expanded part II of the course to be a stand-alone course. The revised course, “Professional Ethics and the Work of a National Statistical Office,” is available as a 70-slide PowerPoint presentation at https://pantherfile.uwm.edu/margo/www/govstat/integrity.htm. It may be copied and used for any non-commercial purpose. The primary purpose of this user’s guide is to serve as an aid for those thinking about presenting the course to a group of participants. With this in mind, the guide describes the course and offers some suggestions on its presentation, with a view to encouraging relevant training in ethics for persons involved in the work of national statistical offices (NSOs). Alternatively, the PowerPoint presentation can be used as the basis of a self-study course and this guide will then serve as a student’s guide. However, there are distinct advantages in taking part in this course as part of a group exercise. The group discussion of the issues raised, whether on ethical priorities and trade-offs or on the sometimes

Focus on Authors

Focus on Authors

A Conversation with George G. Roussas

George G. Roussas was born in the city of Marmara in central Greece, on June 29, 1933. He received a B.A. with high honors in Mathematics from the University of Athens in 1956, and a Ph.D. in Statistics from the University of California, Berkeley, in 1964. In 1964--1966, he served as Assistant Professor of Mathematics at the California State University, San Jose, and he was a faculty member of the Department of Statistics at the University of Wisconsin, Madison, in 1966--1976, starting as an Assistant Professor in 1966, becoming a Professor in 1972. He was a Professor of Applied Mathematics and Director of the Laboratory of Applied Mathematics at the University of Patras, Greece, in 1972--1984. He was elected Dean of the School of Physical and Mathematical Sciences at the University of Patras in 1978, and Chancellor of the university in 1981. He served for about three years as Vice President-Academic Affairs of the then new University of Crete, Greece, in 1981--1985. In 1984, he was a Visiting Professor in the Intercollege Division of Statistics at the University of California, Davis, and he was appointed Professor, Associate Dean and Chair of the Graduate Group in Statistics in the same university in 1985; he served in the two administrative capacities in 1985--1999. He is an elected member of the International Statistical Institute since 1974, a Fellow of the Royal Statistical Society since 1975, a Fellow of the Institute of Mathematical Statistics since 1983, and a Fellow of the American Statistical Association since 1986. He served as a member of the Council of the Hellenic Mathematical Society, and as President of the Balkan Union of Mathematicians.

A Conversation with Martin Bradbury Wilk

Martin Bradbury Wilk was born on December 18, 1922, in Montréal, Québec, Canada. He completed a B.Eng. degree in Chemical Engineering in 1945 at McGill University and worked as a Research Engineer on the Atomic Energy Project for the National Research Council of Canada from 1945 to 1950. He then went to Iowa State College, where he completed a M.Sc. and a Ph.D. degree in Statistics in 1953 and 1955, respectively. After a one-year post-doc with John Tukey, he became Assistant Director of the Statistical Techniques Research Group at Princeton University in 1956–1957, and then served as Professor and Director of Research in Statistics at Rutgers University from 1959 to 1963. In parallel, he also had a 14-year career at Bell Laboratories, Murray Hill, New Jersey. From 1956 to 1969, he was in turn Member of Technical Staff, Head of the Statistical Models and Methods Research Department, and Statistical Director in Management Sciences Research. He wrote a number of influential papers in statistical methodology during that period, notably testing procedures for normality (the Shapiro–Wilk statistic) and probability plotting techniques for multivariate data. In 1970, Martin moved into higher management levels of the American Telephone and Telegraph (AT&T) Company. He occupied various positions culminating as Assistant Vice-President and Director of Corporate Planning. In 1980, he returned to Canada and became the first professional statistician to serve as Chief Statistician. His accomplishments at Statistics Canada were numerous and contributed to a resurgence of the institution’s international standing. He played a crucial role in the reinstatement of the Cabinet-cancelled 1986 Census. He remained active after his retirement, serving as a Senior Advisor to the Privy Council Office as well as on several national commissions. In addition, he chaired the Canadian National Task Forces on Tourism Data and on Health Information. Martin is a former President of the Statistical Society of Canada (SSC) and Vice-President of the American Statistical Association (ASA). He is an elected member of the International Statistical Institute and an honorary member of the SSC. He has received many honors, including the George Snedecor Prize, the Jack Youden Prize, the F.G. Brander Memorial Award, the SSC Gold Medal, and a Distinguished Alumni Achievement Citation from Iowa State University. He is a fellow of the Institute of Mathematical Statistics, the American Statistical Association, the Royal Statistical Society, the American Association for the Advancement of Science, and the New York Academy of Science. He was made an Officer of the Order of Canada in 1999 for his “insightful guidance on important matters related to our country’s national statistical system.” The following conversation took place at Martin Wilk’s home in Salem, Oregon, October 6–7, 2005.

