Workshop on applications and developments of flexible parametric survival models

This one-day workshop will be held on Thursday November 10 in conjunction with the Nordic and Baltic Stata Users Group meeting on Friday November 11.

Organiser: Paul Lambert (paul.lambert@le.ac.uk)

Flexible parametric survival models are a useful alternative to the Cox model and more standard parametric models (e.g. Weibull, lognormal, etc). In Stata the models are implemented in the user written command stpm2 (Paul Lambert and Patrick Royston). The aim of this workshop is to describe some novel applications of the models and to discuss and compare some fu

rther methodological developments. The workshop will include presentations from invited speakers who have either developed methodology or have used the methods in applied projects. Speakers will include Paul Lambert, Patrick Royston and Paul Dickman. Some of the methodological developments that will be covered will be models for relative survival, cure models, competing risks and joint models of survival and longitudinal data. The workshop is aimed at participants who have some prior knowledge of survival analysis methods.

Timetable (Abstracts available here)

Speakers

Paul Lambert is a Reader in Medical Statistics in the Department of Health Sciences at the University of Leicester. Paul currently is seconded (30% FTE) to the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet. Paul's main research interest has been in developing methods for modelling relative survival. In particular modelling time-dependent covariate effects, incorporating period analysis in statistical models, and the estimation and modelling of 'cure' in population-based cancer studies. He is particularly keen on the use of flexible parametric survival models for both standard and relative survival. These offer a number of advantages in terms of communication of results, for example quantifying absolute levels of risk as well as relative risk. He has developed software in Stata to fit cure models for relative survival (strsmix and strsnmix) and also flexible parametric models (stpm2). Paul is coauthor of the book Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model.

Paul Dickman is Associate Professor of Biostatistics and deputy head of department at the the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet. He conducts research in epidemiology and biostatistics with particular focus on cancer epidemiology and register-based epidemiology. Dr Dickman has long been interested in the analysis of cancer patient survival, the topic of his 1997 doctoral thesis where he studied with Professor Timo Hakulinen. His primary interests lie in statistical methods for estimating and modelling relative survival. He has published widely in the field of cancer patient survival, is a coauthor of the Stata strs command for estimating and modelling relative survival, and taught courses in cancer survival analysis in eight different countries.

Patrick Royston is a senior statistician in the MRC Clinical Trials Unit, London, and an honorary professor in statistics at University College London. His main interests include multivariable model-building (with a particular focus on modelling continuous covariates using fractional polynomials and splines), applications of novel methods of survival analysis in clinical trials, novel trial designs, statistical computing and algorithms, explained variation in survival analysis, and flexible parametric survival modelling and its many applications. Patrick originally published the program stpm in the Stata Journal in 2001, and the theory of flexible parametric models with Mahesh Parmar in Statistics in Medicine (2002). Patrick is coauthor of the book Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model.

Edoardo Colzani, M.D., M.P.H.  is currently Scientific Officer for Burden of Communicable Diseases in Europe at the European Centre for Disease Control and prevention (ECDC) and Assistant Professor of Hygiene and Preventive Medicine at the University of Milano-Bicocca. Since 2009 he is also doing a Ph.D. on breast cancer epidemiology at the Department of Medical Epidemiology and Biostatistics (MEB) of the Karolinska Institutet in Stockholm. Edoardo Colzani received in 2002 his professional license as M.D. from the University of Parma where he also specialized in Hygiene and Preventive Medicine in 2006. He received his M.P.H. in quantitative methods from the Harvard School of Public Health in 2006 after being awarded a Fulbright Grant. Edoardo Colzani has published papers on national and international journals on risk behaviours for chronic and infectious diseases and on breast cancer epidemiology.

Camille Maringe is a research fellow and PhD student in the Cancer Research UK Cancer Survival Group at the London School of Hygiene and Tropical Medicine. Her research interests include cancer incidence, mortality and survival of the South Asian population of England and Wales. She is also working on the International Cancer Benchmarking Partnership study looking at the impact of stage and treatment on international differences in cancer survival.

Anna Johansson is a biostatistician at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet since 1999. Her research interests lie in reproductive epidemiology and cancer epidemiology, with a special focus on register-based research, survival analysis and epidemiological methods. Since 2011 she is a registered PhD student studying reproductive factors and breast cancer, and in particular pregnancy associated breast cancer.

Therese Andersson is a biostatistician and PhD student in the Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet. The title of Therese's PhD thesis is "Extensions of flexible parametric models for population-based cancer studies". Therese has worked as an applied biostatistician at MEB for 4.5 years, and has been involved in a variety of epidemiological studies. Her primary interests lie in cancer epidemiology and survival analysis methods used within population-based cancer studies.

Sally Hinchliffe is a PhD student in the Department of Health Sciences at the University of Leicester. Her PhD focuses on adapting and appraising competing risks methodology for better communication of survival statistics. This has so far involved modelling competing risks using flexible parametric models and applying both current and future methodology to population based cancer data.

Sandra Eloranta is a biostatistician and PhD student at the Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet under the supervision of Paul Dickman and Paul Lambert. Her research involves the development and application of statistical methods for estimating and modelling cancer patient survival with particular emphasis on methods for presenting cancer survival statistics in a manner relevant for clinicians and patients. She is also developing methods for studying excess mortality among cancer patients from causes other than cancer. These methods are being applied to study trends in mortality from diseases in the circulatory system among patients with Hodgkin's disease and breast cancer. Sandra has coauthored several scientific papers on cancer survival and is first author of a paper studying the association between socioeconomic status and the prospect of cure from colon cancer.

Mark Clements is a biostatistician at the Department of Medical Epidemiology and Biostatistics at the Karolinska Institutet. He has worked at universities in New Zealand, Australia and the United Kingdom. Mark's research interests include the development of methods for estimating survival for multi-state models and for cancer screening. He has recently implemented flexible parametric survival models in R.

Michael Crowther is a Research Assistant in Medical Statistics in the Department of Health Sciences at the University of Leicester. His main research area is the joint modelling of longitudinal and survival data, involving both methodological and software development.  He is the author of the Stata survsim command for simulating complex survival data, and a co-author of the stmix command which fits 2-component mixture survival models. He is also studying for a PhD part-time.