Willi Sauerbrei - Selected Publications#
Rahnenführer J., De Bin R., Benner A., Ambrogi F., Lusa L., Boulesteix A. L., Migliavacca E., Binder H., Michiels S., Sauerbrei W., McShane L. & topic group “High-dimensional data” (TG9) of the STRATOS initiative. (2023): Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges. BMC Medicine, 21(1), 182.
Liu L., Choi J., Musoro J. Z., Sauerbrei W., Amdal C. D., Alanya A., ... & le Cessie, S. (2023): Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice. The Lancet Oncology, 24(5), e197-e206.
Wallisch C., Bach P., Hafermann L., Klein N., Sauerbrei W., Steyerberg EW., Heinze G., Rauch G., topic group 2 of the STRATOS initiative. (2022): Review of guidance papers on regression modeling in statistical series of medical journals. PLoS One. Jan 24;17(1):e0262918.
Sauerbrei W., Haeussler T., Balmford J., Huebner, M. (2022): Structured reporting to improve transparency of analyses in prognostic marker studies. BMC Medicine, Dec;20(1):1-9.
Sauerbrei W., and Royston P. (2022): Investigating treatment-effect modification by a continuous covariate in IPD meta-analysis: an approach using fractional polynomials. BMC Medical Research Methodology, 22(1), pp.1-13.
Sauerbrei W., Kipruto E., Balmford J. (2022): Effects of Influential Points and Sample Size on the Selection and Replicability of Multivariable Fractional Polynomial Models. Diagnostic and Prognostic Research, 7(1):7.
Kipruto E., Sauerbrei W. (2022): Comparison of variable selection procedures and investigation of the role of shrinkage in linear regression-protocol of a simulation study in low-dimensional data. PLoS One 17(10): e0271240.
Schmidt CO., Struckmann S., Enzenbach C., Reineke A., Stausberg J., Damerow S., Huebner M., Schmidt B., Sauerbrei W., Richter A. (2021): Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R. BMC Med Res Meth, 21(1): 1-15
Sauerbrei W., Boulesteix AL. (2020): Use of Resampling Procedures to Investigate Issues of Model Building and Its Stability. In: Piantadosi S., Meinert C. (eds) Principles and Practice of Clinical Trials. Springer, Cham. DOI: 10.1007/978-3-319-52677-5_130-1
Boulesteix A. L., Groenwold R., Abrahamowicz M., Binder H., Briel M., Hornung R., Morris T., Rahnenführer J., Sauerbrei W. on behalf of the Simulation Panel of the STRATOS initiative (2020): An introduction to statistical simulations in health research. BMJ Open, Vol. 10, Nr. 12, e039921. DOI: 10.1136/bmjopen-2020-039921
Sauerbrei W., Bland M., Evans S.J., Riley R.D., Royston P., Schumacher M., Collins G.S. (2020): Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research. Biometrical Journal.
Schandelmaier S., Briel M., Varadhan R., Schmid C.H., Devasenapathy N., Hayward R.A., Gagnier J., Borenstein M., Van der Heijden G., Dahabreh I.J., Sun X., Sauerbrei W., Walsh M., Ioannidis J.P.A, Thabane L., Guyatt G.H. (2020): A new instrument to assess the credibility of effect modification analyses (ICEMAN) in randomized controlled trials and meta-analyses. CMAJ, 192(32), E901-E906.
Sauerbrei W., Perperoglou A., Schmid M., Abrahamowicz M., Becher H., Binder H., Dunkler D., Harrell Jr. F.E., Royston P., Heinze G. for TG2 of the STRATOS initiative (2020): State of the art in selection of variables and functional forms in multivariable analysis - outstanding issues. Diagnostic and Prognostic Research, 4:3, 1-18.
De Bin R., Boulesteix A.L., Benner A., Becker N., Sauerbrei W. (2019): Combining clinical and molecular data in regression prediction models: insights from a simulation study. Briefings in bioinformatics.
