What does it mean to control for confounding, and when do we actually need to do it? To answer this, we need a well-defined research question, driven by the goal of the study. For descriptive goals, we explain that confounding adjustment is often not just unnecessary but can be harmful.
Subscribe to Journal
Get full journal access for 1 year
only $20.79 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Pedersen, A. B., Horváth-Puhó, E., Ehrenstein, V., Rørth, M. & Sørensen, H. T. Frozen shoulder and risk of cancer: a population-based cohort study. Br. J. Cancer 117, 144–147 (2017).
function2fitnes. Massive population-based study. First nationwide cohort study to examine cancer risk in frozen shoulder patients from British Journal of Cancer. What is the risk of a cancer diagnosis after an incident diagnosis of frozen shoulder? Open Access’ https://t.co/xEh2yy2irN; https://t.co/YZ3soly2GE. Available from https://twitter.com/function2fitnes/status/1214321809178464256 (2020).
giovanni_ef. There is no way in the world this study can even start answering this question. There is absolutely no information on how confounding was handled—which wasn’t even mentioned as a study limitation. This is pure epidemiological rubbish. Available from https://twitter.com/giovanni_ef/status/1214332942417158144 (2020).
LiniearProbe. The risk of this study is clinicians & patients worrying that FS might be a forerunner of cancer when: A) this cannot be supported by this study B) may lead to unnecessary further Ix & referrals Treat FS as FS and use the same clinical skills to be alert to red flags as always. Available from https://twitter.com/LinearProbe/status/1214527944439283712 (2020).
Suzuki, E., Shinozaki, T. & Yamamoto, E. Causal diagrams: pitfalls and tips. J. Epidemiol. 30, 153–162 (2020).
Lesko, C. R., Keil, A. P. & Edwards, J. K. The epidemiologic toolbox: identifying, honing, and using the right tools for the job. Am. J. Epidemiol. https://doi.org/10.1093/aje/kwaa030 (2020).
Hernán, M. A., Hsu, J. & Healy, B. A second chance to get causal inference right: a classification of data science tasks. Chance 32, 42–49 (2019).
Hernán, M. A. & Robins, J. M. Causal Inference: What If (Chapman & Hall/CRC, Boca Raton, 2020).
Wasserstein, R. L. & Lazar, N. A. The ASA’s statement on p-values: context, process, and purpose. Am. Stat. 70, 129–133 (2016).
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N. et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol. 31, 337–350 (2016).
This commentary arose from discussion on #epitwitter. The authors would like to thank everyone who contributed to that conversation.
Ethics approval and consent to participate
Consent to publish
The authors declare no competing interests.
No funding source was used for the creation of this commentary.
Note This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Conroy, S., Murray, E.J. Let the question determine the methods: descriptive epidemiology done right. Br J Cancer (2020). https://doi.org/10.1038/s41416-020-1019-z