Homepage of Stijn Vansteelandt

  • Research interests

 

        My main interest is to develop statistical methods for the analysis of studies subject to information loss. In this context, my research focuses on

    • missing and censored data problems
    • non-compliance in clinical trials and causal inference in observational studies
    • sensitivity analysis to incomplete data assumptions and unmeasured confounders
    • testing for gene-environment interaction in family-based association studies
    • cost-efficient design and analysis associated with prevalence estimation and diagnosis from diagnostic tests on pools of serum

 

        Further, I conduct research on

    • inference for semi-parametric models and the theory of semi-parametric efficiency
    • analysis of longitudinal and clustered data
    •  phylogenetic analysis 
  • Publications

 

            http://www.lib.ugent.be/bibliografie/801001240647

 

 

Incomplete data and sensitivity analysis

 

1.                  Vansteelandt, S. and Goetghebeur, E. (2001). Generalized Linear Models with Incomplete Outcomes: the IDE Algorithm for Estimating Ignorance and Uncertainty. Journal of Computational and Graphical Statistics, 10, 656-676.

2.                  Vansteelandt, S., Goetghebeur, E., Kenward, M. G., and Molenberghs, G. (2006). Ignorance and Uncertainty Regions as Inferential Tools in a Sensitivity Analysis. Statistica Sinica, 16, 953-979.

3.                  Carpenter, J., Kenward, M. and Vansteelandt, S. (2006). A comparison of multiple imputation and doubly robust estimation. Statistics in Society, 169, 571-584.

4.                  Vansteelandt, S., Rotnitzky, A. and Robins, J. M. (2007). Estimation of regression models for the mean of repeated outcomes under non-ignorable non-monotone non-response. Biometrika, 94, 841-860.

5.                  Vansteelandt, S., Carpenter, J. and Kenward, M.G. (2008). Analysis of incomplete data using inverse probability weighting and doubly robust estimators. Accepted for publication in Methodology.

 

Instrumental variables and noncompliance

 

1.                  Loeys, T., Vansteelandt, S. and Goetghebeur, E. (2001). Accounting for Correlation and Compliance in Cluster Randomised Trials. Statistics in Medicine, 20, 3753-3767.

2.                  Goetghebeur, E. and Vansteelandt, S. (2002). Discussion of `Clustered encouragement designs with individual noncompliance: Bayesian inference with randomization, and application to advance directive forms' by Frangakis, Rubin and Zhou. Biostatistics, 3, 169-171.

3.                  Vansteelandt, S. and Goetghebeur, E. (2003) Causal inference with generalized structural mean models. Journal of the Royal Statistical Society – Series B, 65, 817-835.

4.                  Vansteelandt, S. and Goetghebeur, E. (2004). Using potential outcomes as predictors of treatment activity via strong structural mean models. Statistica Sinica, 9, 891-909.

5.                  Vansteelandt, S. and Goetghebeur, E. (2005) Sense and sensitivity when correcting for observed exposures in randomized clinical trials. Statistics in Medicine, 24, 191-210.

6.                  Goetghebeur, E. and Vansteelandt, S. (2005) Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure. Statistical Methods in Medical Research, 14, 397-416.

7.                  Vansteelandt, S., Babanezhad, M. and Goetghebeur, E. (2008). Correcting Instrumental Variables Estimators for Systematic Measurement Error. Statistica Sinica, 19, 1223-1246.

8.                  Vansteelandt, S. (2009). Discussion on `Identifiability and Estimation of Causal Effects in Randomized Trials with Noncompliance and Completely Non-ignorable Missing-Data'. Biometrics, 65, 686-689.

 

Causal inference

 

1.                  Vansteelandt, S. (2007). On confounding, prediction and efficiency in the analysis of clustered and longitudinal data. The Scandinavian Journal of Statistics, 34, 478-498.