Volkmann A., De Bin R., Sauerbrei W., Boulesteix A.L. (2019): A plea for taking all available clinical information into account when assessing the predictive value of omics data, BMC Medical Research Methodology, 19:162
Perperoglou A., Sauerbrei W., Abrahamowicz M., Schmid M. on behalf of TG2 of the STRATOS initiative (2019): A review of spline function procedures in R, BMC Medical Research Methodology, 19:46
White I.R., Kaptoge S., Royston P., Sauerbrei W., The Emerging Risk Factors Collaboration (2018): Meta- analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods. Statistics in Medicine; 38(3): 326-338
Sauerbrei W., Taube S.E., McShane L.M., Cavenagh M.M., Altman D.G. (2018): Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): An Abridged Explanation and Elaboration. J Natl Cancer Inst. 110(8): 803-811
De Bin R., Sauerbrei W. (2018): Handling co-dependence issues in resampling-based variable selection procedures: a simulation study. Journal of Statistical Computation and Simulation, 88 (1): 28-55
De Bin R., Sauerbrei W., Boulesteix A.L. (2017): Detection of influential points as byproduct of resampling- based model-building procedures. Comput Stat Data Anal;116:19-31.
Sekula P., Mallett S., Altman D.G., Sauerbrei W. (2017): Did the reporting of prognostic studies of tumour markers improve since the introduction of REMARK guideline? A comparison of reporting in published articles. PLoS ONE;12(6):e0178531
Kasenda B., Sauerbrei W., Royston P., Mercat A., Slutsky A.S., Cook D., Guyatt G.H., Brochard L., Richard J.C.M., Stewart T.E., Meade M., Briel M. (2016): Multivariable fractional polynomial interaction to investigate continuous effect modifiers in a meta-analysis on higher versus lower PEEP for patients with ARDS. BMJ Open; 6:e011148
Winzer K.J., Buchholz A., Schumacher M., Sauerbrei W. (2016): The prognostic ability of the Nottingham Prognostic Index in breast cancer: better use of standard information and clinical relevance. PLoS ONE, 11(3):e0149977
De Bin R., Janitza S., Sauerbrei W., Boulesteix AL. (2016): Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. Biometrics.
Dunkler D., Sauerbrei W. and Heinze G. (2016): Global, Parameterwise and Joint Post-Estimation Shrinkage. Journal of Statistical Software, 69:8
Sauerbrei W., Abrahamowicz M., Altman D.G., le Cessie S. and Carpenter J. on behalf of the STRATOS initiative. (2014) STRengthening Analytical Thinking for Observational Studies: the STRATOS initiative. Statistics in Medicine, 33: 5413-5432
Kasenda B., Sauerbrei W., Royston P., Briel M. 2014, (2014): Investigation of continuous effect modifiers in a meta-analysis on higher versus lower PEEP in patients requiring mechanical ventilation - protocol of the ICEM study. Systematic Reviews, 3:46. http://www.systematicreviewsjournal.com/content/3/1/46
.Royston P., Sauerbrei W. (2014): Interaction of treatment with a continuous variable: simulation study of power for several methods of analysis. Statistics in Medicine, 33: 4695-4708
Royston P., Sauerbrei W. (2013): Interaction of treatment with a continuous variable: simulation study of significance level for several methods of analysis. Statistics in Medicine, 32(22):3788-3803.
Hingorani A.D., van der Windt D., Riley R.D., Abrams K., Moons K.G.M., Steyerberg E.W., Schroter S., Sauerbrei W., Altman D.G., Hemingway H. for the PROGRESS Group (2013): Prognosis research strategy (PROGRESS) 4: Stratified medicine research. British Medical Journal, 2013; 346.
Van Houwelingen H.C., Sauerbrei W. (2013): Cross-validation, shrinkage and variable selection in linear regression revisited. Open Journal of Statistics, 3: 79-102.
Binder, H., Sauerbrei, W., and Royston, P. (2013): Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response. Statistics in Medicine. 32:2262-2277.
Altman D.G.; McShane L.M.; Sauerbrei W.; Taube S.E. (2012): Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. In: PLoS Med 9(5): E 1001216 and in BMC Med 10 (1), 51
Abo-Zaid G., Sauerbrei W., Riley R.D. (2012): Individual participant data meta-analysis of prognostic factor studies: state of the art? In: BMC Med Res Methodol 12 (1), 56.
