Methodological Research

Developing new statistical methods is a core activity of our group. Our goal is to provide theory and practical methods to analyze time-to-event data, including developing software such as the R packages mstate and dynpred.

Research themes

  • Competing risks and multi-state models (link)
  • Dynamic prediction models.
  • Frailty models, which incorporate unobserved heterogeneity associated with the outcomes in the observed data; they can be univariate, when subjects are followed for one (terminal) event and multivariate, which explain correlated data, such as clustered failures or recurrent events.
  • Time-dependent covariates and time-varying effects of covariates.
  • Performance indicators: the field of evaluating, comparing and reporting the performance of health care providers, such as hospitals. A related topic is volume outcome studies, which aim to disentangle the association between the number of interventions and their outcomes in hospitals of different sizes.
  • Meta-analysis for survival outcomes.
  • Causal inference.

Books

In November 2011, the book Dynamic Prediction in Clinical Survival Analysis, by Hans van Houwelingen and Hein Putter, appeared at Chapman & Hall. Click here for the book website.

The book website of the Handbook of Survival Analysis, edited by John P. Klein, Hans C. van Houwelingen, Joseph G. Ibrahim, Thomas H. Scheike, appeared in 2012, also at Chapman & Hall can be found here.

PhD theses   

Projects & collaboration

Our group is or has been involved in the following projects

  • 2016: KWF: Personalised Sarcoma Care; predicting outcome and improving the balance between prognosis and quality of life for sarcoma patients (2 PhDs)
  • 2015: KIKA: Meta-analysis of individual patient data to investigate dose-intensity relation with survival outcome for osteosarcoma patients. (1 ft for 2 years)
  • ZonMW TOP grant ZONMW-912-07-018 (2007-2012), Prognostic modeling and dynamic prediction for competing risks and multi-state models
  • Pfizer educational grant for the international TEAM study (2003-2010)
  • Mobidyq (Modèles Biostatistiques Dynamiques pour l'Epidémiologie, Dynamical Biostatistical Models for Epidemiology; French Scientific Organization project number 2010-PRSP-006; primary applicant Daniel Commenges)
  • Marie Curie Initial Training Network (ITN) MEDIASRES “Novel Statistical Methodology for Diagnostic and Therapeutic Studies and Systematic Reviews”

We have informal collaborations with biostatistics departments at the universities of Bordeaux, Copenhagen, Rome and Freiburg, and with the Netherlands Interdisciplinary Demographic Institute (NIDI) and the Max Planck Institute for Demographic Research (MPIDR) Rostock, among others.

Awards

The Hans van Houwelingen Award 2016 for the best Dutch paper in a refereed journal in the biometrical field in 2014 and 2015 was awarded to:

Putter H, van Houwelingen HC (2015). Dynamic frailty models based on compound birth-death processes. Biostatistics 16: 550 – 564.

Activities

In March 2014 we co-organized a workshop “Multistate Models: Bridging Biostatistics, Demography and Econometrics” at the Lorentz Center @ Snellius in Leiden.

Selected publications

  1. Putter H, Spitoni C (2016). Non-parametric estimation of transition probabilities in non-Markov multi-state models: The landmark Aalen–Johansen estimator. Stat Methods Med Res. doi:10.1177/0962280216674497
  2. Putter H, van Houwelingen HC (2016). Understanding landmarking and its relation with time-dependent Cox regression. Stat Biosci. doi:10.1007/s12561-016-9157-9
  3. Lancia C, Spitoni C, Anninga J, Sydes M, Jovic G, Fiocco M (2016) Marginal Structural Models with Dose-Delay-Exposure for Assessing Variations to Chemotherapy Intensity. Stat Methods Med Res., to appear
  4. Willems SJ, Schat A, van Noorden MS, Fiocco M (2016) Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator.Stat Methods Med Res. doi: 10.1177/0962280216628900
  5. Balan TA, Jonker MA, Johannesma PC, Putter H (2016). Ascertainment correction in frailty models for recurrent events data. Stat Med 35: 4183 – 4201
  6. Balan TA, Boonk SE, Vermeer MH, Putter H (2016). Score test for association between recurrent events and a terminal event.Stat Med 35: 3037 – 3048
  7. Klinten Grand M, Putter H (2016). Regression models for expected length of stay.Stat Med 35: 1178 – 1192
  8. Putter H, van Houwelingen HC (2015). Dynamic frailty models based on compound birth-death processes.Biostatistics 16: 550 – 564
  9. van Houwelingen HC, Putter H (2015). Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression.Lifetime Data Anal. 21: 180 – 196
  10. Beyersmann, J, Putter H (2014). A note on computing average state occupation times. Demographic Res http://search.proquest.com/assets/r20161.9.0.325.841/core/spacer.gif30: 1681 – 1695
  11. Nicolaie MA, van Houwelingen JC, de Witte TM, Putter H (2013). Dynamic Pseudo-Observations: A Robust Approach to Dynamic Prediction in Competing Risks.  Biometrics 69: 1043–1052.
  12. Nicolaie MA, van Houwelingen JC, de Witte TM, Putter H (2013). Dynamic prediction by landmarking in competing risks. Stat Med 32: 2031–2047.
  13. van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.
  14. Andersen PK, Geskus RB, de Witte T, Putter H (2012). Competing risks in epidemiology: possibilities and pitfalls.  Int J Epidemiol 41: 861-70.
  15. Fiocco M, Stijnen T, Putter H (2012). Meta-analysis of time-to-event outcomes using a hazard-based approach: Comparison with other models, robustness and meta-regression.  Comp Statist Data Anal 56: 1028–1037.
  16. Putter H, van Houwelingen HC (2011). Frailties in multi-state models: Are they identifiable? Do we need them?  Stat Methods Med Res.
  17. de Wreede LC, Fiocco M, Putter H (2011). mstate: An R package for the analysis of competing risks and multi-state models. J Statist Softw 38: 7.
  18. de Wreede LC, Fiocco M, Putter H (2010). The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models.  Comput Methods Programs Biomed 99: 261-274.
  19. Nicolaie MA, van Houwelingen HC, Putter H (2010). Vertical modeling: a pattern mixture approach for competing risks modeling. Stat Med 29: 1190-1205.
  20. Fiocco M, Putter H, van Houwelingen JC (2009). Meta-analysis of pairs of survival curves under heterogeneity: a Poisson correlated gamma-frailty approach. Stat Med 28: 3782-3797.
  21. Fiocco M, Putter H, Van Houwelingen JC (2009). A new serially correlated gamma-frailty process for longitudinal count data. Biostatistics 10: 245-257.
  22. van Houwelingen HC, Putter H (2008). Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal 14: 447-463.
  23. Fiocco M, Putter H, van Houwelingen HC (2008). Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Stat Med 27: 4340-4358.
  24. Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: competing risks and multi-state models.  Stat Med 26: 2389-2430.
  25. Putter H, Sasako M, Hartgrink HH, van de Velde CJ, van Houwelingen JC (2005). Long-term survival with non-proportional hazards: results from the Dutch Gastric Cancer Trial. Stat Med 24: 2807-2821.
  26. Fiocco M, Putter H, Van Houwelingen JC (2005). Reduced rank proportional hazards model for competing risks. Biostatistics 6 : 465-478.