2018 年度 医学統計学集中講義シリーズ
"Longitudinal and Incomplete Data in Clinical Studies"

【 主催】
国立循環器病研究センター・データサイエンス部
京都大学医学研究科・医療統計学
京都大学医学研究科・臨床統計学

日本医療研究開発機構(AMED)・生物統計家育成支援事業(京都大学×京都大学病院×国立循環器病研究センター)の一環として，下記の要領で医学統計学集中講義を開講します．講師としてGeert Molenbgerhs先生を招へいし，”Longitudinal and Incomplete Data in Clinical Studies"の演題で講義いただきます．皆様の奮ってのご参加をお待ちしています．

【参考図書】
1. Verbeke G, Molenberghs G. (1997). Linear Mixed Models for Longitudinal Data. Springer.
2. Molenberghs G Verbeke G (2005). Models for Discrete Longitudinal Data. Springer.
3. Molenberghs G, Kenward M (2007). Missing Data in Clinical Studies. Wiley.

【 申し込み方法】
申し込みは下記のURLから，受講申し込みをお願いします．定員は50名で，定員に達したところで申し込み受け付けを終了します(好評につき，定員を50名から60名に変更しました)．参加費は無料です．通訳の提供はありません．

https://www.kokuchpro.com/event/ncvcds1812gm/

【 講義スケジュール】
2018年12月13日(木) 午前9時30分～午後5時 (開場 午前9時～)
1限 午前9時30分～午前11時・2限 午前11時15分～午後12時45分
3限 午後1時45分～午後3時15分・4限 午後3時30分～午後5時

【 場　所 】
キャンパスプラザ京都 4階第2講義室
 〒600-8216 京都府京都市下京区 西洞院通塩小路下る東塩小路町939

【 参加費 】
無料

【  講　師 】
Professor Geert Molenbgerhs (Universiteit Hasselt and Katholieke Universiteit Leuven)

【 講義要旨】
We begin by presenting linear mixed models for continuous hierarchical data. The focus lies on the modeler’s perspective and on applications. Emphasis will be on model formulation, parameter estimation, and hypothesis testing, as well as on the distinction between the random-effects (hierarchical) model and the implied marginal model.

Then, models for non-Gaussian data will be discussed, with a strong emphasis on generalized estimating equations (GEE) and the generalized linear mixed model (GLMM).To usefully introduce this theme, a brief review of the classical generalized linear modeling framework will be presented. Similarities and differences with the continuous case will be discussed. The differences between marginal models, such as GEE, and random-effects models, such as the GLMM, will be explained in detail.

When analyzing hierarchical and longitudinal data, one is often confronted with missing data, i.e., scheduled measurements have not been made, due to a variety of (known or unknown) reasons. It will be shown that, if no appropriate measures are taken, missing data can cause seriously jeopardize results, and interpretation difficulties are bound to occur. Precisely, a framework will be sketched to handle incomplete data. Simple and simplistic methods will be commented on. Methods to properly analyze incomplete data,under flexible assumptions, are presented. These include ignorable likelihood analysis, ignorable Bayesian analysis, weighted estimating equations, and multiple imputation. To conclude, the issue of sensitivity to non-verifiable assumption is discussed, and addressed through sensitivity analyses.

All developments will be illustrated with worked examples using the SAS System.

【 講師紹介】
Dr. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt and KU Leuven in Belgium. He received the B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from the Universiteit Antwerpen. Dr Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004), Co-editor of Biometrics (2007–2009) and as President of the International Biometric Society (2004-2005). He was Co-editor of Biostatistics (2010–2012) and is currently Executive Editor of Biometrics (2018–). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. He has held visiting positions at the Harvard School of Public Health (Boston, MA). He is founding director of the Center for Statistics at Hasselt University and currently the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics, I-BioStat, a joint initiative of the Hasselt and Leuven universities.

Geert Molenberghs is editor and author of several books on the use of linear mixed models for the analysis of longitudinal data (Springer Lecture Notes 1997, Springer Series in Statistics 2000, Springer Series in Statistics 2005, Chapman Hall/CRC 2007), and he has taught well over a hundred short and longer courses on the topic in universities as well as industry, in Europe, North America, Latin America, and Australia. He received several Excellence in Continuing Education awards for courses offered at the Joint Statistical Meetings.