Interpreting results in 2 x 2 tables: part 9 of a series on evaluation of scientific publications.

Abstract

BACKGROUND: The findings of epidemiological studies, diagnostic tests, and comparative therapeutic trials are often presented in 2 x 2 tables. These must be interpreted correctly for a proper understanding of the findings. METHODS: The authors present basic statistical concepts required for the analysis of nominal data, referring to standard works in statistics. RESULTS: The relative risk and odds ratio are defined to be indices for the relationship between two binary quantities (e.g., exposure--yes/no and disease--yes/no). The topics dealt with in this article include the effect of sample size on the length of the confidence interval and the p-value, and also inaccuracies caused by measuring error. Exposures are often expressed on a three-level scale (none, low, high). The authors also consider the 2 x 3 table as an extension of the 2 x 2 table and discuss the categorization of continuous measurements. Typically, more than one factor is involved in the development of a disease. The effect that a further factor can have on the observed relationship between the exposure and the disease is discussed. CONCLUSIONS: Sample size, measurement error, categorization, and confounders influence the statistical interpretation of 2 x 2 tables in many ways. Readers of scientific publications should know the inherent problems in the interpretation of simple 2 x 2 tables and check that the authors have taken these into account adequately in analyzing and interpreting their data.