2.                  Robins, J.M., Rotnitzky, A. and Vansteelandt, S. (2007). Discussion on `Principal Stratification Designs to Estimate Input Data Missing Due to Death’. Biometrics, 63, 650-653.

3.                  Delbaere, I., Vansteelandt, S., De Bacquer, D., Verstraelen, H., Gerris, J., De Sutter, P., Temmerman, M. (2007). Should we adjust for gestational age when analysing birth weights? The use of z-scores revisited. Human Reproduction, 22, 2080-2083.

4.                  Goetgeluk, S. and Vansteelandt, S. (2008). Conditional generalized estimating equations for the analysis of clustered and longitudinal data. Biometrics, 64, 772-780.

5.                  Goetgeluk, S., Vansteelandt, S. and Goetghebeur, E. (2008). Estimation of controlled direct effects. Journal of the Royal Statistical Society – Series B, 70, 1049-1066.

6.                  Vansteelandt, S., VanderWeele, T., Tchetgen, E.J. and Robins, J.M. (2008). Semiparametric inference for statistical interactions. Journal of the American Statistical Association, 103, 1693–1704.

7.                  Vansteelandt, S., Mertens, K., Suetens, C. and Goetghebeur, E. (2008). Marginal Structural Models for Partial Exposure Regimes.  Biostatistics, 10, 46-59.

8.                  Sjolander, A., Humphreys, K., Vansteelandt, S., Belococco, R. and Palmgren, J. (2009). Sensitivity analysis for principal stratum direct effects with an application to a study of physical activity and coronary heart disease. Biometrics, 65, 514-520.

9.                  Vansteelandt, S. (2009). Estimating direct effects in cohort and case-control studies. Epidemiology, 20, 851-860.

10.              Laetitia Comté, Stijn Vansteelandt, Eric Tousset, Garth Baxter, Bernard Vrijens (2008). Linear and loglinear Structural Mean Models to evaluate the benefits of an on-demand dosing regimen. Clinical Trials, 6, 403-415.

11.              VanderWeele, T.J. and Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and its Interface, 2, 457-468.

12.              Bekaert, M., Vansteelandt, S. and Mertens, K. (2009). Estimation of marginal structural survival models in the presence of competing risks. Lifetime Data Analysis, 16, 45-70.

13.              VanderWeele, T.J., Vansteelandt, S. and Robins, J.M. (2009). Marginal structural models for sufficient cause interactions. Accepted for publication in the American Journal of Epidemiology.

14.              Bekaert, M., Vansteelandt, S., Decruyenaere, J. and Benoit, D. (2009). Commentary:  “Modelling the effect of time-dependent exposure on intensive care unit mortality”, by Wolkewitz and colleagues. Accepted for publication in Intensive Care Medicine.

15.              Babanezhad, M., Vansteelandt, S. and Goetghebeur, E. (2009). Comparison of Causal Effect Estimators Under Exposure Misclassification. Accepted for publication in Journal of Statistical Planning and Inference.

 

Genetic epidemiology and phylogenetics

 

1.                  Baele, G., Raes, J., van de Peer, Y. and Vansteelandt, S. (2006). A powerful multiple testing method to detect heterotachy in nucleotide sequences. Molecular Biology and Evolution, 23, 1397-1405.

2.                  Vansteelandt, S., DeMeo, D., Su, J., Smoller, J., Murphy, A.J., McQueen, M., Celedon, J., Weiss, S.T., Silverman, E.K. and Lange, C. (2008). Testing and estimating gene-environment interactions in family-based association studies. Biometrics, 64, 458-467.

3.                  Baele, G., van de Peer, Y. and Vansteelandt, S. (2008). A model-based approach to study nearest neighbour influences reveals complex substitution patterns in non-coding sequences. Systematic Biology, 57, 675-692.

4.                  Vansteelandt, S., Goetgeluk, S., Lutz, S., Waldman, I., Lyon, H., Schadt, E.E., Weiss, S.T. and Lange, C. (2009). On the adjustment for covariates in genetic association analysis: A novel, simple principle to infer causality. Genetic Epidemiology, 33, 394-405.

