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Review
. 2023 Apr;38(4):634-637.
doi: 10.1016/j.arth.2022.11.019. Epub 2022 Dec 5.

How to Distinguish Correlation From Causation in Orthopaedic Research

Isabella Zaniletti  1 , Dirk R Larson  2 , David G Lewallen  3 , Daniel J Berry  3 , Hilal Maradit Kremers  4
Affiliations
Review

How to Distinguish Correlation From Causation in Orthopaedic Research

Isabella Zaniletti et al. J Arthroplasty. 2023 Apr.

Abstract

Correlations in observational studies are commonly misinterpreted as causation. Although correlation is necessary to establish a causal relationship between two variables, correlations may also arise due to chance, reverse causality, or confounding. There are several methods available to orthopaedic researchers to determine whether the observed correlations are causal. These methods depend on the key components of the study including, but not limited to, study design and data availability on confounders. In this article, we illustrate the main concepts surrounding correlation and causation using intuitive real-world examples from the orthopaedic literature. Please visit the following https://www.youtube.com/watch?v=WW7pFudZbHA&t=52s for a video that explains the highlights of the paper in practical terms.

Keywords: arthroplasty; causation; chance; confounding; correlation; orthopaedics.

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Conflict of interest statement

Conflict of Interest: One or more of the authors of this paper have disclosed potential or pertinent conflicts of interest, which may include receipt of payment, either direct or indirect, institutional support, or association with an entity in the biomedical field which may be perceived to have potential conflict of interest with this work.

Figures

Fig. 1.
Fig. 1.
Role of chance, reverse causality and confounding.
Fig. 2.
Fig. 2.
Four different statistical approaches to mitigate unmeasured confounders. a. Propensity score calibration method; b. Instrumental variable method; c. Target trial method; d. Negative control method.
Fig. 2.
Fig. 2.
Four different statistical approaches to mitigate unmeasured confounders. a. Propensity score calibration method; b. Instrumental variable method; c. Target trial method; d. Negative control method.
Fig. 2.
Fig. 2.
Four different statistical approaches to mitigate unmeasured confounders. a. Propensity score calibration method; b. Instrumental variable method; c. Target trial method; d. Negative control method.
Fig. 2.
Fig. 2.
Four different statistical approaches to mitigate unmeasured confounders. a. Propensity score calibration method; b. Instrumental variable method; c. Target trial method; d. Negative control method.
See this image and copyright information in PMC

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