ISCB Course w/ Dimitris Rizopoulos
ISCB Course w/ Dimitris Rizopoulos
Czech National Group of the International Society for Clinical Biostatistics, in cooperation with the Institute of Computer Science AS CR in Prague, and, Faculty of Mathematics & Physics of the Charles University in Prague
are delighted to invite you to attend
a One-day ISCB course on
„Joint Models for Longitudinal and Survival Data“
presented by
Dimitris Rizopoulos
Erasmus University Medical Center, Rotterdam, The Netherlands
Dimitris Rizopoulos Biography
Dimitris Rizopoulos is a Professor in Biostatistics at the Erasmus University Medical Center. He received an M.Sc. in statistics (2003) from the Athens University of Economics and Business, and a Ph.D. in Biostatistics (2008) from the Katholieke Universiteit Leuven. Dr. Rizopoulos wrote his dissertation and a number of methodological and applied articles on various aspects on models for survival and longitudinal data analysis. He is the author of a book on the topic of joint models for longitudinal and time-to-event data. He has also written three freely available packages to fit such models in R under maximum likelihood (i.e., package JM) and the Bayesian approach (i.e., packages JMbayes and JMbayes2). He currently serves as co-Editor for Biostatistics.
WHERE
Due to worsening epidemic situation in Czechia, the course will proceed virtually on the ZOOM platform.
Registered course participants will obtain ZOOM login details on Monday 8 NOV 2021.
Course material (slides) will be made available to registered course participants by Wednesday 10 NOV 2021.
WHEN
12 November 2021 (Friday), 9:00 AM through 4:30 PM Central European Standard Time (CEST), i.e. a winter time. The time shift to standard (winter) time will be applied on 25 OCT 2021 at 3 am, the clock will shift back 1 hour to 2 am.
REGISTRATION DEADLINE
REGISTRATION IS NOW CLOSED
29 October 2021 (Friday)
Important!!:
Before you make a payment, please, e-mail an inquiry to the Course Secretary Ms. Lenka Semeráková for confirmation (semerakova@cs.cas.cz)
The number of course participants is limited to 30. Should the course reach its capacity, a notification will be placed here
The payment has to be received by this date
COURSE ABSTRACT
In follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., death, dropout). These outcomes are often separately analyzed, but on many occasions, it is of scientific interest to study their association. This research question has given rise to the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First when the focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second when the focus is on the longitudinal outcome, and we wish to correct for non-random dropout.
This course is aimed at applied researchers and graduate students and will provide a comprehensive introduction to this modeling framework. We will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis is given on applications, and after the end of the course, participants will be able to define appropriate joint models to answer their questions of interest.
Course background
This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood and regression models
Also, a basic knowledge of R would be beneficial but is not required
Participants are required to bring their laptop with the battery fully charged. Before the course, instructions will be sent for installing the required software
Target audience
Professional statisticians working in applied environments where hierarchical modelling and survival analysis are key issues; this would include biostatisticians working in the pharmaceutical industry, regulatory agencies, or academic centers
Learning Objectives
After the course, the participants are expected to be able to identify the settings in which joint modeling approach is required. The course will explain which joint models can be used depending on the actual research questions to be answered and which model-building strategies are currently available. Further, participants should be able to construct and fit an appropriate joint model, correctly interpret the obtained results, and extract additional useful information (e.g., plots) that can help communicate the results better.
The course will be explanatory rather than mathematically rigorous. Therefore emphasis is given in sufficient detail for participants to obtain a clear view of the different joint modeling approaches and how they should be used in practice. To this end, we first motivate joint modeling using real datasets and then illustrate in detail the virtues and drawbacks of each of the presented joint modeling approaches. For completeness and throughout the course, references are provided to material with more technical information.
COURSE SCHEDULE
REGISTRATION
8:30 - 9:00 Registration of the participants
LUNCH
12:30 - 13:00 Lunch break
COFFEE BREAKS
10 minutes between the successive sessions
SESSION 1
Introduction & Motivation: Which type of research questions requires joint modeling (Ch 1, JMwithR text)
SESSION 2
Review of Mixed Models: Definitions, linear mixed model estimation, how to fit in R (Ch 2, JMwithR text)
SESSION 3
Review of Mixed Models: Missing data in follow-up studies, missing data mechanisms (Ch 2, JMwithR text)
SESSION 4
Review of Relative Risk Models: Definitions, Cox model, estimation, time-dependent covariates, extended Cox model (Ch 3, JMwithR text)
SESSION 5
The Basic Joint Model: Definition of joint models, assumptions, estimation, comparison with time-dependent Cox model, connection with missing data (Ch 4, JMwithR text)
SESSION 6
Extensions of the Basic Joint Model: Functional form, Multiple longitudinal outcomes, multiple failure times (Ch 5, JMwithR text)
SESSION 7
Special topics: Dynamic predictions for the survival and longitudinal outcomes (Ch 7, JMwithR text)
REGISTRATION RATES
Early Bird (until 31 July 2021)
ISCB student member* 1 500 CZK (60 EUR)
ISCB regular member* 2 000 CZK (80 EUR)
Non-ISCB member – academia 4 000 CZK (160 EUR)
Non-ISCB member – business 10 000 CZK (400 EUR)
Regular (1 Aug – 29 October 2021)
ISCB student member* 2 000 CZK (80 EUR)
ISCB regular member* 2 500 CZK (100 EUR)
Non-ISCB member – academia 5 250 CZK (210 EUR)
Non-ISCB member – business 13 250 CZK (530 EUR)
(*) Note: ISCB membership is required for 2020 and 2021. For doctoral students, the ISCB membership for 2021 will be sufficient.
Payments by bank transfer ONLY
(All international payments have to be made in EUR)
ACCOUNT INFORMATION
For all domestic (CZK) and international transfers (EUR):
Bank's name and address Fio banka, a.s., V Celnici 1028/10, Praha 1, Czech Republic
Account owner Mezinárodní společnost pro klinickou biostatistiku v České republice, z.s.
Account number 2100009829 / 2010
IBAN CZ3820100000002100009829
BIC code/SWIFT FIOBCZPPXXX
For institutions making payments on behalf of their employees: The payment identification number (variabilní symbol) is 20211112
Please, remember to state the name of an employee on whose behalf the payment is made in the comment for the payment recipient (poznámka pro příjemce).
CANCELLATION INFORMATION
Cancellation fees apply as explained below:
before 1 October 2021: 10 EUR
1 through 29 October 2021: 40 EUR
after 29 October 2021: full paid amount
COURSE VENUE
Institute of Computer Science, Czech Academy of Sciences, Pod Vodarenskou vezi 2, 182 07 Prague, Czech Republic
COURSE SECRETARY
Ms. Lenka Semeráková, Institute of Computer Science, Czech Academy of Sciences, tel. +420 266 053 640, e-mail: semerakova@cs.cas.cz