5.                  Baele, G., van de Peer, Y. and Vansteelandt, S. (2009). Efficient Context-dependent Model Building Based on Clustering Posterior Distributions for Non-coding Sequences. BMC Evolutionary Biology, 9, nr. 87.

6.                  Hoffmann, T.J., Lange, C., Vansteelandt, S. and Laird, N.M. (2009). Gene-Environment Interaction Test for Dichotomous Traits in Nuclear Families. Genetic Epidemiology, 33, 691-699.

7.                  Hoffmann, T.J., Lange, C., Vansteelandt, S., Raby, B.A., Demeo, D.L., Silverman, E.K. and Laird, N.M. (2010). Parsing the Effects of Individual SNPs in Candidate Genes with Family Data. Human Heredity, 69, 91-103.

8.                  Moerkerke, B., Vansteelandt, S. and Lange, C. (2009). A doubly-robust test for gene-environment interaction in family-based studies. Accepted for publication in Biostatistics.

 

Group testing

 

1.                  Vansteelandt, S., Goetghebeur, E. and Verstraeten, T. (1999). Adjusting for confounding when estimating a time trend in HIV prevalence based on pooled serum samples. Archives of Public Health, 57, 89-105.

2.                  Vansteelandt, S., Goetghebeur, E. and Verstraeten, T. (2000). Regression Models for Disease Prevalence with Diagnostic Tests on Pooled Serum Samples. Biometrics, 56, 1126-1133.

3.                  Vansteelandt, S., Goetghebeur, E., Thomas, I., Mathys, E. and Van Loock, F. (2005). On the Safety of Plasma Pools and Derivatives. Statistics in Society, 168, 345-363.

 

Technical Reports (under review)

 

1.        Sjolander, A., Humphreys, K., Vansteelandt, S. and Palmgren, J. (2008). A principal stratification approach to assess the differences in prognosis between cancers caused by hormone replacement therapy and by other factors.

4.        Babanezhad, M., Vansteelandt, S. and Goetghebeur, E. (2008). On the performance of IV-estimators for the causal odds ratio.

5.        Bowden, J., and Vansteelandt, S. (2009). IV-estimators of the causal odds ratio for a continuous exposure in prospective and retrospective designs.

6.        Thomas J. Hoffmann, Stijn Vansteelandt, Christoph Lange, Edwin K. Silverman, Dawn L. Demeo and Nan M. Laird (2009). Combining Disease Models to test for Gene-Environment Interaction in Nuclear Families.

7.        Vansteelandt, S. (2009). Estimation of controlled direct effects on a dichotomous outcome using logistic structural direct effect models.

8.        Baele, G., van de Peer, Y. and Vansteelandt, S. (2009). Modelling the ancestral sequence distribution and equilibrium frequencies in context-dependent models for non-coding sequences.

9.        Vansteelandt, S., Bekaert, M. and Claeskens, G. (2010). On model selection and model misspecification in causal inference.

 

  • Ongoing collaborations
    • Collaboration with Tyler VanderWeele and James Robins, department of Epidemiology and Biostatistics of the Harvard School of Public Health, Boston                    

Themes: causal inference

    • Collaboration with Christoph Lange, department of Biostatistics of the Harvard School of Public Health, Boston

Themes: assessment of gene-environment interactions using family-based association tests

 

 

Stijn Vansteelandt                                                                                          Tel. (00)32-(0)9-2644776
Assistant Professor                                                                                         Fax. (00)32-(0)9-2644995
Department of Applied Mathematics and Computer Science                        E-mail: Stijn.Vansteelandt@ugent.be
Ghent University
Room 27 (second floor)
Krijgslaan 281, S9
B-9000 Gent, BELGIUM

Last updated: February 1, 2010.