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Clinical and Translational Oncology

, Volume 20, Issue 8, pp 954–965 | Cite as

Top ten errors of statistical analysis in observational studies for cancer research

  • A. Carmona-Bayonas
  • P. Jimenez-Fonseca
  • A. Fernández-Somoano
  • F. Álvarez-Manceñido
  • E. Castañón
  • A. Custodio
  • F. A. de la Peña
  • R. M. Payo
  • L. P. Valiente
Review Article

Abstract

Observational studies using registry data make it possible to compile quality information and can surpass clinical trials in some contexts. However, data heterogeneity, analytical complexity, and the diversity of aspects to be taken into account when interpreting results makes it easy for mistakes to be made and calls for mastery of statistical methodology. Some questionable research practices that include poor analytical data management are responsible for the low reproducibility of some results; yet, there is a paucity of information in the literature regarding specific statistical pitfalls of cancer studies. In addition to proposing how to avoid or solve them, this article seeks to expose ten common problematic situations in the analysis of cancer registries: convenience, dichotomization, stratification, regression to the mean, impact of sample size, competing risks, immortal time and survivor bias, management of missing values, and data dredging.

Keywords

Cancer research Error Observational studies Pitfalls Registry Statistical analysis 

Notes

Acknowledgements

Priscilla Chase Duran is acknowledged for editing the manuscript.

Compliance with ethical standards

Conflict of interest

None to declare. This is an academic study. No financial support has been received from external sources.

Ethical statement

The study has been performed in accordance with the ethical standards of the Declaration of Helsinki and its later amendments.

Informed consent

Not required.

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Copyright information

© Federación de Sociedades Españolas de Oncología (FESEO) 2017

Authors and Affiliations

  1. 1.Department of Hematology and Medical OncologyHospital Universitario Morales Meseguer, UMU, IMIBMurciaSpain
  2. 2.Department of Medical OncologyHospital Universitario Central de AsturiasOviedoSpain
  3. 3.IUOPA- Area of Preventive Medicine and Public Health; Department of MedicineUniversity of OviedoOviedoSpain
  4. 4.CIBER of Epidemiology and Public Health, CIBERESP, Instituto de Salud Carlos IIIMadridSpain
  5. 5.Department of Hospital PharmacyHospital Universitario Central de AsturiasOviedoSpain
  6. 6.Department of Medical OncologyClínica Universidad de NavarraPamplonaSpain
  7. 7.Department of Medical OncologyHospital Universitario La Paz, CIBERONC CB16/12/00398MadridSpain
  8. 8.Faculty of Medicine and Health SciencesUniversity of OviedoOviedoSpain
  9. 9.Department of Statistical Analysis and Big DataCatholic University of Murcia (UCAM)MurciaSpain

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