A 2 Day course on Longitudinal and Missing Data

presented by

Geert Verbeke and Geert Molenberghs (Interuniversity Institute for Biostatistics and Statistics Bioinformatics, Katholieke Universiteit Leuven and Universiteit Hasselt) on 26 & 27 September 2016

Venue: The Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX Nearest Tube stations: Barbican, Liverpool Street, Moorgate, Old Street The course begins with a brief presentation of linear mixed models for continuous hierarchical data. Course The course focus is from the modeller’s perspective and on applications. Emphasis will be on model Summary: formulation, parameter estimation, and hypothesis testing, as well as on the distinction between the random-effects (hierarchical) model and the implied marginal model. Models for non-Gaussian data will be discussed, with a strong emphasis on generalized estimating equations (GEE) and the generalized linear mixed model (GLMM). A brief review of the classical generalized linear modelling 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. Focus will be primarily on binary outcomes, however, GEE and GLMM model formulations will also be covered. When analysing hierarchical and longitudinal data, one is often confronted with missing observations, 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 seriously jeopardize results, and interpretation difficulties are bound to occur. Methods to properly analyse incomplete data, under flexible assumptions, will be presented.

All topics will be illustrated with worked examples using SAS. While there are no hands-on practical sessions, the course notes include worked examples with annotated programs and output from SAS, discussed in such a way that they are also of use to non-SAS-users. Participants should be able to perform a basic analysis for a particular longitudinal data set at hand, Learning using linear, generalized linear and non-linear tools for longitudinal data. Based on a selection of Outcomes: exploratory tools, the nature of the data, and the research questions to be answered in the analyses, they should be able to construct an appropriate statistical model, to fit the model within the SAS framework, and to interpret the obtained results. Further, participants should be aware not only of the possibilities and strengths of a particular selected approach, but also of its drawbacks in comparison to other methods. Day 1: Linear mixed models, model formulation, parameter interpretation, hierarchical versus marginal Topics model interpretation, estimation and inference, empirical Bayes, model families for discrete outcomes, Covered: marginal models and generalized estimating equations (GEE). Day 2: Generalized mixed models, estimation methods (Laplace, MQL, PQL, Quadrature), comparison with GEE, missing data mechanisms, problems with non-random dropout (i.e., bias, loss of efficiency, etc.), modelling frameworks to handle dropout (selection, pattern mixture and shared parameter models), sensitivity analyses. Throughout the course, it will be assumed that the participants are familiar with basic statistical Knowledge modelling, including linear models (regression and analysis of variance), as well as generalized linear Assumed: models (logistic and Poisson regression). Pre-requisite knowledge should also include general estimation and testing theory (maximum likelihood, likelihood ratio). Course Registration begins at 09.30 and the course runs from 10.00 until 17.00 on 28 September and from Format: 09.30 to 17.00 on 29 September. Fees Registration before Registration on / after (incl VAT) 26 August 2016 26 August 2016

Non Member £562.50+vat £625+vat

RSS Fellow £478.12+vat £531.25+vat

RSS CStat: also MIS, FIS & GradStat £450+vat £500+vat

Contact: Tessa Pearson, Royal Statistical Society, 12 Errol Street, London EC1Y 8LX. Tel: +44 (0)20 7614 3947 Email: [email protected] Fax: +44 (0)20 7614 3905 Non members are welcome to join the Society at the same time as registering for the course and the discount received will cover the cost of their first subscription payment. More information about membership can be found at www.rss.org.uk/join REGISTRATION FORM LONGITUDINAL AND MISSING DATA – 26 & 27 SEPTEMBER 2016

PLEASE USE ONE FORM PER PERSON. Completed forms may be faxed to + 44 (0)20 7614 3905

Preferred first name I am/am not an RSS fellow Membership number ………………………. Surname Telephone Number Fax number E-mail address (our normal method of contact will be e-mail, unless you advise us otherwise)

Organisation Name Address (including postcode)

Invoice Address (including postcode) if different from above COURSE FEES (including VAT @ 20%): Includes full course notes, tea/coffee and lunch Please tick the appropriate box below:

Non member (£562.50+vat) £675.00 □ Non member (£625+vat) £750.00 □

RSS Fellow (£478.12+vat) £573.75 □ RSS Fellow (£531.25+vat) £637.50 □

CStat, MIS, FIS and GradStat (£450+vat) £540.00 □ CStat, MIS, FIS and GradStat (£500+vat) £600.00 □

PAYMENT  I enclose a cheque for £______payable to “RSS Services Limited”  Please invoice me at the above address Purchase order number (required):  Please debit my credit/debit card for the amount of £_____ Type of card: Visa / MasterCard / Maestro / AMEX Registered address, house number Card number: Postcode: Expiry date: Name on card: 3 digit security number (on back of card): Signature: (NB this information will be destroyed once payment has been processed) Terms and conditions:

 Please tick to confirm you have read, understood and agreed to our Terms and Conditions for public and on- line courses

These can be read and downloaded from the RSS training courses page on our website.

Please give details of any special dietary requirements:

How did you hear about this  AllStat  RSS Newsletter  RSS Website  Other (Please State) course?

The information given above will be used by the Royal Statistical Society to process your registration. It will be retained and used by staff, officers and members in furtherance of the aims of the Royal Statistical Society, and in accordance with the 1998 Data Protection Act. The information will not be passed on to any third party. We may contact you with details of other courses and events which might be of interest. If you would prefer not to be contacted in this way, please tick the box Please return Tessa Pearson, Royal Statistical Society, 12 Errol Street, London EC1Y 8LX, UK completed form to: Tel: +44 (0)20 7614 3947 Email: [email protected] Fax: +44 (0)20 7614 3905