Sie sind hier: Startseite Weitere Lehrveranstaltungen Wintersemester Building of multivariable …

Building of multivariable regression models to extract information from empirical data

Aimed at students and researchers from various areas of science


Prof. Dr. Willi Sauerbrei


21 January – 25 January 2019
29 January – 30 January 2019


Daily 14.15 – 17.30
HS Med. Biometrie und Med. Informatik, Stefan-Meier-Straße 26

Language: English or German, depends on participants Slides and other materials in English

Registration required, deadline 15 January 2018.
Please send an email to giving: Name, surname, Department and Institution, student (y/n)

Specific issues discussed in the second week will depend on interest of participants


Weitere Infos im Vorlesungsverzeichnis


For many years the quality of research in the health sciences has been criticized and it is obvious that ‘waste in research’ has to be reduced (Ioannidis et al 2014). Problems in design, analysis and reporting of studies are among the most important reasons for this very disappointing situation. Deficiencies in statistical methods and their applications have been raised and consistently expressed over many years (Altman et al 1994, Sauerbrei 2005). Statistical methodology has seen substantial developments, but many of them are ignored in practice and insufficient statistical knowledge in the research community is often emphasized. It is obvious that fishing for significant p-values produces many false positive results (Kyzas et al 2005). The untapped potential of observational research to inform clinical decision making is well known (Visvanathan et al 2017). Obviously, it is necessary to ensure the use of rigorous methodologies with suitable methods for the design and analysis of a study and transparent reporting of results as key issues.

During the last two decades several initiatives have been started that aim at improving the research process. Obviously, transparent and complete reporting is a pre-requisite to judge the usefulness of data and to interpret study results in the appropriate context. For many different types of studies reporting guidelines have been developed and the EQUATOR network acts as an “umbrella” for developers of such guidelines (Simera et al 2010, Moher et al 2014, Altman et al 2012, Moons et al 2015).

More difficult is the development of guidance for the statistical analysis of observational studies. The STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative has recently been founded (Sauerbrei et al 2014). Currently there are nine topic groups (TG), each working on specific tasks such as study design, missing data, measurement error and misclassification, causal inference or high-dimensional data. Reviews of published papers illustrate that many analyses have not been conducted by experienced statisticians but rather by analysts with low statistical knowledge. It is important that these have access to documents which explain the strengths and weaknesses of different approaches.

In the context of prognostic marker research I will illustrate common weaknesses of the design, analysis and reporting studies, with an emphasis on continuous variables. Problems caused by categorization will be shown and I will briefly introduce fractional polynomials as a relatively simple and advantageous approach (Sauerbrei and Royston 2010). The main aims of the PROGnosis RESearch Strategy (PROGRESS) partnership will be outlined (Hemingway et al 2013, Riley et al 2013). Many other projects with influence on methodological quality of single studies and meta-analyses have been started. I will briefly discuss the main ideas behind study registration, All Trials campaign, data sharing and reproducible research. For further information, references and links to websites will be given.

References and links


  • Altman D.G., Lausen B., Sauerbrei W., Schumacher M. (1994): Dangers of using “Optimal” cutpoints in the evaluation of prognostic factors. Journal of the National Cancer Institute 86: 829-835.
  • Ioannidis J.P. (2005): Why most published research findings are false. PLoS Med 2(8): E 124.
  • Ioannidis J.P., Greenland S., Hlatky M.A., Khoury M.J., Macleod M.R., Moher D., Schulz K.F., Tibshirani R. (2014): Increasing value and reducing waste in research design, conduct, and analysis, Lancet 383(9912): 166-175.
  • Kyzas P.A., Denaxa-Kyza D., Ioannidis J.P. (2007): Almost all articles on cancer prognostic markers report statistically significant results. Eur J Cancer 43: 2559 – 2579.
  • Sauerbrei W. (2005): Prognostic Factors – Confusion caused by bad quality of design, analysis and reporting of many studies. Bier H. (ed). Current Research in Head and Neck Cancer. Advances in Oto-Rhino-Laryngology. Basel, Karger, 62:184-200.
  • Sauerbrei W., Royston P. (2010): Continuous Variables: To Categorize or to Model? In: Reading, C. (Ed.): The 8th International Conference on Teaching Statistics- Data and Context in statistics education: Towards an evidence based society. International Statistical Institute, Voorburg.
  • Visvanathan K., Levit L.A., Raghavan D., Hudis C.A., Wong S., Dueck A., Lyman G.H. (2017): Untapped Potential of Observational Research to Inform Clinical Decision Making: American society of Clinical Oncology Statement. J Clin Oncol 35:1845-1854.
  • The Lancet. Series: “Research: increasing value, reducing waste” (2014):
  • The Lancet Series: “Right Care” (2017):
  • Cochrane Methods Groups:
  • AllTrials campaign:
  • Clinical trial data by pharmaceutical industry:
  • Multivariable Fractional Polynomials:



  • EQUATOR (Enhancing the QUAlity and Transparency Of health Research)
  • Simera I., Moher D., Hirst A., Hoey J., Schulz K.F., Altman D.G. (2010): Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network. BMC Med (8): 24.
  • Moher D., Altman D.G., K., Simera I, Wager E. (2014): Guidelines for Reporting Health Research: A User's Manual. Wiley.
  • Altman D.G., McShane L., Sauerbrei W., Taube S.E. (2012): Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. PLoS Med 9(5): E 1001216.
  • Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. 2015;162:W1-W73. TRIPOD website:


PROGnosis RESearch Strategy (PROGRESS) ->

  • PROGRESS Partnership is a UK Medical Research Council (MRC) funded, international, interdisciplinary collaboration developing understanding in research into quality of care outcomes, prognostic factors, risk prediction models, and predictors of differential treatment response.
  • Hemingway H., Croft P., Perel P., Hayden J.A., Abrams K., Timmis A. et al. Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes BMJ 2013; 346 :e5595
  • Riley R.D., Hayden J.A., Steyerberg E.W., Moons K.G.M., Abrams K., et al. (2013) Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine 10(2): e1001380.
  • Recommendations table from PROGRESS papers 1-4 (.docx):


STRengthening Analytical Thinking for Observational Studies (STRATOS) ->

  • Sauerbrei W., Abrahamowicz M., Altman D.G., le Cessie S., Carpenter J. on behalf of the STRATOS initiative. (2014): STRengthening Analytical Thinking for Observational Studies: the STRATOS initiative. Statistics in Medicine, 33: 5413-5432.