Na Primeira República, Bulhões Carvalho legaliza a atividade estatística e a põe na ordem do Estado

Recriada nos primórdios da República (jan./1890), como órgão central, a Diretoria Geral de Estatística (DGE) enfrentou dificuldades para consolidar a atividade estatística brasileira. Um novo rumo só foi ensaiado, e razoavelmente alcançado, quando José Luiz Sayão de Bulhões Carvalho (1866-1940), médico e sanitarista, dedicado aos estudos e pesquisas demográficas, assumiu sua direção (em dois períodos) por quase 17 anos. Em várias frentes de atuação, aplicou-se em facilitar o convívio da DGE com seus similares na federação (extremada) então vigente. Idealizou acordos com órgãos estaduais; imaginou conselhos para tomada de decisões coletivas; imaginou uma conferência estatística, baseada na experiência de participação em dois congressos do International Statistical Institute (1S1); ajudou a formar uma comunidade estatística, pensando a formação do profissional especializado, para tanto tendo estimulado e patrocinado a tradução de livros; entre outras medidas. O Censo de 1920, que idealizou e conduziu, sendo o único grande censo na Primeira República, pautou-se em relações amistosas com os governos federal e estaduais e com a sociedade organizada (Igreja, imprensa, sindicatos, clubes etc.). Realizou, então, por assim dizer, um censo moderno, utilizando máquinas de apuração e o concluindo em tempo hábil. Na divulgação dos resultados, inovou na feitura de gráficos (pictóricos), seja para os volumes impressos, seja para o Pavilhão da Estatística, também chamado de Pavilhão da Ciência da Certeza, que marcou a presença da DGE na Exposição Internacional Comemorativa do Centenário da Independência. Com sua vivência, antecipou as bases do Instituto Brasileiro de Geografia e Estatística (1BGE), obra de um seu discípulo, e que pode ser visto como a medida do seu sucesso (a longo prazo).

Regression output from a remote analysis server

This paper is concerned with the problem of balancing the competing objectives of allowing statistical analysis of confidential data while maintaining standards of privacy and confidentiality. Remote analysis servers have been proposed as a way to address this problem by delivering results of statistical analyses without giving the analyst any direct access to data. Several national statistical agencies operate remote analysis servers [Australian Bureau of Statistics Remote Access Data Laboratory (RADL), < www.abs.gov.au>; Luxembourg Income Study, < www.lisproject.org>]. Remote analysis servers are not free from disclosure risk, and current implementations address this risk by “confidentialising” the underlying data and/or by denying some queries. In this paper we explore the alternative solution of “confidentialising” the output of a server so that no confidential information is revealed or can be inferred. In this paper we review results on remote analysis servers, and provide a list of measures for confidentialising the output from a single regression query to a remote server as developed by Sparks et al. [R. Sparks, C. Carter, J. Donnelly, J. Duncan, C.M. O’Keefe, L. Ryan, A framework for performing statistical analyses of unit record health data without violating either privacy or confidentiality of individuals, in: Proceedings of the 55th Session of the International Statistical Institute, Sydney, 2005; R. Sparks, C. Carter, J. Donnelly, C.M. O’Keefe, J. Duncan, T. Keighley, D. McAullay, Remote access methods for exploratory data analysis and statistical modelling: privacy-preserving Analytics ™, Comput. Meth. Prog. Biomed. 91 (2008) 208–222.] We provide a fully worked example, and compare the confidentialised output from the query with the output from a traditional statistical package. Finally, we provide a comparison the confidentialised regression diagnostics with the synthetic regression diagnostics generated by the alternative method of Reiter [J.P. Reiter, Model diagnostics for remote-access regression servers, Statistics and Computing 13 (2003) 371–380].

A Conversation with Shayle R. Searle

Born in New Zealand, Shayle Robert Searle earned a bachelor’s degree (1949) and a master’s degree (1950) from Victoria University, Wellington, New Zealand. After working for an actuary, Searle went to Cambridge University where he earned a Diploma in mathematical statistics in 1953. Searle won a Fulbright travel award to Cornell University, where he earned a doctorate in animal breeding, with a strong minor in statistics in 1959, studying under Professor Charles Henderson. In 1962, Cornell invited Searle to work in the university’s computing center, and he soon joined the faculty as an assistant professor of biological statistics. He was promoted to associate professor in 1965, and became a professor of biological statistics in 1970. Searle has also been a visiting professor at Texas A&M University, Florida State University, Universität Augsburg and the University of Auckland. He has published several statistics textbooks and has authored more than 165 papers. Searle is a Fellow of the American Statistical Association, the Royal Statistical Society, and he is an elected member of the International Statistical Institute. He also has received the prestigious Alexander von Humboldt U.S. Senior Scientist Award, is an Honorary Fellow of the Royal Society of New Zealand and was recently awarded the D.Sc. Honoris Causa by his alma mater, Victoria University of Wellington, New Zealand.