| Title |
Top ten errors of statistical analysis in observational studies for cancer research
|
|---|---|
| Published in |
Clinical and Translational Oncology, December 2017
|
| DOI | 10.1007/s12094-017-1817-9 |
| Pubmed ID | |
| Authors |
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 |
| 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. |
X Demographics
The data shown below were collected from the profiles of 25 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 3 | 12% |
| United Kingdom | 2 | 8% |
| Luxembourg | 1 | 4% |
| United States | 1 | 4% |
| Colombia | 1 | 4% |
| Switzerland | 1 | 4% |
| India | 1 | 4% |
| Chile | 1 | 4% |
| New Zealand | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 11 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 56% |
| Scientists | 7 | 28% |
| Practitioners (doctors, other healthcare professionals) | 4 | 16% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 17% |
| Student > Ph. D. Student | 6 | 12% |
| Other | 5 | 10% |
| Lecturer | 4 | 8% |
| Student > Postgraduate | 4 | 8% |
| Other | 11 | 21% |
| Unknown | 13 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 27% |
| Psychology | 4 | 8% |
| Computer Science | 3 | 6% |
| Agricultural and Biological Sciences | 3 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 10 | 19% |
| Unknown | 16 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 December 2018.
All research outputs
#2,388,397
of 24,953,268 outputs
Outputs from Clinical and Translational Oncology
#75
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Outputs of similar age
#52,020
of 451,973 outputs
Outputs of similar age from Clinical and Translational Oncology
#2
of 37 outputs
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