Schumacher M., Holländer N., Schwarzer G., Binder H., Sauerbrei W. (2012): Prognostic Factor Studies, in: Crowley, J., Hoering, A., Handbook of Statistics in Clinical Oncology, Third Edition: Chapman and Hall/CRC, 415-470.
Becher H., Lorenz E., Royston P., Sauerbrei W. (2012): Analysing covariates with spike at zero: a modified FP procedure and conceptual issues. Biometrical Journal 54 (5), 686–700.
Sauerbrei, W., Royston P. (2011): A new strategy for meta-analysis of continuous covariates in observational studies. Statistics in Medicine 30 (28), 3341–3360.
Sauerbrei W., Boulesteix A.-L., Binder H. (2011): Stability investigations of multivariable regression models derived for low and high dimensional data. Journal of Biopharmaceutical Statistics, 21:1206-1231.
Boulesteix A.-L., Guillemot V., Sauerbrei W. (2011): Use of pretransformation to cope with extreme values in important candidate features. Biometrical Journal, 53(4): 673–688.
Buchholz A., Sauerbrei W. (2011): Comparison of procedures to assess non-linear and time- varying effects in multivariable models for survival data. Biometrical Journal, 53(2): 308–331.
Boulesteix A.-L., Sauerbrei W. (2011): Added predictive value of high-throughput molecular data to clinical data, and its validation. Briefings in Bioinformatics, 12(3): 215-229.
Mallett S., Timmer A., Sauerbrei W., Altman D.G. (2010): Reporting of prognostic studies of tumor markers: a review of published articles in relation to REMARK guidelines. British Journal of Cancer, 102:173-180.
Binder H., Sauerbrei W. (2010): Adding local components to global functions for continuous covariates in multivariable regression modeling. Statistics in Medicine, 29: 800-817.
Riley, R. D.,Sauerbrei, W., Altman, D.G. (2009): Prognostic markers in cancer: the evolution of evidence from single studies to meta-analysis, and beyond. British Journal of Cancer, 100:1219-1229.
P. Royston, W. Sauerbrei. 'Multivariable Model - Building: A Pragmatic Approach to Regression Anaylsis based on Fractional Polynomials for Modelling Continuous Variables'. Wiley 2008.
Sauerbrei, W., Royston, P., Binder H (2007): Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Statistics in Medicine, 26: 5512-5528.
Sauerbrei, W., Royston, P. (2007): Modelling to extract more information from clinical trials data - on some roles for the bootstrap. Statistics in Medicine, 26:4989-5001.
Sauerbrei, W., Royston, P., Look, M. (2007): A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biometrical Journal, 49: 453-473.
Royston, P., Altman, D.G., Sauerbrei, W. (2006): Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in Medicine, 25: 127-141.
Royston, P., Sauerbrei, W. (2004): A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Statistics in Medicine, 23:2509-2525.
Royston, P., Sauerbrei, W. (2004): ‘A new measure of prognostic separation in survival data.’ Statistics in Medicine, 23: 723-748
Sauerbrei, W., Royston, P. (1999): ‘Building multivariable prognostic and diagnostic models: Transformation of the predictors by using fractional polynomials’. Journal of the Royal Statistical Society, A. 162:71-94; Corrigendum (2002), 165: 339-400
Sauerbrei, W. (1999): ‘The use of resampling methods to simplify regression models in medical statistics’, Applied Statistics, 48:313-329.70.
Graf, E., Schmoor, C., Sauerbrei, W., Schumacher, M. (1999): ‘Assessment and comparison of prognostic classification schemes for survival data’, Statistics in Medicine, 18:2529-2545.
Blettner, M., Sauerbrei, W., Schlehofer, B., Scheuchenpflug, T., Friedenreich, C. (1999): ‘Traditional reviews, meta-analyses and pooled analyses in epidemiology’, International Journal of Epidemiology, 28:1-9.
Sauerbrei, W., Schumacher, M. (1992): ‘A Bootstrap Resampling Procedure for Model Building: Application to the Cox Regression Model’, Statistics in Med
